Keyboard shortcuts

Press ← or → to navigate between chapters

Press S or / to search in the book

Press ? to show this help

Press Esc to hide this help

Список литературы

Библиографические записи приведены на языке оригинала.

  • Cerebras Systems (2021). Wafer-Scale Deep Learning Acceleration with the Cerebras CS-2. Cerebras Technical Paper.

  • Choi et al. (2018). PACT: Parameterized Clipping Activation for Quantized Neural Networks. arXiv preprint.

  • Google (2025). XLA: Optimizing Compiler for Machine Learning.

  • Graphcore (2020). The Colossus MK2 IPU Processor.

  • Hubara et al. (2018). Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. Journal of Machine Learning Research.

  • Jia et al. (2019). Optimizing DNN Computation with Relaxed Graph Substitutions. Proceedings of Machine Learning and Systems (MLSys).

  • NVIDIA Corporation (2020). NVIDIA A100 Tensor Core GPU Architecture. NVIDIA Whitepaper, V1.0.

  • NVIDIA Corporation (2020). NVLink: Scalable High-Performance Interconnect. NVIDIA Technical Report.

  • Raffel et al. (2020). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research.

  • Rastegari et al. (2016). XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. European Conference on Computer Vision (ECCV).

  • Tesla, Inc. (2021). Tesla Dojo Technology: A Guide to Tesla’s Configurable Floating Point Formats & Arithmetic. Tesla AI Day, Whitepaper.

  • Zhu et al. (2017). Trained Ternary Quantization. International Conference on Learning Representations (ICLR).

  • Abadi et al. (2016). TensorFlow: A System for Large-Scale Machine Learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI).

  • Agarwal et al. (2005). Geometric Approximation via Coresets. Combinatorial and Computational Geometry.

  • Agrawal et al. (2024). Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve. 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24).

  • Ignatov and Timofte (2024). AI Benchmark.

  • Ainslie et al. (2023). GQA: Training Generalized Multi-Query Transformer Models From Multi-Head Checkpoints. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

  • Akidau et al. (2015). The dataflow model. Proceedings of the VLDB Endowment.

  • Team et al. (2016). Theano: A Python framework for fast computation of mathematical expressions. arXiv preprint.

  • Altini and Kinnunen (2021). The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring. Sensors.

  • Amazon Web Services (2024). Amazon Simple Storage Service (S3).

  • AMD (2023). AMD Instinct MI300X Accelerators. AMD product documentation.

  • Amdahl (1967). Validity of the single processor approach to achieving large scale computing capabilities. Proceedings of the April 18-20, 1967, spring joint computer conference on - AFIPS ’67 (Spring).

  • Amershi et al. (2019). Software Engineering for Machine Learning: A Case Study. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP).

  • Amodei and Hernandez (2018). AI and Compute. OpenAI Blog.

  • Amodei and Hernandez (2018). AI and Compute. OpenAI Blog.

  • Angwin et al. (2016). Machine Bias. ProPublica investigation.

  • Ansel et al. (2024). PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation. Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2.

  • Anthropic (2023). Introducing Claude 2.1.

  • Anthropic (2024). Introducing the Next Generation of Claude.

  • AnyLogic (2024). Synthetic Data for Artificial Intelligence.

  • Apache Software Foundation (2024). Apache Airflow.

  • Apple (2024). Facial matching security. Apple Platform Security.

  • Apple (2024). The Secure Enclave. Apple Platform Security.

  • Ardila et al. (2020). Common Voice: A Massively-Multilingual Speech Corpus. Proceedings of the Twelfth Language Resources and Evaluation Conference.

  • Armbrust et al. (2020). Delta Lake. Proceedings of the VLDB Endowment.

  • Amazon Web Services (2024). Amazon Web Services (AWS).

  • Amazon Web Services (2026). Amazon EC2 Spot Instances.

  • Amazon Web Services (2024). AWS Auto Scaling.

  • Amazon Web Services (2024). AWS CloudFormation.

  • Microsoft (2024). Microsoft Azure.

  • Ba et al. (2016). Layer Normalization. arXiv preprint arXiv:1607.06450.

  • Bahdanau et al. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. International Conference on Learning Representations (ICLR).

  • Bai et al. (2006). LAPACK. Handbook of Linear Algebra.

  • ONNX Contributors (2019). ONNX: Open Neural Network Exchange.

  • Bakheet and Al-Hamadi (2021). A framework for instantaneous driver drowsiness detection based on improved HOG features and naïve Bayesian classification. Brain Sciences.

  • Banbury et al. (2020). Benchmarking TinyML Systems: Challenges and Direction. arXiv preprint arXiv:2003.04821.

  • Banbury et al. (2021). MicroNets: Neural network architectures for deploying TinyML applications on commodity microcontrollers. Proceedings of Machine Learning and Systems.

  • Banbury et al. (2021). MLPerf Tiny Benchmark. Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.

  • Barocas and Selbst (2016). Big Data’s Disparate Impact. California Law Review.

  • Barroso et al. (2017). Attack of the killer microseconds. Communications of the ACM.

  • Barroso et al. (2019). The Datacenter as a Computer. Springer International Publishing.

  • Baur and Strassen (1983). The Complexity of Partial Derivatives. Theoretical Computer Science.

  • Baydin et al. (2018). Automatic Differentiation in Machine Learning: A Survey. Journal of Machine Learning Research.

  • Baylor et al. (2017). TFX. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

  • Gadre et al. (2023). DataComp: In search of the next generation of multimodal datasets. Advances in Neural Information Processing Systems (NeurIPS).

  • Bedford Taylor (2017). The Evolution of Bitcoin Hardware. Computer.

  • Beede et al. (2020). A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI).

  • Belkin et al. (2019). Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences.

  • Bellamy et al. (2019). AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM Journal of Research and Development.

  • Bender and Friedman (2018). Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science. Transactions of the Association for Computational Linguistics.

  • Bengio et al. (2009). Curriculum Learning. Proceedings of the 26th Annual International Conference on Machine Learning.

  • Bengio et al. (2013). Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence.

  • Bengio et al. (2013). Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. arXiv preprint arXiv:1308.3432.

  • Bengio et al. (2015). Conditional Computation in Neural Networks for faster models. arXiv preprint arXiv:1511.06297.

  • Berger and Zhou (2014). Kolmogorov–Smirnov Test: Overview. Wiley StatsRef: Statistics Reference Online.

  • Bergstra et al. (2010). Theano: A CPU and GPU Math Compiler in Python. Proceedings of the Python in Science Conference.

  • Beyer et al. (2016). Site Reliability Engineering: How Google Runs Production Systems. O’Reilly Media.

  • Beyer et al. (2020). Are we done with ImageNet?. arXiv preprint arXiv:2006.07159.

  • Bird et al. (2020). Fairlearn: A toolkit for assessing and improving fairness in AI. Microsoft Technical Report MSR-TR-2020-32.

  • Bishop (2006). Pattern Recognition and Machine Learning. Springer.

  • Blalock et al. (2020). What is the State of Neural Network Pruning?. Proceedings of Machine Learning and Systems (MLSys).

  • Kuzmin et al. (2022). FP8 Quantization: The Power of the Exponent. Advances in Neural Information Processing Systems (NeurIPS).

