1 citations · 1 across the 11 of their papers we have counts for
11 papers
FRoD: Full-Rank Efficient Fine-Tuning with Rotational Degrees for Fast Convergence
Guoan Wan, Tianyu Chen, Fangzheng Feng +2
Parameter-efficient fine-tuning (PEFT) methods have emerged as a practical solution for adapting large foundation models to downstream tasks, reducing computational and memory cost…
Towards Long-window Anchoring in Vision-Language Model Distillation
Haoyi Zhou, Shuo Li, Tianyu Chen +3
While large vision-language models (VLMs) demonstrate strong long-context understanding, their prevalent small branches fail on linguistics-photography alignment for a limited wind…
Accelerate Scaling of LLM Finetuning via Quantifying the Coverage and Depth of Instruction Set
Chengwei Wu, Li Du, Hanyu Zhao +4
Scaling the amount of data used for supervied fine-tuning(SFT) does not guarantee the proportional gains in model performance, highlighting a critical need to understand what makes…
FreqMoE: Dynamic Frequency Enhancement for Neural PDE Solvers
Tianyu Chen, Haoyi Zhou, Ying Li +5
Fourier Neural Operators (FNO) have emerged as promising solutions for efficiently solving partial differential equations (PDEs) by learning infinite-dimensional function mappings…
Instructing the Architecture Search for Spatial-temporal Sequence Forecasting with LLM
Xin Xue, Haoyi Zhou, Tianyu Chen +3
Spatial-temporal sequence forecasting (STSF) is a long-standing research problem with widespread real-world applications. Neural architecture search (NAS), which automates the neur…
Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding
Tianyu Chen, Xingcheng Fu, Yisen Gao +5
Modern vision-language models (VLMs) develop patch embedding and convolution backbone within vector space, especially Euclidean ones, at the very founding. When expanding VLMs to a…
PINNsAgent: Automated PDE Surrogation with Large Language Models
Qingpo Wuwu, Chonghan Gao, Tianyu Chen +6
Solving partial differential equations (PDEs) using neural methods has been a long-standing scientific and engineering research pursuit. Physics-Informed Neural Networks (PINNs) ha…
Physics-inspired Machine Learning for Quantum Error Mitigation
Xiao-Yue Xu, Xin Xue, Tianyu Chen +5
Noise is a major obstacle in current quantum computing, and Machine Learning for Quantum Error Mitigation (ML-QEM) promises to address this challenge, enhancing computational accur…
Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks
Jiayu Lin, Guanrong Chen, Bojun Jin +26
In this paper we present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023), and introduce the related datasets. We organiz…
OmniArch: Building Foundation Model For Scientific Computing
Tianyu Chen, Haoyi Zhou, Ying Li +5
Foundation models have revolutionized language modeling, while whether this success is replicated in scientific computing remains unexplored. We present OmniArch, the first prototy…
PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization
Ziqi Yuan, Haoyi Zhou, Tianyu Chen +1
A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and spam, have severely disrupted the smooth operation of networks. Due to the inherent sen…