Search Results
Similarity of Neural Network Representations Revisited
ICML 2019
• google-research/google-research
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We introduce a similarity index that measures the relationship between representational similarity matrices and does not suffer from this limitation.
Evaluating Feature Importance Estimates
arXiv preprint arXiv:1806.10758 2019
• google-research/google-research
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Interpretability methods should be both meaningful to a human and correctly explain model behavior.
FEATURE IMPORTANCE IMAGE CLASSIFICATION INTERPRETABLE MACHINE LEARNING
The State of Sparsity in Deep Neural Networks
• google-research/google-research
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We rigorously evaluate three state-of-the-art techniques for inducing sparsity in deep neural networks on two large-scale learning tasks: Transformer trained on WMT 2014 English-to-German, and ResNet-50 trained on ImageNet.
NoRML: No-Reward Meta Learning
• google-research/google-research
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To this end, we introduce a method that allows for self-adaptation of learned policies: No-Reward Meta Learning (NoRML).
Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.