23 citations · 92 across the 11 of their papers we have counts for
11 papers
Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media
Hongzhi Qi, Qing Zhao, Jianqiang Li +9
On social media, users often express their personal feelings, which may exhibit cognitive distortions or even suicidal tendencies on certain specific topics. Early recognition of t…
Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals
Guanghui Fu, Qing Zhao, Jianqiang Li +9
In the contemporary landscape of social media, an alarming number of users express negative emotions, some of which manifest as strong suicidal intentions. This situation underscor…
LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models
Peng Xu, Wenqi Shao, Kaipeng Zhang +7
Large Vision-Language Models (LVLMs) have recently played a dominant role in multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation of their…
Absence of logarithmic and algebraic scaling entanglement phases due to skin effect
Xu Feng, Shuo Liu, Shu Chen +1
Measurement-induced phase transition in the presence of competition between projective measurement and random unitary evolution has attracted increasing attention due to the rich p…
Impact of Redundancy on Resilience in Distributed Optimization and Learning
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
This report considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, w…
Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection
Jianshu Li, Man Luo, Jian Liu +12
In this paper, we present the Multi-Forgery Detection Challenge held concurrently with the IEEE Computer Society Workshop on Biometrics at CVPR 2022. Our Multi-Forgery Detection Ch…
Byzantine Fault-Tolerant Min-Max Optimization
Shuo Liu, Nitin Vaidya
In this paper, we consider a min-max optimization problem under adversarial manipulation, where there are cost functions, up to of which may be replaced by arbitrary faulty…
A Survey on Fault-tolerance in Distributed Optimization and Machine Learning
Shuo Liu
The robustness of distributed optimization is an emerging field of study, motivated by various applications of distributed optimization including distributed machine learning, dist…
Asynchronous Distributed Optimization with Redundancy in Cost Functions
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
This paper considers the problem of asynchronous distributed multi-agent optimization on server-based system architecture. In this problem, each agent has a local cost, and the goa…
Approximate Byzantine Fault-Tolerance in Distributed Optimization
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
This paper considers the problem of Byzantine fault-tolerance in distributed multi-agent optimization. In this problem, each agent has a local cost function, and in the fault-free…
Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE)
Nirupam Gupta, Shuo Liu, Nitin H. Vaidya
This paper considers the Byzantine fault-tolerance problem in distributed stochastic gradient descent (D-SGD) method - a popular algorithm for distributed multi-agent machine learn…