James Diffenderfer

According to our database, James Diffenderfer authored at least 22 papers between 2014 and 2024.

Collaborative distances:

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

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 Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li

CoRR, 2024

GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations.

Jinhao Duan, Renming Zhang, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, Kaidi Xu

CoRR, 2024

2023

Approximate High-Performance Computing: A Fast and Energy-Efficient Computing Paradigm in the Post-Moore Era.

Harshitha Menon, James Diffenderfer, Giorgis Georgakoudis, Ignacio Laguna, Michael O. Lam, Daniel Osei-Kuffuor, Konstantinos Parasyris, Jackson Vanover

IT Prof., 2023

When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks.

Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura, Peter A. Beerel

CoRR, 2023

DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training.

Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Jiancheng Liu, Konstantinos Parasyris, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu

CoRR, 2023

Neural Image Compression: Generalization, Robustness, and Spectral Biases.

Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

A Framework for Error-Bounded Approximate Computing, with an Application to Dot Products.

James Diffenderfer, Daniel Osei-Kuffuor, Harshitha Menon

SIAM J. Sci. Comput., 2022

Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed.

Hao Cheng, Pu Zhao, Yize Li, Xue Lin, James Diffenderfer, Ryan A. Goldhahn, Bhavya Kailkhura

CoRR, 2022

"Understanding Robustness Lottery": A Comparative Visual Analysis of Neural Network Pruning Approaches.

Zhimin Li, Shusen Liu, Xin Yu, Bhavya Kailkhura, Jie Cao, James Daniel Diffenderfer, Peer-Timo Bremer, Valerio Pascucci

CoRR, 2022

Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning.

Ioannis C. Tsaknakis, Bhavya Kailkhura, Sijia Liu, Donald Loveland, James Diffenderfer, Anna Maria Hiszpanski, Mingyi Hong

CoRR, 2022

Approximate Computing Through the Lens of Uncertainty Quantification.

Konstantinos Parasyris, James Diffenderfer, Harshitha Menon, Ignacio Laguna, Jackson Vanover, Ryan Vogt, Daniel Osei-Kuffuor

Proceedings of the SC22: International Conference for High Performance Computing, 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

Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices.

Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale

Proceedings of the International IEEE Symposium on Performance Analysis of Systems and Software, 2022

Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study.

Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale

Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition, 2022

2021

QDOT: Quantized Dot Product Kernel for Approximate High-Performance Computing.

James Diffenderfer, Daniel Osei-Kuffuor, Harshitha Menon

CoRR, 2021

HPAC: evaluating approximate computing techniques on HPC OpenMP applications.

Konstantinos Parasyris, Giorgis Georgakoudis, Harshitha Menon, James Diffenderfer, Ignacio Laguna, Daniel Osei-Kuffuor, Markus Schordan

Proceedings of the International Conference for High Performance Computing, 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

Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network.

James Diffenderfer, Bhavya Kailkhura

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Stability Analysis of Inline ZFP Compression for Floating-Point Data in Iterative Methods.

Alyson Fox, James Diffenderfer, Jeffrey Hittinger, Geoffrey Sanders, Peter Lindstrom

SIAM J. Sci. Comput., 2020

2019

Error Analysis of ZFP Compression for Floating-Point Data.

James Diffenderfer, Alyson Fox, Jeffrey A. F. Hittinger, Geoffrey Sanders, Peter G. Lindstrom

SIAM J. Sci. Comput., 2019

2017

Construction of a full row-rank matrix system for multiple scanning directions in discrete tomography.

Xiezhang Li, James Diffenderfer, Jiehua Zhu

J. Comput. Appl. Math., 2017

2014

Building a Better Bijection Between Classes of Compositions.

James Daniel Diffenderfer

Integers, 2014