222 citations · 268 across the 4 of their papers we have counts for
5 papers · 1 filter
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks
Hieu Pham, Quoc V. Le
Neural networks are often over-parameterized and hence benefit from aggressive regularization. Conventional regularization methods, such as Dropout or weight decay, do not leverage…
Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour
Arissa Wongpanich, Hieu Pham, James Demmel +4
EfficientNets are a family of state-of-the-art image classification models based on efficiently scaled convolutional neural networks. Currently, EfficientNets can take on the order…
Towards Domain-Agnostic Contrastive Learning
Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi +2
Despite recent success, most contrastive self-supervised learning methods are domain-specific, relying heavily on data augmentation techniques that require knowledge about a partic…
Efficient Neural Architecture Search via Parameter Sharing
Hieu Pham, Melody Y. Guan, Barret Zoph +2
We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architec…
Device Placement Optimization with Reinforcement Learning
Azalia Mirhoseini, Hieu Pham, Quoc V. Le +7
The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these req…