  • Bobrow (1964). Natural Language Input for a Computer Problem Solving System.

  • Boehm (1981). Software Engineering Economics. Prentice-Hall.

  • Bolukbasi et al. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. Advances in Neural Information Processing Systems (NeurIPS).

  • Bommasani et al. (2021). On the Opportunities and Risks of Foundation Models. arXiv preprint arXiv:2108.07258.

  • Boroumand et al. (2018). Google Workloads for Consumer Devices. Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems.

  • Bourtoule et al. (2021). Machine Unlearning. 2021 IEEE Symposium on Security and Privacy (SP).

  • Bradbury et al. (2018). JAX: composable transformations of Python+NumPy programs.

  • Breck et al. (2017). The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data).

  • Breck et al. (2019). Data Validation for Machine Learning. Proceedings of the 2nd SysML Conference.

  • Brewer (2000). Towards robust distributed systems (abstract). Proceedings of the nineteenth annual ACM symposium on Principles of distributed computing.

  • Broder (1997). On the resemblance and containment of documents. Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No.97TB100171).

  • Smith (1997). Digital Signal Processing. Digital Signal Processing Demystified.

  • Brown et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual.

  • Brutlag (2009). Speed Matters for Google Web Search. Google Research Blog.

  • Buolamwini and Gebru (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Conference on fairness, accountability and transparency.

  • Burns et al. (2016). Borg, Omega, and Kubernetes. Communications of the ACM.

  • Cai et al. (2020). TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning. Advances in Neural Information Processing Systems (NeurIPS).

  • Campbell et al. (2002). Deep Blue. Artificial Intelligence.

  • Cao and Yang (2015). Towards Making Systems Forget With Machine Unlearning. 2015 IEEE Symposium on Security and Privacy.

  • Chapman et al. (2000). CRISP-DM 1.0: Step-by-step data mining guide. SPSS Inc.

  • Chen et al. (2014). DianNao: A Small-Footprint High-Throughput Accelerator for Ubiquitous Machine-Learning. Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’14).

  • Chen et al. (2016). Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks. 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).

  • Chen et al. (2016). Training Deep Nets with Sublinear Memory Cost. arXiv preprint arXiv:1604.06174.

  • Chen et al. (2017). Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators. IEEE Micro.

  • Chen et al. (2018). TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’18).

  • Chen et al. (2020). Developments in MLflow. Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning.

  • Chen et al. (2020). A Simple Framework for Contrastive Learning of Visual Representations. International Conference on Machine Learning (ICML).

  • Chen et al. (2021). Evaluating Large Language Models Trained on Code. arXiv preprint arXiv:2107.03374.

  • Chen et al. (2023). A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring. Nature Biomedical Engineering.

  • Chen et al. (2024). EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism. Proceedings of the 41st International Conference on Machine Learning (ICML).

  • Chetlur et al. (2014). cuDNN: Efficient Primitives for Deep Learning. arXiv preprint arXiv:1410.0759.

  • Cho et al. (2014). On the properties of neural machine translation: Encoder-decoder approaches. Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation.

  • Cho et al. (2025). TRANSFORMER EXPLAINER: Interactive Learning of Text-Generative Models. Proceedings of the AAAI Conference on Artificial Intelligence.

  • Choi et al. (2019). Faster Neural Network Training with Data Echoing. arXiv preprint.

  • Chollet (2018). Deep Learning with Python. Manning Publications.

  • Choquette et al. (2021). NVIDIA A100 Tensor Core GPU: Performance and Innovation. IEEE Micro.

  • Choquette (2023). NVIDIA Hopper H100 GPU: Scaling Performance. IEEE Micro.

  • Choudhary et al. (2020). A comprehensive survey on model compression and acceleration. Artificial Intelligence Review.

  • Choukroun et al. (2019). Low-Bit Quantization of Neural Networks for Efficient Inference. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

  • Chouldechova (2017). Fair Prediction With Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data.

  • Chowdhery et al. (2022). PaLM: Scaling Language Modeling with Pathways. arXiv preprint arXiv:2204.02311.

  • Chu et al. (2021). Discovering Multi-Hardware Mobile Models via Architecture Search. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

  • Krizhevsky (2009). Learning Multiple Layers of Features from Tiny Images.

  • CircleCI (2024). CircleCI: Continuous Integration and Delivery Platform.

  • Clark et al. (2019). What Does BERT Look at? An Analysis of BERT’s Attention. Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP.

  • Cohen (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement.

  • Cohen and Welling (2016). Group Equivariant Convolutional Networks. Proceedings of the 33rd International Conference on Machine Learning (ICML).

  • Coleman et al. (2019). Analysis of DAWNBench, a Time-To-Accuracy Machine Learning Performance Benchmark. NIPS ML Systems Workshop.

  • Coleman et al. (2022). Similarity Search for Efficient Active Learning and Search of Rare Concepts. Proceedings of the AAAI Conference on Artificial Intelligence.

  • Courbariaux et al. (2015). BinaryConnect: Training Deep Neural Networks with Binary Weights during Propagations. Advances in Neural Information Processing Systems (NeurIPS).

  • Cover and Thomas (2006). Elements of Information Theory. Wiley.

  • Covington et al. (2016). Deep Neural Networks for YouTube Recommendations. Proceedings of the 10th ACM Conference on Recommender Systems.

  • Crankshaw et al. (2017). Clipper: A Low-Latency Online Prediction Serving System. 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17).

  • CrowdFlower (2016). Data Science Report 2016.

  • Cubuk et al. (2019). AutoAugment: Learning Augmentation Strategies From Data. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

  • Cubuk et al. (2020). Randaugment: Practical automated data augmentation with a reduced search space. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

  • Cunningham (1992). The WyCash Portfolio Management System. ACM SIGPLAN OOPS Messenger.

  • Curnow and Wichmann (1976). A synthetic benchmark. The Computer Journal.

  • Cybenko (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems.

  • Dalal and Triggs (2005). Histograms of Oriented Gradients for Human Detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05).

  • Dally et al. (2021). Evolution of the Graphics Processing Unit (GPU). IEEE Micro.

  • Dally (2023). Hardware for Deep Learning. 2023 IEEE Hot Chips 35 Symposium (HCS).

  • Dao et al. (2022). FlashAttention: Fast and Memory-Efficient Exact Attention With IO-Awareness. Advances in Neural Information Processing Systems (NeurIPS).

  • Dao et al. (2022). Monarch: Expressive Structured Matrices for Efficient and Accurate Training. arXiv preprint.

  • Dao (2023). FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning. arXiv preprint arXiv:2307.08691.

  • Dastin (2018). Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women. Reuters.

  • David et al. (2021). TensorFlow Lite Micro: Embedded machine learning for TinyML systems. Proceedings of Machine Learning and Systems.

  • dbt Labs (2024). dbt (data build tool).

  • Dean and Ghemawat (2004). MapReduce: Simplified Data Processing on Large Clusters. Proceedings of the 6th Symposium on Operating Systems Design and Implementation (OSDI).

  • Dean et al. (2012). Large Scale Distributed Deep Networks. Advances in Neural Information Processing Systems (NeurIPS).

  • Dean (2012). Achieving Rapid Response Times in Large Online Services. Berkeley AMPLab Cloud Seminar.

