Jason Yosinski | DeepAI

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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...

0 Maciej Sypetkowski, et al. ∙

∙ 09/16/2021

Language Models are Few-shot Multilingual Learners

General-purpose language models have demonstrated impressive capabilitie...

6 Genta Indra Winata, et al. ∙

∙ 07/16/2021

When does loss-based prioritization fail?

Not all examples are created equal, but standard deep neural network tra...

12 Niel Teng Hu, et al. ∙

∙ 06/26/2020

Supermasks in Superposition

We present the Supermasks in Superposition (SupSup) model, capable of se...

0 Mitchell Wortsman, et al. ∙

∙ 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...

7 Ashley D. Edwards, et al. ∙

∙ 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...

13 Sumanth Dathathri, et al. ∙

∙ 10/18/2019

First-Order Preconditioning via Hypergradient Descent

Standard gradient descent methods are susceptible to a range of issues t...

22 Ted Moskovitz, et al. ∙

∙ 09/03/2019

LCA: Loss Change Allocation for Neural Network Training

Neural networks enjoy widespread use, but many aspects of their training...

6 Janice Lan, et al. ∙

∙ 06/04/2019

Hamiltonian Neural Networks

Even though neural networks enjoy widespread use, they still struggle to...

13 Sam Greydanus, et al. ∙

∙ 05/03/2019

Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask

The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed ...

12 Hattie Zhou, et al. ∙

∙ 04/18/2019

Understanding Neural Networks via Feature Visualization: A survey

A neuroscience method to understanding the brain is to find and study th...

38 Anh Nguyen, et al. ∙

∙ 11/28/2018

Metropolis-Hastings Generative Adversarial Networks

We introduce the Metropolis-Hastings generative adversarial network (MH-...

6 Ryan Turner, et al. ∙

∙ 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...

6 Rosanne Liu, et al. ∙

∙ 04/24/2018

Measuring the Intrinsic Dimension of Objective Landscapes

Many recently trained neural networks employ large numbers of parameters...

0 Chunyuan Li, et al. ∙

∙ 11/27/2017

Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning

This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine ...

0 Andrew Gordon Wilson, et al. ∙

∙ 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...

0 Jojo Yun, et al. ∙

∙ 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...

0 Anh Nguyen, et al. ∙

∙ 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...

0 Anh Nguyen, et al. ∙

∙ 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...

0 Anh Nguyen, et al. ∙

∙ 11/24/2015

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

Recent success in training deep neural networks have prompted active inv...

0 Yixuan Li, et al. ∙

∙ 11/23/2015

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

Deep neural networks with alternating convolutional, max-pooling and dec...

0 Sina Honari, et al. ∙

∙ 06/22/2015

Understanding Neural Networks Through Deep Visualization

Recent years have produced great advances in training large, deep neural...

0 Jason Yosinski, et al. ∙

∙ 03/18/2015

GSNs : Generative Stochastic Networks

We introduce a novel training principle for probabilistic models that is...

0 Guillaume Alain, et al. ∙

∙ 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...

0 Anh Nguyen, et al. ∙

∙ 11/06/2014

How transferable are features in deep neural networks?

Many deep neural networks trained on natural images exhibit a curious ph...

0 Jason Yosinski, et al. ∙

∙ 04/17/2013

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

Interactive evolution has shown the potential to create amazing and comp...

0 Nick Cheney, et al. ∙

∙ 02/20/2012

MAV Stabilization using Machine Learning and Onboard Sensors

In many situations, Miniature Aerial Vehicles (MAVs) are limited to usin...

0 Jason Yosinski, et al. ∙

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