Brian R. Bartoldson

According to our database, Brian R. Bartoldson authored at least 13 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion.

Jacob K. Christopher, Brian R. Bartoldson, Bhavya Kailkhura, Ferdinando Fioretto

CoRR, 2024

ELFS: Enhancing Label-Free Coreset Selection via Clustering-based Pseudo-Labeling.

Haizhong Zheng, Elisa Tsai, Yifu Lu, Jiachen Sun, Brian R. Bartoldson, Bhavya Kailkhura, Atul Prakash

CoRR, 2024

Transformers Can Do Arithmetic with the Right Embeddings.

Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein

CoRR, 2024

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression.

Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian R. Bartoldson, Ajay Kumar Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies.

Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura

Proceedings of the Forty-first International Conference on Machine Learning, 2024

NEFTune: Noisy Embeddings Improve Instruction Finetuning.

Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities.

Brian R. Bartoldson, Bhavya Kailkhura, Davis W. Blalock

J. Mach. Learn. Res., 2023

Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics.

Brian R. Bartoldson, Yeping Hu, Amar Saini, Jose Cadena, Yucheng Fu, Jie Bao, Zhijie Xu, Brenda Ng, Phan Nguyen

CoRR, 2023

2022

Models Out of Line: A Fourier Lens on Distribution Shift Robustness.

Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Timo Bremer

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Latent Space Simulation for Carbon Capture Design Optimization.

Brian R. Bartoldson, Rui Wang, Yucheng Fu, David Widemann, Sam Nguyen, Jie Bao, Zhijie Xu, Brenda Ng

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness.

James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

The Generalization-Stability Tradeoff In Neural Network Pruning.

Brian R. Bartoldson, Ari S. Morcos, Adrian Barbu, Gordon Erlebacher

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2018

Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks.

Brian R. Bartoldson, Adrian Barbu, Gordon Erlebacher

CoRR, 2018