Jason Yosinski

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Uber ∙ Geometric Intelligence Inc. ∙ cornell university

Machine Learning Scientist, founding member of Uber AI Labs

When does loss-based prioritization fail?

Not all examples are created equal, but standard deep neural network tra...
07/16/2021 ∙ by Niel Teng Hu, et al. ∙ 12 ∙ share

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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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Jason Yosinski

Featured Co-authors

Yoshua Bengio

Rui Wang

Thomas Brox

Ali Farhadi

Andrew Gordon Wilson

Jascha Sohl-Dickstein

Chunyuan Li

Pascal Vincent

Andrea Madotto

Anh Nguyen

Jeff Clune

When does loss-based prioritization fail?

Supermasks in Superposition

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

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

First-Order Preconditioning via Hypergradient Descent

LCA: Loss Change Allocation for Neural Network Training

Hamiltonian Neural Networks

Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask

Understanding Neural Networks via Feature Visualization: A survey

Metropolis-Hastings Generative Adversarial Networks

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

Measuring the Intrinsic Dimension of Objective Landscapes

Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning

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

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

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

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

Understanding Neural Networks Through Deep Visualization

GSNs : Generative Stochastic Networks

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

How transferable are features in deep neural networks?

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

MAV Stabilization using Machine Learning and Onboard Sensors