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Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML.
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MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
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The Loss Surfaces of Multilayer Networks
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Adam: A Method for Stochastic Optimization
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TensorFlow: A System for Large-Scale Machine Learning
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Wide & Deep Learning for Recommender Systems
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MovieLens 20M Dataset, Oct 2016
GroupLens · 2016
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Speed/Accuracy Trade-offs for Modern Convolutional Object Detectors, 2016
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SSD: Single Shot Multibox Detector
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Mastering the Game of Go with Deep Neural Networks and Tree Search
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Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
OpenAI Five, Jun 2018
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{ \{ TVM } \} : An Automated End-to-End Optimizing Compiler for Deep Learning
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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NVIDIA Tensor Core Programmability, Performance & Precision
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Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al · 2016
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Zhu, C., Han, S., Mao, H., and Dally, W. J · 2016
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DeepBench: Benchmarking Deep Learning Operations on Different Hardware
Baidu · 2017
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DAWNBench: An End-to-End Deep Learning Benchmark and Competition
Coleman, C., Narayanan, D., Kang, D., Zhao, T., Zhang, J., Nardi, L., Bailis, P., Olukotun, K., Ré, C., and Zaharia, M · 2017
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TensorFlow Benchmarks
Google · 2017
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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
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In-Datacenter Performance Analysis of a Tensor Processing Unit
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Mixed Precision Training
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Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash
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A General Reinforcement Learning Algorithm that masters Chess, Shogi, and Go through Self-Play
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Image Classification at Supercomputer Scale
Ying, C., Kumar, S., Chen, D., Wang, T., and Cheng, Y · 2018
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Deep Interest Network for Click-through Rate Prediction
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Benchmarking and Analyzing Deep Neural Network Training
Zhu, H., Akrout, M., Zheng, B., Pelegris, A., Jayarajan, A., Phanishayee, A., Schroeder, B., and Pekhimenko, G · 2018
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ONNX: Open Neural Network Exchange
Bai, J., Lu, F., Zhang, K., et al · 2019
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Scalable Realistic Recommendation Datasets through Fractal Expansions
Belletti, F., Lakshmanan, K., Krichene, W., Chen, Y.-F., and Anderson, J · 2019
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A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning
Ben-Nun, T., Besta, M., Huber, S., Ziogas, A. N., Peter, D., and Hoefler, T · 2019
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Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
Coleman, C., Kang, D., Narayanan, D., Nardi, L., Zhao, T., Zhang, J., Bailis, P., Olukotun, K., Ré, C., and Zaharia, M · 2019
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BigDL: Distributed Deep Learning Library for Apache Spark, 2019
Intel · 2019
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PipeDream: Generalized Pipeline Parallelism for DNN Training
Narayanan, D., Harlap, A., Phanishayee, A., Seshadri, V., Devanur, N. R., Ganger, G. R., Gibbons, P. B., and Zaharia, M · 2019
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Deep Learning Recommendation Model for Personalization and Recommendation Systems
Naumov, M., Mudigere, D., Shi, H.-J. M., Huang, J., Sundaraman, N., Park, J., Wang, X., Gupta, U., Wu, C.-J., Azzolini, A. G., et al · 2019
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Language Models are Unsupervised Multitask Learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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Sun, P., Feng, W., Han, R., Yan, S., and Wen, Y · 2019
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