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01/13/2023
RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
High-throughput screening techniques are commonly used to obtain large q...
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09/16/2021
Language Models are Few-shot Multilingual Learners
General-purpose language models have demonstrated impressive capabilitie...
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07/16/2021
When does loss-based prioritization fail?
Not all examples are created equal, but standard deep neural network tra...
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06/26/2020
Supermasks in Superposition
We present the Supermasks in Superposition (SupSup) model, capable of se...
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02/21/2020
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
In this paper, we introduce a novel form of value function, Q(s, s'), th...
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12/04/2019
Plug and Play Language Models: a Simple Approach to Controlled Text Generation
Large transformer-based language models (LMs) trained on huge text corpo...
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10/18/2019
First-Order Preconditioning via Hypergradient Descent
Standard gradient descent methods are susceptible to a range of issues t...
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09/03/2019
LCA: Loss Change Allocation for Neural Network Training
Neural networks enjoy widespread use, but many aspects of their training...
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06/04/2019
Hamiltonian Neural Networks
Even though neural networks enjoy widespread use, they still struggle to...
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05/03/2019
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed ...
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04/18/2019
Understanding Neural Networks via Feature Visualization: A survey
A neuroscience method to understanding the brain is to find and study th...
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11/28/2018
Metropolis-Hastings Generative Adversarial Networks
We introduce the Metropolis-Hastings generative adversarial network (MH-...
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07/09/2018
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Few ideas have enjoyed as large an impact on deep learning as convolutio...
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04/24/2018
Measuring the Intrinsic Dimension of Objective Landscapes
Many recently trained neural networks employ large numbers of parameters...
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11/27/2017
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning
This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine ...
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06/19/2017
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
We propose a new technique, Singular Vector Canonical Correlation Analys...
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11/30/2016
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Generating high-resolution, photo-realistic images has been a long-stand...
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05/30/2016
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Deep neural networks (DNNs) have demonstrated state-of-the-art results o...
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02/11/2016
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
We can better understand deep neural networks by identifying which featu...
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11/24/2015
Convergent Learning: Do different neural networks learn the same representations?
Recent success in training deep neural networks have prompted active inv...
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11/23/2015
Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Deep neural networks with alternating convolutional, max-pooling and dec...
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06/22/2015
Understanding Neural Networks Through Deep Visualization
Recent years have produced great advances in training large, deep neural...
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03/18/2015
GSNs : Generative Stochastic Networks
We introduce a novel training principle for probabilistic models that is...
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12/05/2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Deep neural networks (DNNs) have recently been achieving state-of-the-ar...
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11/06/2014
How transferable are features in deep neural networks?
Many deep neural networks trained on natural images exhibit a curious ph...
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04/17/2013
Hands-free Evolution of 3D-printable Objects via Eye Tracking
Interactive evolution has shown the potential to create amazing and comp...
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02/20/2012
