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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
8 June 2017
Priya Goyal
Piotr Dollár
Ross B. Girshick
P. Noordhuis
Lukasz Wesolowski
Aapo Kyrola
Andrew Tulloch
Yangqing Jia
Kaiming He
3DH
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Papers citing
"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour"
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Title
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
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Farkas layers: don't shift the data, fix the geometry
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MLPerf Training Benchmark
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SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
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Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos
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Chuang Gan
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The Non-IID Data Quagmire of Decentralized Machine Learning
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Elastic deep learning in multi-tenant GPU cluster
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Revisiting Knowledge Distillation via Label Smoothing Regularization
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Speech Recognition with Augmented Synthesized Speech
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Gap Aware Mitigation of Gradient Staleness
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Machine Learning Pipelines with Modern Big Data Tools for High Energy Physics
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Scale MLPerf-0.6 models on Google TPU-v3 Pods
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Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
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Heterogeneity-Aware Asynchronous Decentralized Training
Qinyi Luo
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DARTS+: Improved Differentiable Architecture Search with Early Stopping
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CARS: Continuous Evolution for Efficient Neural Architecture Search
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Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle
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Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
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Distributed Training of Embeddings using Graph Analytics
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Roshan Dathathri
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Todd Mytkowicz
Olli Saarikivi The University of Texas at Austin
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Linear Context Transform Block
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Minibatch Processing in Spiking Neural Networks
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Hierarchical Federated Learning Across Heterogeneous Cellular Networks
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POD: Practical Object Detection with Scale-Sensitive Network
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Beyond Human-Level Accuracy: Computational Challenges in Deep Learning
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Training-Time-Friendly Network for Real-Time Object Detection
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TapirXLA: Embedding Fork-Join Parallelism into the XLA Compiler in TensorFlow Using Tapir
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Distributed Deep Learning for Precipitation Nowcasting
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Push for Center Learning via Orthogonalization and Subspace Masking for Person Re-Identification
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Instance Scale Normalization for image understanding
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