1 citations · 1 across the 3 of their papers we have counts for
5 papers
Where Do the Joules Go? Diagnosing Inference Energy Consumption
Jae-Won Chung, Ruofan Wu, Jeff J. Ma +1
Energy is now a critical ML computing resource. While measuring energy consumption and observing trends is a valuable first step, accurately understanding and diagnosing why those…
Sphinx: Efficiently Serving Novel View Synthesis using Regression-Guided Selective Refinement
Yuchen Xia, Souvik Kundu, Mosharaf Chowdhury +1
Novel View Synthesis (NVS) is the task of generating new images of a scene from viewpoints that were not part of the original input. Diffusion-based NVS can generate high-quality,…
EXP-Bench: Can AI Conduct AI Research Experiments?
Patrick Tser Jern Kon, Jiachen Liu, Xinyi Zhu +10
Automating AI research holds immense potential for accelerating scientific progress, yet current AI agents struggle with the complexities of rigorous, end-to-end experimentation. W…
The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization
Jae-Won Chung, Jeff J. Ma, Ruofan Wu +5
As the adoption of Generative AI in real-world services grow explosively, energy has emerged as a critical bottleneck resource. However, energy remains a metric that is often overl…
Curie: Toward Rigorous and Automated Scientific Experimentation with AI Agents
Patrick Tser Jern Kon, Jiachen Liu, Qiuyi Ding +7
Scientific experimentation, a cornerstone of human progress, demands rigor in reliability, methodical control, and interpretability to yield meaningful results. Despite the growing…