activity
20162026
most citedJudging LLM-as-a-Judge with MT-Bench and Chatbot Arena

492 citations · 1.1k across the 52 of their papers we have counts for

collaborators
Showing 2022Show all

7 papers · 1 filter

cs.LG2022★ 3 cited

On Optimizing the Communication of Model Parallelism

Yonghao Zhuang, Hexu Zhao, Lianmin Zheng +6

We study a novel and important communication pattern in large-scale model-parallel deep learning (DL), which we call cross-mesh resharding. This pattern emerges when the two paradi…

cs.NI2022★ 11 cited

Skyplane: Optimizing Transfer Cost and Throughput Using Cloud-Aware Overlays

Paras Jain, Sam Kumar, Sarah Wooders +3

Cloud applications are increasingly distributing data across multiple regions and cloud providers. Unfortunately, wide-area bulk data transfers are often slow, bottlenecking applic…

cs.CV2022

Context-Aware Streaming Perception in Dynamic Environments

Gur-Eyal Sela, Ionel Gog, Justin Wong +9

Efficient vision works maximize accuracy under a latency budget. These works evaluate accuracy offline, one image at a time. However, real-time vision applications like autonomous…

cs.LG2022★ 6 cited

POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging

Shishir G. Patil, Paras Jain, Prabal Dutta +2

Fine-tuning models on edge devices like mobile phones would enable privacy-preserving personalization over sensitive data. However, edge training has historically been limited to r…

cs.DC2022★ 2 cited

NumS: Scalable Array Programming for the Cloud

Melih Elibol, Vinamra Benara, Samyu Yagati +4

Scientists increasingly rely on Python tools to perform scalable distributed memory array operations using rich, NumPy-like expressions. However, many of these tools rely on dynami…

cs.LG2022★ 127 cited

Representing Long-Range Context for Graph Neural Networks with Global Attention

Zhanghao Wu, Paras Jain, Matthew A. Wright +3

Graph neural networks are powerful architectures for structured datasets. However, current methods struggle to represent long-range dependencies. Scaling the depth or width of GNNs…