1 code implementation • 8 May 2024 • Dawei Li, Shu Yang, Zhen Tan, Jae Young Baik, Sunkwon Yun, Joseph Lee, Aaron Chacko, BoJian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen
With a synergized framework of LLM and KG mutually enhancing each other, we first leverage LLM to construct an evolving AD-specific knowledge graph (KG) sourced from AD-related scientific literature, and then we utilize a coarse-to-fine sampling method with a novel self-aware knowledge retrieval approach to select appropriate knowledge from the KG to augment LLM inference capabilities.
no code implementations • 7 May 2024 • Junchao Wu, Runzhe Zhan, Derek F. Wong, Shu Yang, Xuebo Liu, Lidia S. Chao, Min Zhang
The efficacy of an large language model (LLM) generated text detector depends substantially on the availability of sizable training data.
1 code implementation • 29 Apr 2024 • Rui Xu, Shu Yang, Yihui Wang, Bo Du, Hao Chen
To help keep pace with the rapid advancements in computer vision, this paper aims to provide a comprehensive review of visual Mamba approaches.
no code implementations • 23 Apr 2024 • Sunan He, Yuxiang Nie, Zhixuan Chen, Zhiyuan Cai, Hongmei Wang, Shu Yang, Hao Chen
The rapid advancement of large-scale vision-language models has showcased remarkable capabilities across various tasks.
no code implementations • 30 Mar 2024 • Muhammad Asif Ali, ZhengPing Li, Shu Yang, Keyuan Cheng, Yang Cao, Tianhao Huang, Lijie Hu, Lu Yu, Di Wang
Large language models (LLMs) have shown exceptional abilities for multiple different natural language processing tasks.
no code implementations • 30 Mar 2024 • Shu Yang, Jiayuan Su, Han Jiang, Mengdi Li, Keyuan Cheng, Muhammad Asif Ali, Lijie Hu, Di Wang
With the rise of large language models (LLMs), ensuring they embody the principles of being helpful, honest, and harmless (3H), known as Human Alignment, becomes crucial.
1 code implementation • 11 Mar 2024 • Shu Yang, Yihui Wang, Hao Chen
Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract discriminative feature representations within Whole Slide Images (WSIs) in computational pathology.
no code implementations • 18 Feb 2024 • Shu Yang, Hanzhi Ma, Chengting Yu, Aili Wang, Er-Ping Li
In this paper, we explore a novel diffusion model architecture within spiking neural networks.
no code implementations • 17 Feb 2024 • Shu Yang, Muhammad Asif Ali, Cheng-Long Wang, Lijie Hu, Di Wang
Adapting large language models (LLMs) to new domains/tasks and enabling them to be efficient lifelong learners is a pivotal challenge.
no code implementations • 17 Feb 2024 • Shu Yang, Muhammad Asif Ali, Lu Yu, Lijie Hu, Di Wang
The increasing significance of large models and their multi-modal variants in societal information processing has ignited debates on social safety and ethics.
no code implementations • 6 Feb 2024 • Xiaojun Mao, Hengfang Wang, Zhonglei Wang, Shu Yang
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods.
1 code implementation • 23 Oct 2023 • Junchao Wu, Shu Yang, Runzhe Zhan, Yulin Yuan, Derek F. Wong, Lidia S. Chao
In this survey, we collate recent research breakthroughs in this area and underscore the pressing need to bolster detector research.
1 code implementation • 10 Oct 2023 • Pan Zhao, Antoine Chambaz, Julie Josse, Shu Yang
Policy learning utilizing observational data is pivotal across various domains, with the objective of learning the optimal treatment assignment policy while adhering to specific constraints such as fairness, budget, and simplicity.
no code implementations • 4 Apr 2023 • Tao Fang, Shu Yang, Kaixin Lan, Derek F. Wong, Jinpeng Hu, Lidia S. Chao, Yue Zhang
To showcase its capabilities in GEC, we design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT.
