activity
20192026
most citedLiveGraph: A Transactional Graph Storage System with Purely Sequential Adjacency List Scans

70 citations · 78 across the 5 of their papers we have counts for

collaborators

7 papers

cs.SD2026

Real-Time Language Model Jamming: A Case Study for Live Music Accompaniment Generation

Bowen Zheng, Andrew H. Yang, Jiaqi Ruan +5

Language models (LMs) have become one of the most prominent paradigms in modern generative modeling. While making them faster has been the main focus of real-time deployment, speed…

math.OC2025

RIDGECUT: Learning Graph Partitioning with Rings and Wedges

Qize Jiang, Angelo Zangari, Linsey Pang +7

Reinforcement learning (RL) has shown promise for combinatorial optimization problems on graphs by learning heuristics that generalize across instances. However, effectively incorp…

cs.IR2025

HCT-QA: A Benchmark for Question Answering on Human-Centric Tables

Mohammad S. Ahmad, Zan A. Naeem, Michaël Aupetit +6

Tabular data embedded in PDF files, web pages, and other types of documents is prevalent in various domains. These tables, which we call human-centric tables (HCTs for short), are…

cs.CL2025★ 5 cited

Fanar: An Arabic-Centric Multimodal Generative AI Platform

Fanar Team, Ummar Abbas, Mohammad Shahmeer Ahmad +39

We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star an…

cs.DC2024

Hiding Communication Cost in Distributed LLM Training via Micro-batch Co-execution

Haiquan Wang, Chaoyi Ruan, Jia He +4

The growth of Large Language Models (LLMs) has necessitated large-scale distributed training. Highly optimized frameworks, however, still suffer significant losses in Model FLOPS u…

cs.LG2023★ 3 cited

Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks

Ping Gong, Yuxin Ma, Cheng Li +2

In this paper, we primarily focus on understanding the data preprocessing pipeline for DNN Training in the public cloud. First, we run experiments to test the performance implicati…

cs.DB2019★ 70 cited

LiveGraph: A Transactional Graph Storage System with Purely Sequential Adjacency List Scans

Xiaowei Zhu, Guanyu Feng, Marco Serafini +5

The specific characteristics of graph workloads make it hard to design a one-size-fits-all graph storage system. Systems that support transactional updates use data structures with…