  • Dean and Barroso (2013). The tail at scale. Communications of the ACM.

  • Dean et al. (2018). A New Golden Age in Computer Architecture: Empowering the Machine-Learning Revolution. IEEE Micro.

  • Baidu Research (2016). DeepBench: Benchmarking Deep Learning Operations on Different Hardware.

  • DeepSeek (2024). Introducing DeepSeek-V3.

  • Dehghani (2022). Data Mesh: Delivering Data-Driven Value at Scale. O’Reilly Media.

  • Deng et al. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition.

  • Dennard et al. (1974). Design of ion-implanted MOSFET’s with very small physical dimensions. IEEE J. Solid-State Circuits.

  • Denning (1968). The Working Set Model for Program Behavior. Communications of the ACM.

  • Dettmers et al. (2022). 8-Bit Optimizers via Block-Wise Quantization. International Conference on Learning Representations (ICLR).

  • Devlin et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

  • DeVries and Taylor (2017). Improved Regularization of Convolutional Neural Networks with Cutout. arXiv preprint arXiv:1708.04552.

  • Howarth (2020). Why Discord is switching from Go to Rust.

  • Dixit (1993). Overview of the SPEC Benchmarks. The Benchmark Handbook for Database and Transaction Systems.

  • Merkel (2014). Docker: Lightweight Linux Containers for Consistent Development and Deployment. Linux Journal.

  • Dongarra et al. (1979). LINPACK Users’ Guide. SIAM.

  • Dongarra et al. (1988). An extended set of FORTRAN basic linear algebra subprograms. ACM Transactions on Mathematical Software.

  • Dosovitskiy et al. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. International Conference on Learning Representations (ICLR).

  • Data Protection Commission (2023). Data Protection Commission Announces Conclusion of Two Inquiries into Meta Ireland. Regulatory press release.

  • Duarte et al. (2022). FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning. arXiv preprint arXiv:2207.07958.

  • Dubey et al. (2024). The Llama 3 Herd of Models. arXiv preprint arXiv:2407.21783.

  • Iterative (2024). Data Version Control (DVC).

  • Dwork et al. (2006). Calibrating Noise to Sensitivity in Private Data Analysis. Theory of Cryptography Conference (TCC).

  • Dwork (2008). Differential Privacy: A Survey of Results. Theory and Applications of Models of Computation.

  • European Data Protection Board (2018). Guidelines on Automated Individual Decision-Making and Profiling for the Purposes of Regulation 2016/679.

  • Elastic NV (2024). Elasticsearch: Distributed Search and Analytics Engine.

  • Elman (1990). Finding Structure in Time. Cognitive Science.

  • Elsen et al. (2020). Fast Sparse ConvNets. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

  • Elsken et al. (2019). Neural Architecture Search. The Springer Series on Challenges in Machine Learning.

  • Epoch AI (2024). Machine Learning Trends. Epoch AI Research Database.

  • European Space Agency (1996). Ariane 501: Presentation of Inquiry Board Report. European Space Agency press release.

  • Esmaeilzadeh et al. (2011). Dark silicon and the end of multicore scaling. Proceedings of the 38th annual international symposium on Computer architecture.

  • Ethernet Alliance (2025). 2025 Ethernet Roadmap. Ethernet Alliance roadmap.

  • European Parliament and Council of the European Union (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (AI Act). Official Journal of the European Union, L 2024/1689.

  • Everingham et al. (2009). The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision.

  • Engineering (2016). Introducing FBLearner Flow: Facebook’s AI backbone. Engineering at Meta Blog.

  • Mosseri (2018). Bringing People Closer Together. Meta Newsroom.

  • U.S. Food and Drug Administration (2021). Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan.

  • U.S. Food and Drug Administration (2025). Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions.

  • Gojek and Google (2019). Feast: An open source feature store for machine learning. Google Cloud Blog.

  • Fedus et al. (2022). Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity. Journal of Machine Learning Research.

  • Feigenbaum (1984). Knowledge Engineering: The Applied Side of Artificial Intelligence. Annals of the New York Academy of Sciences.

  • Feng and Cameron (2007). The Green500 List: Encouraging Sustainable Supercomputing. Computer.

  • Fitzpatrick (1988). The Validity and Practicality of Sun-Reactive Skin Types I Through VI. Archives of Dermatology.

  • FlatBuffers Authors (2026). FlatBuffers Documentation.

  • Fleiss (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin.

  • Flynn (1966). Very high-speed computing systems. Proceedings of the IEEE.

  • Frankle and Carbin (2019). The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. International Conference on Learning Representations (ICLR).

  • Fredrikson et al. (2015). Model Inversion Attacks That Exploit Confidence Information and Basic Countermeasures. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security.

  • Frostig et al. (2018). Compiling Machine Learning Programs via High-Level Tracing. Systems for Machine Learning.

  • Gabor (1946). Theory of Communication. Part 1: The Analysis of Information. Journal of the Institution of Electrical Engineers - Part III: Radio and Communication Engineering.

  • Gale et al. (2019). The State of Sparsity in Deep Neural Networks. arXiv preprint arXiv:1902.09574.

  • Gale et al. (2020). Sparse GPU Kernels for Deep Learning. SC20: International Conference for High Performance Computing, Networking, Storage and Analysis.

  • Gale et al. (2022). MegaBlocks: Efficient Sparse Training with Mixture-of-Experts. arXiv preprint.

  • Gama et al. (2014). A survey on concept drift adaptation. ACM Computing Surveys.

  • Sorscher et al. (2022). Beyond Neural Scaling Laws: Beating Power Law Scaling via Data Pruning. Advances in Neural Information Processing Systems (NeurIPS).

  • Gao et al. (2020). The Pile: An 800GB Dataset of Diverse Text for Language Modeling. arXiv preprint arXiv:2101.00027.

  • Gartner (2024). Gartner Forecasts Worldwide Public Cloud End-User Spending to Total 1 Million Algorithm Due to Engineering Costs. Ars Technica.

  • Jordan (1982). A Guide to Parallel Computation and Some Cray-1 Experiences. Parallel Computations.

  • Jouppi et al. (2017). In-Datacenter Performance Analysis of a Tensor Processing Unit. Proceedings of the 44th Annual International Symposium on Computer Architecture.

  • Jouppi et al. (2021). Ten Lessons From Three Generations Shaped Google’s TPUv4i: Industrial Product. 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA).

  • Jouppi et al. (2023). TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning With Hardware Support for Embeddings. Proceedings of the 50th Annual International Symposium on Computer Architecture.

  • Jumper et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature.

  • Project Jupyter (2024). Project Jupyter.

  • Kalamkar et al. (2019). A Study of BFLOAT16 for Deep Learning Training.

  • Kamiran and Calders (2012). Data Preprocessing Techniques for Classification Without Discrimination. Knowledge and Information Systems.

  • Kaplan et al. (2020). Scaling Laws for Neural Language Models. ArXiv preprint.

  • Karpathy (2017). Software 2.0. Medium.

  • Chollet and others (2024). Keras.

  • Kiela et al. (2021). Dynabench: Rethinking Benchmarking in NLP. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

  • Kilburn et al. (1962). One-Level Storage System. IRE Transactions on Electronic Computers.