1 code implementation • 13 Jan 2023 • Pan Zhao, Julie Josse, Shu Yang
We present an efficient and robust transfer learning framework for estimating the optimal ITR with right-censored survival data that generalizes well to the target population.
no code implementations • 3 Nov 2022 • Steven G. Xu, Shu Yang, Brian J. Reich
We adopt a semiparametric conditional distribution regression model that allows inference on any functionals of counterfactual distributions, including PDFs and multiple QTEs.
no code implementations • 26 Sep 2022 • Chenyin Gao, Shu Yang, Anru R. Zhang
With the proposed design, we are able to characterize our denoiser with fewer parameters and train it based on a single image, which considerably improves the model generalizability and reduces the cost of data acquisition.
1 code implementation • 23 Sep 2022 • Boyuan Feng, Tianqi Tang, yuke wang, Zhaodong Chen, Zheng Wang, Shu Yang, Yuan Xie, Yufei Ding
In this paper, we propose Faith, an efficient framework for transformer verification on GPUs.
no code implementations • 24 May 2022 • Jialiang Wang, Haotian Wei, Yi Wang, Shu Yang, Chi Li
Human activity recognition (HAR) based on multimodal sensors has become a rapidly growing branch of biometric recognition and artificial intelligence.
1 code implementation • 23 Apr 2022 • Xiaoqing Tan, Shu Yang, Wenyu Ye, Douglas E. Faries, Ilya Lipkovich, Zbigniew Kadziola
Recently, various doubly robust methods have been proposed for average treatment effect estimation by combining the treatment model and the outcome model via different vehicles, such as matching, weighting, and regression.
no code implementations • 17 Jan 2022 • Jianing Chu, Wenbin Lu, Shu Yang
We consider the problem of treatment regime estimation when the source and target populations may be heterogeneous, individual-level data is available from the source population, and only the summary information of covariates, such as moments, is accessible from the target population.
1 code implementation • ICCV 2021 • Shu Yang, Lu Zhang, Jinqing Qi, Huchuan Lu, Shuo Wang, Xiaoxing Zhang
How to make the appearance and motion information interact effectively to accommodate complex scenarios is a fundamental issue in flow-based zero-shot video object segmentation.
Semantic Segmentation Unsupervised Video Object Segmentation +2
no code implementations • 22 Dec 2020 • Yawen Guan, Garritt L. Page, Brian J Reich, Massimo Ventrucci, Shu Yang
We show that this assumption in the spectral domain is equivalent to adjusting for global-scale confounding in the spatial domain by adding a spatially smoothed version of the treatment variable to the mean of the response variable.
Methodology
no code implementations • 26 Oct 2020 • Xiaojun Chen, Shu Yang, Li Shen, Xuanrong Pang
In this paper, we propose a {distributed GANs training algorithm with quantized gradient, dubbed DQGAN,} which is the first distributed training method with quantized gradient for GANs.
no code implementations • 9 Jul 2020 • Boyuan Feng, yuke wang, Xu Li, Shu Yang, Xueqiao Peng, Yufei Ding
With the increasing popularity of graph-based learning, Graph Neural Networks (GNNs) win lots of attention from the research and industry field because of their high accuracy.
1 code implementation • 16 Jan 2020 • Shu Yang, Yunshu Zhang
Propensity score matching has a long tradition for handling confounding in causal inference.
Methodology
no code implementations • 31 Jul 2019 • Dehan Kong, Shu Yang, Linbo Wang
Unobserved confounding presents a major threat to causal inference in observational studies.
Methodology
no code implementations • ICLR 2019 • Boyuan Feng, Kun Wan, Shu Yang, Yufei Ding
Convolutional Neural Networks (CNNs) have achieved tremendous success for many computer vision tasks, which shows a promising perspective of deploying CNNs on mobile platforms.
no code implementations • ICLR 2019 • Kun Wan, Boyuan Feng, Shu Yang, Yufei Ding
In this paper, we are the first in the field to consider how to craft an effective sparse kernel design by eliminating the large design space.
1 code implementation • 28 Sep 2018 • Lingwei Xie, Song He, Shu Yang, Boyuan Feng, Kun Wan, Zhongnan Zhang, Xiaochen Bo, Yufei Ding
In this paper, we propose a novel domain-adversarial multi-task framework for integrating shared knowledge from multiple domains.
1 code implementation • 20 Aug 2018 • Shu Yang, Karen Pieper, Frank Cools
We propose a class of continuous-time structural failure time models and semiparametric estimators, which do not restrict to regularly spaced data.
Methodology