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07/05/2023
Set Learning for Accurate and Calibrated Models
Model overconfidence and poor calibration are common in machine learning...
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05/17/2022
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Molecular dynamics (MD) simulations allow atomistic insights into chemic...
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01/13/2022
GradMax: Growing Neural Networks using Gradient Information
The architecture and the parameters of neural networks are often optimiz...
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08/19/2021
Do Vision Transformers See Like Convolutional Neural Networks?
Convolutional neural networks (CNNs) have so far been the de-facto model...
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05/04/2021
MLP-Mixer: An all-MLP Architecture for Vision
Convolutional Neural Networks (CNNs) are the go-to model for computer vi...
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04/07/2021
Differentiable Patch Selection for Image Recognition
Neural Networks require large amounts of memory and compute to process h...
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03/26/2021
Understanding Robustness of Transformers for Image Classification
Deep Convolutional Neural Networks (CNNs) have long been the architectur...
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10/22/2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
While the Transformer architecture has become the de-facto standard for ...
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02/26/2020
Predicting Neural Network Accuracy from Weights
We study the prediction of the accuracy of a neural network given only i...
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03/07/2019
Interpretable Deep Learning in Drug Discovery
Without any means of interpretation, neural networks that predict molecu...
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12/03/2018
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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06/20/2018
RUDDER: Return Decomposition for Delayed Rewards
We propose a novel reinforcement learning approach for finite Markov dec...
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03/26/2018
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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02/13/2018
First Order Generative Adversarial Networks
GANs excel at learning high dimensional distributions, but they can upda...
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08/29/2017
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
Generative adversarial networks (GANs) evolved into one of the most succ...
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06/26/2017
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Generative Adversarial Networks (GANs) excel at creating realistic image...
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06/08/2017
Self-Normalizing Neural Networks
Deep Learning has revolutionized vision via convolutional neural network...
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11/23/2015
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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03/04/2015
Toxicity Prediction using Deep Learning
Everyday we are exposed to various chemicals via food additives, cleanin...
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02/23/2015