  • Kim et al. (2008). System level analysis of fast, per-core DVFS using on-chip switching regulators. IEEE International Symposium on High-Performance Computer Architecture (HPCA).

  • Kingma and Ba (2015). Adam: A Method for Stochastic Optimization. 3rd International Conference on Learning Representations (ICLR).

  • Kipf and Welling (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).

  • Kleinberg et al. (2017). Inherent Trade-Offs in the Fair Determination of Risk Scores. 8th Innovations in Theoretical Computer Science Conference (ITCS 2017).

  • Kleppmann (2016). Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. O’Reilly Media.

  • Knight (2023). OpenAI’s CEO says the age of giant AI models is already over. WIRED.

  • Koh et al. (2021). WILDS: A Benchmark of in-the-Wild Distribution Shifts. Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event.

  • Koizumi et al. (2019). ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection. 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).

  • Koomey et al. (2011). Implications of Historical Trends in the Electrical Efficiency of Computing. IEEE Annals of the History of Computing.

  • Kreuzberger et al. (2023). Machine Learning Operations (MLOps): Overview, Definition, and Architecture. IEEE Access.

  • Krishnamoorthi (2018). Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342.

  • Krishnan et al. (2022). Self-supervised learning in medicine and healthcare. Nature Biomedical Engineering.

  • Krizhevsky et al. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems (NeurIPS).

  • KServe Community (2024). KServe: Highly Scalable and Standards-Based Model Inference Platform on Kubernetes.

  • Authors (2024). Kubeflow.

  • Cloud Native Computing Foundation (2024). Kubernetes: Production-Grade Container Orchestration.

  • Kuhn and Johnson (2013). Applied Predictive Modeling. Springer New York.

  • Kullback and Leibler (1951). On Information and Sufficiency. The Annals of Mathematical Statistics.

  • Kung and Leiserson (1979). Systolic arrays (for VLSI). Sparse Matrix Proceedings 1978.

  • Kung (1982). Why systolic architectures?. Computer.

  • Kuznetsova et al. (2020). The Open Images Dataset V4. International Journal of Computer Vision.

  • Kwon and Rhu (2018). TensorDIMM. Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture.

  • Kwon et al. (2023). Efficient Memory Management for Large Language Model Serving With PagedAttention. Proceedings of the 29th Symposium on Operating Systems Principles.

  • HumanSignal (2024). Label Studio: Open Source Data Labeling Platform.

  • Labelbox, Inc. (2024). Labelbox Consensus Documentation.

  • Lai et al. (2018). CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs. ArXiv preprint.

  • Lam et al. (1991). The cache performance and optimizations of blocked algorithms. Proceedings of the fourth international conference on Architectural support for programming languages and operating systems - ASPLOS-IV.

  • Lamport et al. (1982). The Byzantine Generals Problem. ACM Transactions on Programming Languages and Systems.

  • Lampson (1983). Hints for Computer System Design. Proceedings of the Ninth ACM Symposium on Operating Systems Principles.

  • Landis and Koch (1977). The measurement of observer agreement for categorical data. Biometrics.

  • Lange (2009). Identifying Shades of Green: The SPECpower Benchmarks. IEEE Computer.

  • Lattner et al. (2020). MLIR: Scaling Compiler Infrastructure for Domain Specific Computation. 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO).

  • Lawson et al. (1979). Basic Linear Algebra Subprograms for Fortran Usage. ACM Transactions on Mathematical Software.

  • Lazer et al. (2014). The Parable of Google Flu: Traps in Big Data Analysis. Science.

  • Le Sueur and Heiser (2010). Dynamic voltage and frequency scaling: The laws of diminishing returns. Proceedings of the 2010 international conference on Power aware computing and systems.

  • Lebedev et al. (2015). Speeding up convolutional neural networks using fine-tuned CP-decomposition. 3rd International Conference on Learning Representations (ICLR).

  • LeCun et al. (1989). Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation.

  • LeCun et al. (1989). Optimal Brain Damage. Advances in Neural Information Processing Systems 2 (NIPS 1989).

  • LeCun et al. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE.

  • LeCun et al. (2012). Efficient BackProp. Neural Networks: Tricks of the Trade.

  • LeCun et al. (2015). Deep Learning. Nature.

  • Lee et al. (2022). Deduplicating Training Data Makes Language Models Better. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

  • Lehdonvirta et al. (2025). Measuring Domestic Public Cloud Compute Availability for Artificial Intelligence.

  • Lepikhin et al. (2021). GShard: Scaling giant models with conditional computation and automatic sharding. International Conference on Learning Representations (ICLR).

  • Leviathan et al. (2023). Fast Inference from Transformers via Speculative Decoding. International Conference on Machine Learning (ICML).

  • Li et al. (2007). t-Closeness: Privacy Beyond k-Anonymity and l-Diversity. 2007 IEEE 23rd International Conference on Data Engineering.

  • Li et al. (2014). Communication Efficient Distributed Machine Learning with the Parameter Server. Advances in Neural Information Processing Systems (NeurIPS).

  • Li et al. (2017). Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. Journal of Machine Learning Research.

  • Li et al. (2017). Pruning Filters for Efficient ConvNets. International Conference on Learning Representations (ICLR).

  • Li (2020). Estimating the Training Cost of GPT-3.

  • Li et al. (2021). The Deep Learning Compiler: A Comprehensive Survey. IEEE Transactions on Parallel and Distributed Systems.

  • Li et al. (2023). AlpaServe: Statistical multiplexing with model parallelism for deep learning serving. 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23).

  • Liang et al. (2022). Holistic Evaluation of Language Models. arXiv preprint arXiv:2211.09110.

  • Lie (2021). Thinking Outside the Die: Architecting the ML Accelerator of the Future.

  • Lighthill (1973). Artificial Intelligence: A General Survey. Artificial Intelligence: A Paper Symposium.

  • Lin et al. (2014). Microsoft COCO: Common Objects in Context. European Conference on Computer Vision (ECCV).

  • Lin et al. (2020). MCUNet: Tiny Deep Learning on IoT Devices. Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual.

  • Lin et al. (2024). AWQ: Activation-aware Weight Quantization for on-device LLM compression and acceleration. Proceedings of Machine Learning and Systems.

  • Lindholm et al. (2008). NVIDIA Tesla: A Unified Graphics and Computing Architecture. IEEE Micro.

  • Linnainmaa (1970). The Representation of the Cumulative Rounding Error of an Algorithm as a Taylor Expansion of the Local Rounding Errors.

  • Little (1961). A Proof for the Queuing Formula: . Operations Research.

  • Liu et al. (2019). DARTS: Differentiable Architecture Search. International Conference on Learning Representations (ICLR).

  • Liu et al. (2021). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

  • Liu et al. (2022). A ConvNet for the 2020s. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

  • Loshchilov and Hutter (2019). Decoupled Weight Decay Regularization. Proceedings of the International Conference on Learning Representations (ICLR).

  • Lowe (1999). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision.

  • Lu et al. (2023). STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition. arXiv preprint.

  • Lucic et al. (2018). Are GANs Created Equal? A Large-Scale Study. Advances in Neural Information Processing Systems (NeurIPS).

  • Lundberg and Lee (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS).

  • Lyons (2011). Understanding Digital Signal Processing. Prentice Hall.

