Justin Gilmer

Orcid: 0009-0003-4813-7874

According to our database1, Justin Gilmer authored at least 40 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of three.
  • Erdős number3 of two.

Timeline

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Bibliography

2024
Pre-trained Gaussian Processes for Bayesian Optimization.
J. Mach. Learn. Res., 2024

Small-scale proxies for large-scale Transformer training instabilities.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Replacing softmax with ReLU in Vision Transformers.
CoRR, 2023

Benchmarking Neural Network Training Algorithms.
CoRR, 2023

Order Matters in the Presence of Dataset Imbalance for Multilingual Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Improving Training Stability for Multitask Ranking Models in Recommender Systems.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023


2022
Adaptive Gradient Methods at the Edge of Stability.
CoRR, 2022

Pre-training helps Bayesian optimization too.
CoRR, 2022

AI system for fetal ultrasound in low-resource settings.
CoRR, 2022

Do Current Multi-Task Optimization Methods in Deep Learning Even Help?
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Loss Curvature Perspective on Training Instabilities of Deep Learning Models.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
A Loss Curvature Perspective on Training Instability in Deep Learning.
CoRR, 2021

Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers.
CoRR, 2021

A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes.
CoRR, 2021

The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation.
CoRR, 2019

MNIST-C: A Robustness Benchmark for Computer Vision.
CoRR, 2019

A Fourier Perspective on Model Robustness in Computer Vision.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Adversarial Examples Are a Natural Consequence of Test Error in Noise.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Motivating the Rules of the Game for Adversarial Example Research.
CoRR, 2018

Relational inductive biases, deep learning, and graph networks.
CoRR, 2018

Sanity Checks for Saliency Maps.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV).
Proceedings of the 35th International Conference on Machine Learning, 2018

Adversarial Spheres.
Proceedings of the 6th International Conference on Learning Representations, 2018

Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
A Communication Game Related to the Sensitivity Conjecture.
Theory Comput., 2017

Adversarial Patch.
CoRR, 2017

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement.
CoRR, 2017

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Neural Message Passing for Quantum Chemistry.
Proceedings of the 34th International Conference on Machine Learning, 2017

Input Switched Affine Networks: An RNN Architecture Designed for Interpretability.
Proceedings of the 34th International Conference on Machine Learning, 2017

Deep Information Propagation.
Proceedings of the 5th International Conference on Learning Representations, 2017

Explaining the Learning Dynamics of Direct Feedback Alignment.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
A local central limit theorem for triangles in a random graph.
Random Struct. Algorithms, 2016

Intelligible Language Modeling with Input Switched Affine Networks.
CoRR, 2016

Composition limits and separating examples for some boolean function complexity measures.
Comb., 2016

2015
A New Approach to the Sensitivity Conjecture.
Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science, 2015


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