Justin Gilmer
16 papers · 2017–2024 · 6 venues · across top CS/AI venues
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Achievements
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Research topics
Machine Learning
(14)
Optimization & Theory Β· Machine Learning
(10)
Deep Learning
(8)
Artificial Intelligence
(5)
Application Areas
(4)
Techniques
(4)
Architectures
(3)
Core AI
(3)
Learning Types Β· Machine Learning
(3)
Computer Vision
(2)
Analysis
(2)
Learning Paradigms
(2)
Learning Types Β· Deep Learning
(2)
Optimization & Theory Β· Deep Learning
(2)
Computer Science
(1)
Keywords
neural network
(3)
multi-task learning
(3)
data augmentation
(3)
image classification
(3)
deep learning
(2)
distribution shift
(2)
learning rate
(2)
training instability
(2)
hyperparameter tuning
(2)
black-box optimization
(1)
neural machine translation
(1)
kl divergence
(1)
supervised learning
(1)
empirical study
(1)
adversarial robustness
(1)
batch normalization
(1)
feature attribution
(1)
feature importance
(1)
model robustness
(1)
adversarial training
(1)
Papers
Pre-trained Gaussian Processes for Bayesian Optimization
JMLR 2024
Small-scale proxies for large-scale Transformer training instabilities
ICLR 2024
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
NeurIPS 2023
Scaling Vision Transformers to 22 Billion Parameters
ICML 2023
A Loss Curvature Perspective on Training Instabilities of Deep Learning Models
ICLR 2022
Do Current Multi-Task Optimization Methods in Deep Learning Even Help?
NeurIPS 2022
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
AISTATS 2022
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
ICCV 2021
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
ICLR 2020
Adversarial Examples Are a Natural Consequence of Test Error in Noise
ICML 2019
A Fourier Perspective on Model Robustness in Computer Vision
NeurIPS 2019
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
ICML 2018
Sanity Checks for Saliency Maps
NeurIPS 2018
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
ICML 2017
Neural Message Passing for Quantum Chemistry
ICML 2017
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
NeurIPS 2017