  • Machanavajjhala et al. (2007). l-Diversity: Privacy Beyond k-Anonymity. ACM Transactions on Knowledge Discovery from Data.

  • Fowler (2014). Canary Release. Martin Fowler’s Blog.

  • Mattson et al. (2020). MLPerf Training Benchmark. Proceedings of Machine Learning and Systems (MLSys).

  • Mazumder et al. (2021). Multilingual Spoken Words Corpus. Advances in Neural Information Processing Systems 35 (NeurIPS 2021) Datasets and Benchmarks Track.

  • Mazumder (2021). TinyML: Multilingual spoken words corpus. tinyML Talks.

  • Mazumder et al. (2023). DataPerf: Benchmarks for Data-centric AI Development. Advances in Neural Information Processing Systems.

  • McCarthy et al. (1955). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. Dartmouth College.

  • McCrory (2010). Data Gravity – In the Clouds.

  • McCulloch and Pitts (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics.

  • McKinsey Global Institute (2021). The Internet of Things: Catching Up to an Accelerating Opportunity.

  • McMahan et al. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. International Conference on Artificial Intelligence and Statistics (AISTATS).

  • Mellempudi et al. (2019). Mixed Precision Training With 8-bit Floating Point. arXiv preprint arXiv:1905.12334.

  • Merity et al. (2016). Pointer Sentinel Mixture Models. arXiv preprint arXiv:1609.07843.

  • Netflix (2024). Metaflow.

  • Michel et al. (2011). Quantitative Analysis of Culture Using Millions of Digitized Books. Science.

  • Michel et al. (2019). Are Sixteen Heads Really Better than One?. Advances in Neural Information Processing Systems (NeurIPS).

  • Micikevicius et al. (2017). Mixed Precision Training. arXiv preprint arXiv:1710.03740.

  • Micikevicius et al. (2022). FP8 Formats for Deep Learning. arXiv preprint arXiv:2209.05433.

  • Lee (2016). Learning from Tay’s Introduction. The Official Microsoft Blog.

  • Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. International Conference on Learning Representations (ICLR).

  • Mila (2026). Open Source Software.

  • Minsky and Papert (1969). Perceptrons. The MIT Press.

  • Mirhoseini et al. (2017). Device Placement Optimization with Reinforcement Learning. Proceedings of the 34th International Conference on Machine Learning (ICML).

  • Mitchell (1980). The Need for Biases in Learning Generalizations.

  • Mitchell et al. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency.

  • MLCommons (2026). MLCommons: About Us.

  • MLflow Project (2026). MLflow Model Registry.

  • Databricks (2024). MLflow: An Open Source Platform for the Machine Learning Lifecycle.

  • MLCommons (2026). MLPerf Client Benchmark.

  • MLCommons (2024). MLPerf Mobile Benchmark.

  • MLCommons (2024). MLPerf Power Measurement.

  • MLCommons (2024). MLPerf Training Benchmark.

  • Moore (1998). Cramming More Components Onto Integrated Circuits. Proceedings of the IEEE.

  • Moravec (1988). Mind Children: The Future of Robot and Human Intelligence. Harvard University Press.

  • Moritz et al. (2018). Ray: A Distributed Framework for Emerging AI Applications. Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI).

  • Amazon Web Services (2024). Amazon Mechanical Turk.

  • Murray et al. (2021). tf.data. Proceedings of the VLDB Endowment.

  • Nair and Hinton (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th international conference on machine learning (ICML-10).

  • Nakkiran et al. (2019). Deep Double Descent: Where Bigger Models and More Data Hurt. arXiv preprint arXiv:1912.02292.

  • Narayanan et al. (2019). PipeDream. Proceedings of the 27th ACM Symposium on Operating Systems Principles.

  • Narayanan et al. (2021). Efficient large-scale language model training on GPU clusters using Megatron-LM. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis.

  • Mars Climate Orbiter Mishap Investigation Board (1999). Mars Climate Orbiter Mishap Investigation Board Phase I Report.

  • National Aeronautics and Space Administration (2016). NASA Systems Engineering Handbook.

  • Naumov et al. (2019). Deep Learning Recommendation Model for Personalization and Recommendation Systems. arXiv preprint arXiv:1906.00091.

  • naur1969softwareengineering (1969). Software Engineering: Report of a Conference Sponsored by the NATO Science Committee, Garmisch, Germany, 7–11 October 1968.

  • Nayak et al. (2022). Improving Voice Trigger Detection With Metric Learning. Interspeech 2022.

  • Project Jupyter (2026). nbconvert: Convert Notebooks to Other Formats.

  • Newell and Simon (1976). Computer Science as Empirical Inquiry: Symbols and Search. Communications of the ACM.

  • Neyshabur et al. (2017). Exploring Generalization in Deep Learning. Advances in Neural Information Processing Systems.

  • Ng (2021). MLOps: From Model-centric to Data-centric AI. DeepLearning.AI.

  • Nickolls et al. (2008). Scalable Parallel Programming With CUDA. ACM Queue.

  • Norrie et al. (2021). The Design Process for Google’s Training Chips: TPUv2 and TPUv3. IEEE Micro.

  • Northcutt et al. (2021). Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks. Advances in Neural Information Processing Systems 34 (NeurIPS 2021) Track on Datasets and Benchmarks.

  • National Transportation Safety Board (2017). Collision Between a Car Operating With Automated Vehicle Control Systems and a Tractor-Semitrailer Truck Near Williston, Florida, May 7, 2016.

  • National Transportation Safety Board (2019). Collision Between Vehicle Controlled by Developmental Automated Driving System and Pedestrian, Tempe, Arizona, March 18, 2018.

  • NVIDIA Corporation (2017). NVIDIA Tesla V100 GPU Architecture. NVIDIA Whitepaper.

  • NVIDIA Corporation (2018). NVIDIA Tesla T4 Tensor Core GPU. NVIDIA product documentation.

  • NVIDIA Corporation (2020). NVIDIA V100 Tensor Core GPU Datasheet. NVIDIA product documentation.

  • NVIDIA Corporation (2021). NVIDIA A100 Tensor Core GPU Datasheet. NVIDIA product documentation.

  • NVIDIA Corporation (2021). NVIDIA cuDNN Developer Guide.

  • NVIDIA Corporation (2023). NVIDIA cuDNN Developer Guide, Version 8.9.6.

  • NVIDIA Corporation (2024). NVIDIA Blackwell Architecture. NVIDIA product documentation.

  • NVIDIA (2024). NVIDIA TensorRT: Programmable Inference Accelerator.

  • NVIDIA (2026). NVIDIA Quantum-X800 InfiniBand Platform. NVIDIA product documentation.

  • NVIDIA Corporation (2026). How TensorRT Works.

  • NVIDIA Corporation (2026). Working with Dynamic Shapes.

  • NVIDIA (2026). Lazy Loading: CUDA Programming Guide.

  • NVIDIA (2026). NVIDIA CUDA Multi-Process Service.

  • NVIDIA (2026). NVIDIA Multi-Instance GPU User Guide.

  • NVIDIA (2026). NVIDIA TensorRT-LLM.

  • NVIDIA (2026). Model Management: NVIDIA Triton Inference Server.

