Justin Gilmer
16 papers · 2017–2024 · 6 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Cross-Pollinator (9) π Conference Polyglot (6) π Academic Marathon (7) π Renaissance Researcher (6)
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Interdisciplinary Bridge
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(26)
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Mega-Team
(42)
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Topic Evolution
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Century Club
(16)
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The Questioner
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Keyword Collector
(63)
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Unstoppable
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Conferences
ICML (5)
NIPS (5)
ICLR (3)
AISTATS (1)
ICCV (1)
JMLR (1)
Top co-authors
Keywords
multi-task learning
(3)
neural network
(3)
deep learning
(2)
image classification
(2)
hyperparameter tuning
(2)
data augmentation
(2)
black-box optimization
(1)
neural machine translation
(1)
adversarial robustness
(1)
corruption robustness
(1)
kl divergence
(1)
supervised learning
(1)
empirical study
(1)
explainable ai
(1)
feature attribution
(1)
model robustness
(1)
feature importance
(1)
adversarial training
(1)
computer vision
(1)
vision transformer
(1)
Papers
Small-scale proxies for large-scale Transformer training instabilities
ICLR 2024
Pre-trained Gaussian Processes for Bayesian Optimization
JMLR 2024
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
NIPS 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?
NIPS 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
NIPS 2019
Sanity Checks for Saliency Maps
NIPS 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
ICML 2018
Neural Message Passing for Quantum Chemistry
ICML 2017
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
ICML 2017
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
NIPS 2017