7 papers
Construting Reverse Thinking: Developing Large Language Models' Reverse Thingking Ability
Xin Liu, Yunhai Li, Chunfu Jia +2
When facing complex problems, humans tend to try various ideas for different issues. Human thinking patterns exhibit remarkable flexibility in adapting to diverse scenarios. GPT-o1…
FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System
Yongsen Zheng, Ziliang Chen, Jinghui Qin +1
The filter bubble is a notorious issue in Recommender Systems (RSs), which describes the phenomenon whereby users are exposed to a limited and narrow range of information or conten…
A Retrospect to Multi-prompt Learning across Vision and Language
Ziliang Chen, Xin Huang, Quanlong Guan +2
The vision community is undergoing the unprecedented progress with the emergence of Vision-Language Pretraining Models (VLMs). Prompt learning plays as the holy grail of accessing…
An Exhaustive DPLL Approach to Model Counting over Integer Linear Constraints with Simplification Techniques
Mingwei Zhang, Zhenhao Gu, Liangda Fang +4
Linear constraints are one of the most fundamental constraints in fields such as computer science, operations research and optimization. Many applications reduce to the task of mod…
De-singularity Subgradient for the -th-Powered -Norm Weber Location Problem
Zhao-Rong Lai, Xiaotian Wu, Liangda Fang +2
The Weber location problem is widely used in several artificial intelligence scenarios. However, the gradient of the objective does not exist at a considerable set of singular poin…
A De-singularity Subgradient Approach for the Extended Weber Location Problem
Zhao-Rong Lai, Xiaotian Wu, Liangda Fang +1
The extended Weber location problem is a classical optimization problem that has inspired some new works in several machine learning scenarios recently. However, most existing algo…
Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach
Ziliang Chen, Yongsen Zheng, Zhao-Rong Lai +2
Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels de-confounded from the environments, advancing the technical roadmap of o…