  • NVIDIA (2026). Model Repository: NVIDIA Triton Inference Server.

  • NVIDIA (2024). cuBLAS: CUDA Basic Linear Algebra Subprograms.

  • NVIDIA (2020). Accelerating Matrix Multiplication with Block Sparse Format and NVIDIA Tensor Cores.

  • NVIDIA (2024). NVIDIA Omniverse and Simulation.

  • NVIDIA (2017). Training with Mixed Precision.

  • NVIDIA (2020). TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x.

  • NVIDIA (2024). NVIDIA Triton Inference Server.

  • Oakden-Rayner et al. (2020). Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. Proceedings of the ACM Conference on Health, Inference, and Learning.

  • Obermeyer et al. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science.

  • Olston et al. (2017). TensorFlow-Serving: Flexible, High-Performance ML Serving. Workshop on ML Systems at NIPS.

  • Intel Corporation (2021). oneDNN: Intel’s Deep Learning Neural Network Library.

  • Microsoft (2024). ONNX Runtime: Cross-Platform Inference and Training Machine-Learning Accelerator. GitHub.

  • OpenAI (2023). Introducing ChatGPT and Whisper APIs.

  • OpenAI (2023). New Models and Developer Products Announced at DevDay.

  • OpenAI et al. (2023). GPT-4 Technical Report. arXiv preprint arXiv:2303.08774.

  • OpenAI (2023). GPT-4.

  • OpenAI (2024). GPT-4o Mini: Advancing Cost-Efficient Intelligence.

  • OpenAI Developer Community (2024). Announcing GPT-4o in the API.

  • OpenBLAS Project (2026). OpenBLAS: An Optimized BLAS Library.

  • Intel Corporation (2026). OpenVINO Documentation.

  • Orr et al. (2021). Managing ML Pipelines. Proceedings of the VLDB Endowment.

  • Owens et al. (2008). GPU Computing. Proceedings of the IEEE.

  • Paleyes et al. (2022). Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Computing Surveys.

  • Palmer (1980). The Intel 8087 Numeric Data Processor. Proceedings of the 1980 National Computer Conference (AFIPS).

  • Papermill Project (2026). Papermill Documentation.

  • Papineni et al. (2002). Bleu. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL ’02.

  • Pareto (1896). Cours d’économie politique. F. Rouge.

  • Park et al. (2019). SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. Interspeech 2019.

  • Paszke et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems (NeurIPS).

  • Hennessy and Patterson (2017). Computer Architecture: A Quantitative Approach. Morgan Kaufmann.

  • Patterson et al. (2021). Carbon Emissions and Large Neural Network Training. arXiv preprint arXiv:2104.10350.

  • Paul et al. (2021). Deep Learning on a Data Diet: Finding Important Examples Early in Training. Advances in Neural Information Processing Systems (NeurIPS).

  • Maslej et al. (2024). Artificial Intelligence Index Report 2024. CoRR.

  • Pham et al. (2018). Efficient Neural Architecture Search via Parameter Sharing. Proceedings of the 35th International Conference on Machine Learning (ICML).

  • Pichai (2023). Introducing Gemini: Our Largest and Most Capable AI Model.

  • Pineau et al. (2021). Improving reproducibility in machine learning research (a report from the neurips 2019 reproducibility program). Journal of machine learning research.

  • Pope et al. (2023). Efficiently Scaling Transformer Inference. Proceedings of Machine Learning and Systems (MLSys).

  • Prefect Technologies, Inc. (2024). Prefect: Workflow Orchestration Framework for Python.

  • Cloud Native Computing Foundation (2024). Prometheus: Monitoring System and Time Series Database.

  • Protocol Buffers Authors (2026). Overview: Protocol Buffers.

  • Pushkarna et al. (2022). Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT).

  • Putnam et al. (2014). A reconfigurable fabric for accelerating large-scale datacenter services. Proceedings of the 41st Annual International Symposium on Computer Architecture (ISCA).

  • PyTorch Contributors (2026). Tensor Views.

  • PyTorch Contributors (2026). PyTorch Autograd Mechanics.

  • PyTorch Contributors (2026). torch.export.

  • PyTorch Contributors (2026). TorchScript.

  • PyTorch (2022). Accelerating Neural Network Training with Sparse Tensors.

  • Qi et al. (2021). An efficient pruning scheme of deep neural networks for Internet of Things applications. EURASIP Journal on Advances in Signal Processing.

  • quinonero2009dataset (2009). Dataset Shift in Machine Learning. The MIT Press.

  • Rachwan et al. (2022). Winning the lottery ahead of time: Efficient early network pruning. International Conference on Machine Learning (ICML).

  • Radford et al. (2019). Language models are unsupervised multitask learners.

  • Radford et al. (2021). Learning transferable visual models from natural language supervision. International Conference on Machine Learning (ICML).

  • Raichle and Gusnard (2002). Appraising the Brain’s Energy Budget. Proceedings of the National Academy of Sciences.

  • Rainio et al. (2024). Evaluation metrics and statistical tests for machine learning. Scientific Reports.

  • Raji and Buolamwini (2019). Actionable Auditing. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society.

  • Rajpurkar et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.

  • Ranjit et al. (2019). Efficient Deep Learning Hyperparameter Tuning Using Cloud Infrastructure: Intelligent Distributed Hyperparameter Tuning With Bayesian Optimization in the Cloud. 2019 IEEE 12th International Conference on Cloud Computing (CLOUD).

  • Ratner et al. (2017). Snorkel. Proceedings of the VLDB Endowment.

  • Ratner et al. (2018). Snorkel MeTaL. Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning.

  • Ratner et al. (2019). Weak supervision: A new programming paradigm for machine learning. Stanford AI Lab Blog.

  • Reagen et al. (2017). Deep Learning for Computer Architects. Springer International Publishing.

  • Real et al. (2019). Regularized Evolution for Image Classifier Architecture Search. AAAI Conference on Artificial Intelligence.

  • Recht et al. (2019). Do ImageNet Classifiers Generalize to ImageNet?. Proceedings of the 36th International Conference on Machine Learning (ICML).

  • Reddi et al. (2019). MLPerf Inference Benchmark. 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA).

  • Ren et al. (2021). A Survey of Deep Active Learning. ACM Computing Surveys.

  • Ribeiro et al. (2016). Why Should I Trust You?: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

  • Ribeiro et al. (2020). Auditing radicalization pathways on YouTube. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.

  • Ribeiro et al. (2020). Beyond Accuracy: Behavioral Testing of NLP Models with CheckList. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.

  • Roesch et al. (2018). Relay: A New IR for Machine Learning Frameworks. Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages.

  • Rombach et al. (2022). High-Resolution Image Synthesis With Latent Diffusion Models. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

  • Romero et al. (2021). INFaaS: Automated Model-less Inference Serving. 2021 USENIX Annual Technical Conference (USENIX ATC 21).

  • Rosenblatt (1957). The Perceptron: A Perceiving and Recognizing Automaton.

  • Rosenblatt (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review.

  • Royce (1970). Managing the Development of Large Software Systems. Proceedings of IEEE WESCON.

  • Rumelhart et al. (1986). Learning representations by back-propagating errors. Nature.

  • Russakovsky et al. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision.

  • Sabour et al. (2017). Dynamic Routing Between Capsules. Advances in Neural Information Processing Systems (NeurIPS).

