Search Results
Predicting the Generalization Gap in Deep Networks with Margin Distributions
google-research/google-research
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ICLR 2019
In this paper, we propose such a measure, and conduct extensive empirical studies on how well it can predict the generalization gap.
Can weight sharing outperform random architecture search? An investigation with TuNAS
google-research/google-research
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CVPR 2020
Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models.
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
google-research/google-research
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NeurIPS 2019
Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}.
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
google-research/google-research
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NeurIPS 2019
We give a new analysis of this sketch, providing nearly optimal bounds.
Momentum-Based Variance Reduction in Non-Convex SGD
google-research/google-research
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NeurIPS 2019
Variance reduction has emerged in recent years as a strong competitor to stochastic gradient descent in non-convex problems, providing the first algorithms to improve upon the converge rate of stochastic gradient descent for finding first-order critical points.
Saccader: Improving Accuracy of Hard Attention Models for Vision
google-research/google-research
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NeurIPS 2019
Although deep convolutional neural networks achieve state-of-the-art performance across nearly all image classification tasks, their decisions are difficult to interpret.
Milking CowMask for Semi-Supervised Image Classification
google-research/google-research
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Using it to provide perturbations for semi-supervised consistency regularization, we achieve a state-of-the-art result on ImageNet with 10% labeled data, with a top-5 error of 8. 76% and top-1 error of 26. 06%.
Meta-Learning Requires Meta-Augmentation
google-research/google-research
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NeurIPS 2020
Meta-learning algorithms aim to learn two components: a model that predicts targets for a task, and a base learner that quickly updates that model when given examples from a new task.
A Spectral Energy Distance for Parallel Speech Synthesis
google-research/google-research
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NeurIPS 2020
Speech synthesis is an important practical generative modeling problem that has seen great progress over the last few years, with likelihood-based autoregressive neural models now outperforming traditional concatenative systems.
Meta Back-translation
google-research/google-research
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ICLR 2021
Back-translation is an effective strategy to improve the performance of Neural Machine Translation~(NMT) by generating pseudo-parallel data.
