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Jason Yosinski

18 papers · 2014–2021 · 5 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (7) πŸ—ΊοΈ Taxonomy Completionist (49) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (13) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (5) 🌟 Keyword Trendsetter Combo (6) 🧬 Topic Evolution πŸ‘‘ Triple Crown πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (82) πŸ”₯ Unstoppable (8) ❓ The Questioner πŸ’Ž Century Club (18)

Conferences

NIPS (9) CVPR (3) ICML (3) ICLR (2) EMNLP (1)

Top co-authors

Rosanne Liu (9) Jeff Clune (4) Jane Hung (3) Eric Frank (3) Anh Nguyen (3) Janice Lan (3) Yoshua Bengio (3) Piero Molino (2) Andrea Madotto (2) Alexey Dosovitskiy (2)

Keywords

convolutional neural network (4) image generation (3) neural network (3) neural network pruning (2) image classification (2) activation maximization (2) representation learning (2) gradient-based optimization (1) transfer learning (1) continual learning (1) few-shot learning (1) neural network training (1) lottery ticket hypothesis (1) object detection (1) cross-lingual transfer (1) feature learning (1) in-context learning (1) conditional generation (1) neural network interpretability (1) multilingual nlp (1)

Papers

Language Models are Few-shot Multilingual Learners EMNLP 2021
Supermasks in Superposition NIPS 2020
Plug and Play Language Models: A Simple Approach to Controlled Text Generation ICLR 2020
Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients ICML 2020
LCA: Loss Change Allocation for Neural Network Training NIPS 2019
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask NIPS 2019
Hamiltonian Neural Networks NIPS 2019
Metropolis-Hastings Generative Adversarial Networks ICML 2019
Faster Neural Networks Straight from JPEG NIPS 2018
An intriguing failing of convolutional neural networks and the CoordConv solution NIPS 2018
Measuring the Intrinsic Dimension of Objective Landscapes ICLR 2018
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space CVPR 2017
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability NIPS 2017
Recombinator Networks: Learning Coarse-To-Fine Feature Aggregation CVPR 2016
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks NIPS 2016
Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images CVPR 2015
Deep Generative Stochastic Networks Trainable by Backprop ICML 2014
How transferable are features in deep neural networks? NIPS 2014