718 citations · 1.7k across the 44 of their papers we have counts for
7 papers · 1 filter
SAMN: A Sample Attention Memory Network Combining SVM and NN in One Architecture
Qiaoling Yang, Linkai Luo, Haoyu Zhang +2
Support vector machine (SVM) and neural networks (NN) have strong complementarity. SVM focuses on the inner operation among samples while NN focuses on the operation among the feat…
Efficient Memory Management for Large Language Model Serving with PagedAttention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang +6
High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV c…
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
Mingyue Cheng, Xiaoyu Tao, Zhiding Liu +4
Learning transferable representations from unlabeled time series is crucial for improving performance in data-scarce classification. Existing self-supervised methods often operate…
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…
MPCFormer: fast, performant and private Transformer inference with MPC
Dacheng Li, Rulin Shao, Hongyi Wang +3
Enabling private inference is crucial for many cloud inference services that are based on Transformer models. However, existing private inference solutions can increase the inferen…
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness
Dacheng Li, Hongyi Wang, Eric Xing +1
Scaling up model sizes can lead to fundamentally new capabilities in many machine learning (ML) tasks. However, training big models requires strong distributed system expertise to…
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye +12
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and im…