565 citations · 604 across the 10 of their papers we have counts for
10 papers
Jet-RL: Enabling On-Policy FP8 Reinforcement Learning with Unified Training and Rollout Precision Flow
Haocheng Xi, Charlie Ruan, Peiyuan Liao +7
Reinforcement learning (RL) is essential for enhancing the complex reasoning capabilities of large language models (LLMs). However, existing RL training pipelines are computational…
Towards Anonymous Neural Network Inference
Liao Peiyuan
We introduce funion, a system providing end-to-end sender-receiver unlinkability for neural network inference. By leveraging the Pigeonhole storage protocol and BACAP (blinding-and…
A Systematic Evaluation of On-Device LLMs: Quantization, Performance, and Resources
Qingyu Song, Rui Liu, Wei Lin +11
Deploying Large Language Models (LLMs) on edge devices enhances privacy but faces performance hurdles due to limited resources. We introduce a systematic methodology to evaluate on…
GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism
Byungsoo Jeon, Mengdi Wu, Shiyi Cao +11
Deep neural networks (DNNs) continue to grow rapidly in size, making them infeasible to train on a single device. Pipeline parallelism is commonly used in existing DNN systems to s…
The ArtBench Dataset: Benchmarking Generative Models with Artworks
Peiyuan Liao, Xiuyu Li, Xihui Liu +1
We introduce ArtBench-10, the first class-balanced, high-quality, cleanly annotated, and standardized dataset for benchmarking artwork generation. It comprises 60,000 images of art…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Collage: Seamless Integration of Deep Learning Backends with Automatic Placement
Byungsoo Jeon, Sunghyun Park, Peiyuan Liao +3
The strong demand for efficient and performant deployment of Deep Learning (DL) applications prompts the rapid development of a rich DL ecosystem. To keep up with this fast advance…
Integrating Auxiliary Information in Self-supervised Learning
Yao-Hung Hubert Tsai, Tianqin Li, Weixin Liu +3
This paper presents to integrate the auxiliary information (e.g., additional attributes for data such as the hashtags for Instagram images) in the self-supervised learning process.…
Google Landmark Recognition 2020 Competition Third Place Solution
Qishen Ha, Bo Liu, Fuxu Liu +1
We present our third place solution to the Google Landmark Recognition 2020 competition. It is an ensemble of global features only Sub-center ArcFace models. We introduce dynamic m…
Information Obfuscation of Graph Neural Networks
Peiyuan Liao, Han Zhao, Keyulu Xu +4
While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representation learning in many applications, the neighborhood aggregation scheme exposes addit…