Timezone: »
There is growing interest in automating neural network architecture design. Existing architecture search methods can be computationally expensive, requiring thousands of different architectures to be trained from scratch. Recent work has explored \emph{weight sharing} across models to amortize the cost of training. Although previous methods reduced the cost of architecture search by orders of magnitude, they remain complex, requiring hypernetworks or reinforcement learning controllers. We aim to understand weight sharing for one-shot architecture search. With careful experimental analysis, we show that it is possible to efficiently identify promising architectures from a complex search space without either hypernetworks or RL.
Author Information
Gabriel Bender (Google)
Pieter-Jan Kindermans (Google)
Barret Zoph (Google)
Vijay Vasudevan (Google)
Quoc Le (Google Brain)
Related Events (a corresponding poster, oral, or spotlight)
-
2018 Poster: Understanding and Simplifying One-Shot Architecture Search »
Fri Jul 13th 04:15 -- 07:00 PM Room Hall B
More from the Same Authors
-
2020 Poster: Go Wide, Then Narrow: Efficient Training of Deep Thin Networks »
Denny Zhou · Mao Ye · Chen Chen · Tianjian Meng · Mingxing Tan · Xiaodan Song · Quoc Le · Qiang Liu · Dale Schuurmans -
2020 Poster: AutoML-Zero: Evolving Machine Learning Algorithms From Scratch »
Esteban Real · Chen Liang · David So · Quoc Le -
2019 Poster: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks »
Mingxing Tan · Quoc Le -
2019 Poster: The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study »
Daniel Park · Jascha Sohl-Dickstein · Quoc Le · Samuel Smith -
2019 Poster: The Evolved Transformer »
David So · Quoc Le · Chen Liang -
2019 Oral: The Evolved Transformer »
David So · Quoc Le · Chen Liang -
2019 Oral: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks »
Mingxing Tan · Quoc Le -
2019 Oral: The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study »
Daniel Park · Jascha Sohl-Dickstein · Quoc Le · Samuel Smith -
2018 Poster: Learning Longer-term Dependencies in RNNs with Auxiliary Losses »
Trieu H Trinh · Andrew Dai · Thang Luong · Quoc Le -
2018 Oral: Learning Longer-term Dependencies in RNNs with Auxiliary Losses »
Trieu H Trinh · Andrew Dai · Thang Luong · Quoc Le -
2018 Poster: Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? »
Maithra Raghu · Alexander Irpan · Jacob Andreas · Bobby Kleinberg · Quoc Le · Jon Kleinberg -
2018 Oral: Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? »
Maithra Raghu · Alexander Irpan · Jacob Andreas · Bobby Kleinberg · Quoc Le · Jon Kleinberg -
2018 Poster: Efficient Neural Architecture Search via Parameters Sharing »
Hieu Pham · Melody Guan · Barret Zoph · Quoc Le · Jeff Dean -
2018 Oral: Efficient Neural Architecture Search via Parameters Sharing »
Hieu Pham · Melody Guan · Barret Zoph · Quoc Le · Jeff Dean -
2017 Poster: Large-Scale Evolution of Image Classifiers »
Esteban Real · Sherry Moore · Andrew Selle · Saurabh Saxena · Yutaka Leon Suematsu · Jie Tan · Quoc Le · Alexey Kurakin -
2017 Poster: Neural Optimizer Search using Reinforcement Learning »
Irwan Bello · Barret Zoph · Vijay Vasudevan · Quoc Le -
2017 Poster: Device Placement Optimization with Reinforcement Learning »
Azalia Mirhoseini · Hieu Pham · Quoc Le · benoit steiner · Mohammad Norouzi · Rasmus Larsen · Yuefeng Zhou · Naveen Kumar · Samy Bengio · Jeff Dean -
2017 Talk: Neural Optimizer Search using Reinforcement Learning »
Irwan Bello · Barret Zoph · Vijay Vasudevan · Quoc Le -
2017 Talk: Large-Scale Evolution of Image Classifiers »
Esteban Real · Sherry Moore · Andrew Selle · Saurabh Saxena · Yutaka Leon Suematsu · Jie Tan · Quoc Le · Alexey Kurakin -
2017 Talk: Device Placement Optimization with Reinforcement Learning »
Azalia Mirhoseini · Hieu Pham · Quoc Le · benoit steiner · Mohammad Norouzi · Rasmus Larsen · Yuefeng Zhou · Naveen Kumar · Samy Bengio · Jeff Dean