Search Results for author: Jeff Dean

Found 16 papers, 8 papers with code

Carbon Emissions and Large Neural Network Training

no code implementations21 Apr 2021 David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, Jeff Dean

To help reduce the carbon footprint of ML, we believe energy usage and CO2e should be a key metric in evaluating models, and we are collaborating with MLPerf developers to include energy usage during training and inference in this industry standard benchmark.

Neural Architecture Search

Interlocking Backpropagation: Improving depthwise model-parallelism

1 code implementation8 Oct 2020 Aidan N. Gomez, Oscar Key, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal

Motivated by poor resource utilisation, we introduce a class of intermediary strategies between local and global learning referred to as interlocking backpropagation.

Image Classification

Faster Discovery of Neural Architectures by Searching for Paths in a Large Model

no code implementations ICLR 2018 Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean

We propose Efficient Neural Architecture Search (ENAS), a faster and less expensive approach to automated model design than previous methods.

Neural Architecture Search

A Hierarchical Model for Device Placement

no code implementations ICLR 2018 Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean

We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices.

Machine Translation Translation

Device Placement Optimization with Reinforcement Learning

1 code implementation ICML 2017 Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices.

Language Modelling Machine Translation +1

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

3 code implementations23 Jan 2017 Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean

In this work, we address these challenges and finally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters.

Language Modelling Machine Translation +1

Distilling the Knowledge in a Neural Network

45 code implementations9 Mar 2015 Geoffrey Hinton, Oriol Vinyals, Jeff Dean

A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions.

Knowledge Distillation

Using Web Co-occurrence Statistics for Improving Image Categorization

no code implementations19 Dec 2013 Samy Bengio, Jeff Dean, Dumitru Erhan, Eugene Ie, Quoc Le, Andrew Rabinovich, Jonathon Shlens, Yoram Singer

Albeit the simplicity of the resulting optimization problem, it is effective in improving both recognition and localization accuracy.

Common Sense Reasoning Image Categorization +1

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