Search Results
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
NeurIPS 2019
• google-research/google-research
• 
Our algorithm, search on the replay buffer (SoRB), enables agents to solve sparse reward tasks over hundreds of steps, and generalizes substantially better than standard RL algorithms.
Optimizing Generalized Rate Metrics with Three Players
NeurIPS 2019
• google-research/google-research
•
We present a general framework for solving a large class of learning problems with non-linear functions of classification rates.
Memory Efficient Adaptive Optimization
NeurIPS 2019
• google-research/google-research
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Adaptive gradient-based optimizers such as Adagrad and Adam are crucial for achieving state-of-the-art performance in machine translation and language modeling.
A Benchmark for Interpretability Methods in Deep Neural Networks
NeurIPS 2019
• google-research/google-research
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We propose an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks.
Differentiable Ranking and Sorting using Optimal Transport
NeurIPS 2019
• google-research/google-research
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From this observation, we propose extended rank and sort operators by considering optimal transport (OT) problems (the natural relaxation for assignments) where the auxiliary measure can be any weighted measure supported on $m$ increasing values, where $m \ne n$.
Reducing the variance in online optimization by transporting past gradients
NeurIPS 2019
• google-research/google-research
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While variance reduction methods have shown that reusing past gradients can be beneficial when there is a finite number of datapoints, they do not easily extend to the online setting.
Practical and Consistent Estimation of f-Divergences
NeurIPS 2019
• google-research/google-research
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The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning.
Unsupervised learning of object structure and dynamics from videos
NeurIPS 2019
• google-research/google-research
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Extracting and predicting object structure and dynamics from videos without supervision is a major challenge in machine learning.
Stand-Alone Self-Attention in Vision Models
NeurIPS 2019
• google-research/google-research
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The natural question that arises is whether attention can be a stand-alone primitive for vision models instead of serving as just an augmentation on top of convolutions.
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.