Jason Yosinski

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Uber ∙ Geometric Intelligence Inc. ∙ cornell university

Machine Learning Scientist, founding member of Uber AI Labs

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Jason Yosinski

Featured Co-authors

Yoshua Bengio

Rui Wang

Thomas Brox

Jascha Sohl-Dickstein

Andrew Gordon Wilson

Pascal Vincent

Jeff Clune

Alexey Dosovitskiy

Chunyuan Li

Christopher Pal

Hod Lipson

Understanding Neural Networks via Feature Visualization: A survey

First-Order Preconditioning via Hypergradient Descent

Hamiltonian Neural Networks

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

Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

Metropolis-Hastings Generative Adversarial Networks

LCA: Loss Change Allocation for Neural Network Training

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

GSNs : Generative Stochastic Networks

How transferable are features in deep neural networks?

Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning

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

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

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

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

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks

MAV Stabilization using Machine Learning and Onboard Sensors

Understanding Neural Networks Through Deep Visualization

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

Measuring the Intrinsic Dimension of Objective Landscapes