Jason Yosinski

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Uber ∙ Geometric Intelligence Inc. ∙ cornell university

Machine Learning Scientist, founding member of Uber AI Labs

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Supermasks in Superposition

We present the Supermasks in Superposition (SupSup) model, capable of se...
06/26/2020 ∙ by Mitchell Wortsman, et al. ∙ 0 ∙ share

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Estimating Q(s,s') with Deep Deterministic Dynamics Gradients

In this paper, we introduce a novel form of value function, Q(s, s'), th...
02/21/2020 ∙ by Ashley D. Edwards, et al. ∙ 7 ∙ share

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Plug and Play Language Models: a Simple Approach to Controlled Text Generation

Large transformer-based language models (LMs) trained on huge text corpo...
12/04/2019 ∙ by Sumanth Dathathri, et al. ∙ 13 ∙ share

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First-Order Preconditioning via Hypergradient Descent

Standard gradient descent methods are susceptible to a range of issues t...
10/18/2019 ∙ by Ted Moskovitz, et al. ∙ 22 ∙ share

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LCA: Loss Change Allocation for Neural Network Training

Neural networks enjoy widespread use, but many aspects of their training...
09/03/2019 ∙ by Janice Lan, et al. ∙ 6 ∙ share

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Hamiltonian Neural Networks

Even though neural networks enjoy widespread use, they still struggle to...
06/04/2019 ∙ by Sam Greydanus, et al. ∙ 13 ∙ share

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Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask

The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed ...
05/03/2019 ∙ by Hattie Zhou, et al. ∙ 12 ∙ share

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Understanding Neural Networks via Feature Visualization: A survey

A neuroscience method to understanding the brain is to find and study th...
04/18/2019 ∙ by Anh Nguyen, et al. ∙ 38 ∙ share

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Metropolis-Hastings Generative Adversarial Networks

We introduce the Metropolis-Hastings generative adversarial network (MH-...
11/28/2018 ∙ by Ryan Turner, et al. ∙ 6 ∙ share

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An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

Few ideas have enjoyed as large an impact on deep learning as convolutio...
07/09/2018 ∙ by Rosanne Liu, et al. ∙ 6 ∙ share

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Measuring the Intrinsic Dimension of Objective Landscapes

Many recently trained neural networks employ large numbers of parameters...
04/24/2018 ∙ by Chunyuan Li, et al. ∙ 0 ∙ share

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Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning

This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine ...
11/27/2017 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ share

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SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability

We propose a new technique, Singular Vector Canonical Correlation Analys...
06/19/2017 ∙ by Maithra Raghu, et al. ∙ 0 ∙ share

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Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

Generating high-resolution, photo-realistic images has been a long-stand...
11/30/2016 ∙ by Anh Nguyen, et al. ∙ 0 ∙ share

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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...
05/30/2016 ∙ by Anh Nguyen, et al. ∙ 0 ∙ share

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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...
02/11/2016 ∙ by Anh Nguyen, et al. ∙ 0 ∙ share

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Convergent Learning: Do different neural networks learn the same representations?

Recent success in training deep neural networks have prompted active inv...
11/24/2015 ∙ by Yixuan Li, et al. ∙ 0 ∙ share

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Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

Deep neural networks with alternating convolutional, max-pooling and dec...
11/23/2015 ∙ by Sina Honari, et al. ∙ 0 ∙ share

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Understanding Neural Networks Through Deep Visualization

Recent years have produced great advances in training large, deep neural...
06/22/2015 ∙ by Jason Yosinski, et al. ∙ 0 ∙ share

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GSNs : Generative Stochastic Networks

We introduce a novel training principle for probabilistic models that is...
03/18/2015 ∙ by Guillaume Alain, et al. ∙ 0 ∙ share

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Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

Deep neural networks (DNNs) have recently been achieving state-of-the-ar...
12/05/2014 ∙ by Anh Nguyen, et al. ∙ 0 ∙ share

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How transferable are features in deep neural networks?

Many deep neural networks trained on natural images exhibit a curious ph...
11/06/2014 ∙ by Jason Yosinski, et al. ∙ 0 ∙ share

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Hands-free Evolution of 3D-printable Objects via Eye Tracking

Interactive evolution has shown the potential to create amazing and comp...
04/17/2013 ∙ by Nick Cheney, et al. ∙ 0 ∙ share

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MAV Stabilization using Machine Learning and Onboard Sensors

In many situations, Miniature Aerial Vehicles (MAVs) are limited to usin...
02/20/2012 ∙ by Jason Yosinski, et al. ∙ 0 ∙ share

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