  • Sambasivan et al. (2021). ``Everyone wants to do the model work, not the data work’’: Data Cascades in High-Stakes AI. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems.

  • Samuel (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development.

  • Sandler et al. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

  • Sanh et al. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing at NeurIPS 2019.

  • Savage (2009). The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty. John Wiley & Sons.

  • Scale AI, Inc. (2024). Scale AI Advanced Settings.

  • Schelter et al. (2018). Automating large-scale machine learning model management. Proceedings of the 2018 IEEE International Conference on Data Engineering (ICDE).

  • Schwartz et al. (2020). Green AI. Communications of the ACM.

  • scikit-learn developers (2024). sklearn.metrics.confusion_matrix — scikit-learn documentation. scikit-learn Documentation.

  • scikit-learn developers (2024). Feature selection — scikit-learn documentation. scikit-learn Documentation.

  • Scott (2012). Numbers every programmer should know by year.

  • Sculley et al. (2015). Hidden Technical Debt in Machine Learning Systems. Advances in Neural Information Processing Systems (NeurIPS).

  • U.S. Securities and Exchange Commission (2013). Securities Exchange Act of 1934, Release No. 70694: Knight Capital Americas LLC.

  • Patel and Wong (2023). GPT-4 Architecture, Infrastructure, Training Dataset, Costs, Vision, MoE. SemiAnalysis Blog.

  • Sener and Savarese (2018). Active Learning for Convolutional Neural Networks: A Core-Set Approach. International Conference on Learning Representations (ICLR).

  • Sennrich et al. (2016). Improving Neural Machine Translation Models with Monolingual Data. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

  • Sergeev and Balso (2018). Horovod: fast and easy distributed deep learning in TensorFlow. arXiv preprint arXiv:1802.05799.

  • Settles (2009). Active Learning Literature Survey.

  • Settles (2012). Active Learning. Springer Cham.

  • Sevilla et al. (2022). Compute Trends Across Three Eras of Machine Learning. 2022 International Joint Conference on Neural Networks (IJCNN).

  • Shah et al. (2024). FlashAttention-3: Fast and Accurate Attention With Asynchrony and Low-Precision. Advances in Neural Information Processing Systems 37 (NeurIPS).

  • Shannon (1948). A Mathematical Theory of Communication. Bell System Technical Journal.

  • Shazeer et al. (2017). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. International Conference on Learning Representations (ICLR).

  • Shazeer and Stern (2018). Adafactor: Adaptive Learning Rates with Sublinear Memory Cost. International Conference on Machine Learning (ICML).

  • Shen et al. (2019). Nexus. Proceedings of the 27th ACM Symposium on Operating Systems Principles.

  • Shen et al. (2020). Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. Proceedings of the AAAI Conference on Artificial Intelligence.

  • Sheng and Zhang (2019). Machine Learning With Crowdsourcing: A Brief Summary of the Past Research and Future Directions. The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019.

  • Shi et al. (2016). Edge Computing: Vision and Challenges. IEEE Internet of Things Journal.

  • Shneiderman (2022). Human-centered AI. Oxford University Press.

  • Shoeybi et al. (2019). Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism. arXiv preprint arXiv:1909.08053.

  • Shokri et al. (2017). Membership Inference Attacks Against Machine Learning Models. 2017 IEEE Symposium on Security and Privacy (SP).

  • Shorten and Khoshgoftaar (2019). A Survey on Image Data Augmentation for Deep Learning. Journal of Big Data.

  • Shortliffe et al. (1975). Computer-based consultations in clinical therapeutics: Explanation and rule acquisition capabilities of the MYCIN system. Computers and Biomedical Research.

  • Shumailov et al. (2024). AI Models Collapse When Trained on Recursively Generated Data. Nature.

  • Sifre and Mallat (2014). Rigid-Motion Scattering for Image Classification. arXiv preprint arXiv:1403.1687.

  • Silver et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature.

  • Silver et al. (2017). Mastering the game of Go without human knowledge. Nature.

  • Simonyan and Zisserman (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations (ICLR).

  • Snow et al. (2008). Cheap and fast—but is it good?. Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP ’08.

  • Sohn et al. (2020). FixMatch: Simplifying Semi-supervised Learning with Consistency and Confidence. Advances in Neural Information Processing Systems (NeurIPS).

  • Sokolova and Lapalme (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management.

  • Soviany et al. (2022). Curriculum Learning: A Survey. International Journal of Computer Vision.

  • Srivastava et al. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research.

  • Statista Research Department (2024). Number of sent and received e-mails per day worldwide from 2017 to 2027. Statista.

  • Stephens et al. (2017). The ARM Scalable Vector Extension. IEEE Micro.

  • Stonebraker et al. (2018). C-Store: A Column-Oriented DBMS. Making Databases Work: the Pragmatic Wisdom of Michael Stonebraker.

  • Strassen (1969). Gaussian elimination is not optimal. Numerische Mathematik.

  • Strathern (1997). `Improving Ratings’: Audit in the British University System. European Review.

  • Stromberg (2015). When was the first traffic light installed? Today in 1914. Vox.

  • Strubell et al. (2019). Energy and Policy Considerations for Deep Learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

  • Sullivan et al. (2012). Overview of the High Efficiency Video Coding (HEVC) Standard. IEEE Transactions on Circuits and Systems for Video Technology.

  • Sun et al. (2020). Scalability in Perception for Autonomous Driving: Waymo Open Dataset. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

  • Sutskever et al. (2014). Sequence to Sequence Learning with Neural Networks. Advances in Neural Information Processing Systems (NeurIPS).

  • Sutton (2019). The Bitter Lesson. incompleteideas.net.

  • Sweeney (2002). K-ANONYMITY: A MODEL for PROTECTING PRIVACY. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.

  • Sze et al. (2017). Efficient Processing of Deep Neural Networks: A Tutorial and Survey. Proceedings of the IEEE.

  • Szegedy et al. (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

  • Tan et al. (2019). MnasNet: Platform-Aware Neural Architecture Search for Mobile. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

  • Tan and Le (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. International Conference on Machine Learning (ICML).

  • Teerapittayanon et al. (2017). BranchyNet: Fast Inference via Early Exiting From Deep Neural Networks. 2016 23rd International Conference on Pattern Recognition (ICPR).

  • Telgarsky (2016). Benefits of Depth in Neural Networks. Proceedings of the 29th Annual Conference on Learning Theory.

  • TensorFlow Developers (2024). Optimize TensorFlow Performance Using the Profiler.

  • TensorFlow Developers (2024). Better Performance with tf.function.

  • HashiCorp (2014). Terraform: Infrastructure as Code. Software available from https://www.terraform.io/.

  • Tesler (1984). The Law of Conservation of Complexity. Web page.

  • Thyagarajan et al. (2022). Identifying Incorrect Annotations in Multi-Label Classification Data. CoRR.

  • Tibshirani (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological).

  • Tillet et al. (2019). Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations. Proceedings of the 3rd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages.

  • Toneva et al. (2019). An Empirical Study of Example Forgetting During Deep Neural Network Learning. International Conference on Learning Representations (ICLR).

  • Touvron et al. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv preprint arXiv:2302.13971.

