
Predicting Neural Network Accuracy from Weights
We study the prediction of the accuracy of a neural network given only i...
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Interpretable Deep Learning in Drug Discovery
Without any means of interpretation, neural networks that predict molecu...
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Towards Accurate Generative Models of Video: A New Metric & Challenges
Recent advances in deep generative models have lead to remarkable progre...
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RUDDER: Return Decomposition for Delayed Rewards
We propose a novel reinforcement learning approach for finite Markov dec...
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Fréchet ChemblNet Distance: A metric for generative models for molecules
The new wave of successful generative models in machine learning has inc...
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First Order Generative Adversarial Networks
GANs excel at learning high dimensional distributions, but they can upda...
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Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
Generative adversarial networks (GANs) evolved into one of the most succ...
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GANs Trained by a Two TimeScale Update Rule Converge to a Local Nash Equilibrium
Generative Adversarial Networks (GANs) excel at creating realistic image...
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SelfNormalizing Neural Networks
Deep Learning has revolutionized vision via convolutional neural network...
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Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
We introduce the "exponential linear unit" (ELU) which speeds up learnin...
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Toxicity Prediction using Deep Learning
Everyday we are exposed to various chemicals via food additives, cleanin...
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Rectified Factor Networks
We propose rectified factor networks (RFNs) to efficiently construct ver...
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Thomas Unterthiner
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