  • Touvron et al. (2023). Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv preprint arXiv:2307.09288.

  • Tramèr et al. (2016). Stealing machine learning models via prediction APIs. 25th USENIX Security Symposium (USENIX Security 16).

  • Tschand et al. (2024). MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems From μWatts to MWatts for Sustainable AI. 2025 IEEE International Symposium on High Performance Computer Architecture (HPCA).

  • Turakhia et al. (2018). Darwin: A Genomics Co-processor Provides up to 15,000X Acceleration on Long Read Assembly. Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS).

  • Turing (1950). I.—Computing Machinery and Intelligence. Mind.

  • Dua and Graff (2024). UCI Machine Learning Repository.

  • Ultralytics (2023). YOLOv8.

  • Umuroglu et al. (2017). Finn. Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays.

  • Equal Employment Opportunity Commission et al. (1978). Uniform Guidelines on Employee Selection Procedures. Code of Federal Regulations.

  • Vasisht et al. (2017). FarmBeats: An IoT Platform for Data-Driven Agriculture. 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17).

  • Vaswani et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems (NeurIPS).

  • Google Cloud (2026). Explore Models in Model Garden.

  • Google Cloud (2024). Tune models overview.

  • Google Cloud (2024). Vertex AI Model Registry.

  • Villalobos et al. (2022). Will we run out of data? Limits of LLM scaling based on human-generated data. arXiv preprint arXiv:2211.04325.

  • Viola and Jones (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

  • Wang et al. (2018). GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP.

  • Wang et al. (2018). SkipNet: Learning Dynamic Routing in Convolutional Networks. European Conference on Computer Vision (ECCV).

  • Wang et al. (2019). Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations. 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

  • Wang et al. (2019). Benchmarking TPU, GPU, and CPU Platforms for Deep Learning. arXiv preprint arXiv:1907.10701.

  • Wang et al. (2019). SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems. arXiv preprint arXiv:1905.00537.

  • Wang et al. (2018). SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks. Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming.

  • Wang et al. (2021). CNN Explainer: Learning Convolutional Neural Networks With Interactive Visualization. IEEE Transactions on Visualization and Computer Graphics.

  • Warden (2018). Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition. arXiv preprint arXiv:1804.03209.

  • Warden and Situnayake (2020). TinyML: Machine learning with TensorFlow Lite on Arduino and ultra-low-power microcontrollers. O’Reilly Media.

  • Waterman et al. (2013). The RISC-V Instruction Set. 2013 IEEE Hot Chips 25 Symposium (HCS).

  • Wei et al. (2022). Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems (NeurIPS).

  • Weights & Biases, Inc. (2024). Weights & Biases: The AI Developer Platform.

  • Weiser (1991). The Computer for the 21st Century. Scientific American.

  • Weizenbaum (1966). ELIZA–a computer program for the study of natural language communication between man and machine. Communications of the ACM.

  • Welling (2009). Herding dynamical weights to learn. Proceedings of the 26th Annual International Conference on Machine Learning.

  • Wengert (1964). A simple automatic derivative evaluation program. Communications of the ACM.

  • Werbos (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences.

  • Werchniak et al. (2021). Exploring the application of synthetic audio in training keyword spotters. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

  • Wexler et al. (2020). The What-If Tool: Interactive Probing of Machine Learning Models. IEEE Transactions on Visualization and Computer Graphics.

  • Widmer and Kubat (1996). Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning.

  • Merity (2016). The WikiText Long Term Dependency Language Modeling Dataset.

  • Williams et al. (2009). Roofline. Communications of the ACM.

  • Wolpert and Macready (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation.

  • Wu et al. (2019). FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

  • Wu et al. (2019). Fast Neural Networks: Efficient and Adaptive Computation for Inference. Advances in Neural Information Processing Systems (NeurIPS).

  • Wu et al. (2020). Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation. arXiv preprint arXiv:2004.09602.

  • Wulf and McKee (1995). Hitting the memory wall. ACM SIGARCH Computer Architecture News.

  • Xin et al. (2021). BERxiT: Early Exiting for BERT With Better Fine-Tuning and Extension to Regression. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume.

  • Xu et al. (2024). Benchmarking Benchmark Leakage in Large Language Models. arXiv preprint arXiv:2404.18824.

  • Yang et al. (2020). Resolution Adaptive Networks for Efficient Inference. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

  • Yao et al. (2021). HAWQ-V3: Dyadic Neural Network Quantization. Proceedings of the 38th International Conference on Machine Learning (ICML).

  • Yee et al. (2021). Image Cropping on Twitter: Fairness Metrics, Their Limitations, and the Importance of Representation, Design, and Agency. Proceedings of the ACM on Human-Computer Interaction.

  • Yosinski et al. (2014). How transferable are features in deep neural networks?. Advances in neural information processing systems.

  • Yu et al. (2022). Orca: A Distributed Serving System for Transformer-Based Generative Models. 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22).

  • Yun et al. (2019). CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features. 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

  • Yurdakul and Naranjo (2020). Statistical properties of the population stability index. The Journal of Risk Model Validation.

  • Zafrir et al. (2019). Q8BERT: Quantized 8Bit BERT. 2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition (EMC2-NIPS).

  • Zaharia et al. (2010). Spark: Cluster Computing with Working Sets. HotCloud.

  • Zaharia et al. (2018). Accelerating the Machine Learning Lifecycle with MLflow. IEEE Data Engineering Bulletin.

  • Zaharia et al. (2021). Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics. Proceedings of CIDR.

  • Zech et al. (2018). Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLOS Medicine.

  • Zhang et al. (2017). Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. BioMedical Engineering OnLine.

  • Zhang et al. (2017). Hello Edge: Keyword Spotting on Microcontrollers.

  • Zhang et al. (2017). Understanding deep learning requires rethinking generalization. International Conference on Learning Representations (ICLR).

  • Zhang et al. (2018). Mitigating Unwanted Biases With Adversarial Learning. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society.

  • Zhang et al. (2018). mixup: Beyond Empirical Risk Minimization. International Conference on Learning Representations (ICLR).

  • Zhang et al. (2019). MArk: Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving. 2019 USENIX Annual Technical Conference (USENIX ATC 19).

  • Zhang and Sennrich (2019). Root Mean Square Layer Normalization. Advances in Neural Information Processing Systems (NeurIPS).

  • Zhang et al. (2020). Fast Hardware-Aware Neural Architecture Search. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

  • Zhao et al. (2024). GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection. arXiv preprint.

  • Zheng et al. (2020). Ansor: Generating High-Performance Tensor Programs for Deep Learning. Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI).

  • Zhou et al. (2021). Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch. arXiv preprint.

  • Zhu et al. (2019). Deep Leakage from Gradients. Advances in Neural Information Processing Systems (NeurIPS).

  • Zillow Group (2021). Zillow Group Reports Third-Quarter 2021 Financial Results and Shares Plan to Wind Down Zillow Offers Operations. Investor Relations Press Release.

  • Zoph and Le (2016). Neural Architecture Search with Reinforcement Learning. Proceedings of the 5th International Conference on Learning Representations (ICLR).

  • Zu et al. (2024). Resiliency at Scale: Managing Google’s TPUv4 Machine Learning Supercomputer. 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24).