Publications (21)
Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation
Jiazhi Xu, Sheng Huang, Fengtao Zhou +3
Multi-Label Image Classification (MLIC) approaches usually exploit label correlations to achieve good performance. However, emphasizing correlation like co-occurrence may overlook…
Spread-gram: A spreading-activation schema of network structural learning
Jie Bai, Linjing Li, Daniel Zeng
Network representation learning has exploded recently. However, existing studies usually reconstruct networks as sequences or matrices, which may cause information bias or sparsity…
Wasserstein Diversity-Enriched Regularizer for Hierarchical Reinforcement Learning
Haorui Li, Jiaqi Liang, Linjing Li +1
Hierarchical reinforcement learning composites subpolicies in different hierarchies to accomplish complex tasks.Automated subpolicies discovery, which does not depend on domain kno…
How Search Engine Advertising Affects Sales over Time: An Empirical Investigation
Yanwu Yang, Kang Zhao, Daniel Zeng +1
As a mainstream marketing channel on the Internet, Search Engine Advertising (SEA) has a huge business impact and attracts a plethora of attention from both academia and industry.…
A Random Walk Model for Item Recommendation in Folksonomies
Zhu Zhang, Daniel Zeng, Ahmed Abbasi +1
Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web2.0 applications. The tags provide a new type of information that…
Pearl: A Foundation Model for Placing Every Atom in the Right Location
Genesis Research Team, Alejandro Dobles, Nina Jovic +37
Accurately predicting the three-dimensional structures of protein-ligand complexes remains a fundamental challenge in computational drug discovery that limits the pace and success…
PRODIGY: Enabling In-context Learning Over Graphs
Qian Huang, Hongyu Ren, Peng Chen +4
In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters. While…
Learning Parameters for a Generalized Vidale-Wolfe Response Model with Flexible Ad Elasticity and Word-of-Mouth
Yanwu Yang, Baozhu Feng, Daniel Zeng
In this research, we investigate a generalized form of Vidale-Wolfe (GVW) model. One key element of our modeling work is that the GVW model contains two useful indexes representing…
Evaluating the Usefulness of Sentiment Information for Focused Crawlers
Tianjun Fu, Ahmed Abbasi, Daniel Zeng +1
Despite the prevalence of sentiment-related content on the Web, there has been limited work on focused crawlers capable of effectively collecting such content. In this study, we ev…
ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy
Daniel Zeng, Tailin Wu, Jure Leskovec
Visual relations form the basis of understanding our compositional world, as relationships between visual objects capture key information in a scene. It is then advantageous to lea…
Keyword Optimization in Sponsored Search Advertising: A Multi-Level Computational Framework
Yanwu Yang, Bernard J. Jansen, Yinghui Yang +2
In sponsored search advertising, keywords serve as an essential bridge linking advertisers, search users and search engines. Advertisers have to deal with a series of keyword decis…
Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study
Yanwu Yang, Xin Li, Bernard J. Jansen +1
Purpose: We model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are competing in the same auction or…
The Powerful Model Adpredictor for Search Engine Switching Detection Challenge
Heng Gao, Yongbao Li, Qiudan Li +1
The purpose of the Switching Detection Challenge in the 2013 WSCD workshop was to predict users' search engine swithcing actions given records about search sessions and logs.Our so…
Evolutionary dynamics of cryptocurrency transaction networks: An empirical study
Jiaqi Liang, Linjing Li, Daniel Zeng
Cryptocurrency is a well-developed blockchain technology application that is currently a heated topic throughout the world. The public availability of transaction histories offers…
Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model
Jiaheng Xie, Ruicheng Liang, Yidong Chai +2
Along with the rise of short-form videos, their mental impacts on viewers have led to widespread consequences, prompting platforms to predict videos' impact on viewers' mental heal…
YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction
Xinglin Xiao, Yijie Wang, Nan Xu +7
The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures. Recent work has proposed methods based on…
Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback
Jiamei Jiang, Jiajing Zhang, Feifei Mo +2
Planning often requires symbolic specifications that are both executable and verifiable. For large language models deployed in autonomous or decision-support systems, failures in s…
Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models?
Yifei Wang, Yu Sheng, Linjing Li +1
Recent advances in handling long sequences have facilitated the exploration of long-context in-context learning (ICL). While much of the existing research emphasizes performance im…
Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework
Jiajing Zhang, Jiamei Jiang, Chenyang Zhang +3
Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns regarding robustness and reliabi…
The RoSiD Tool: Empowering Users to Design Multimodal Signals for Human-Robot Collaboration
Nathaniel Dennler, David Delgado, Daniel Zeng +2
Robots that cooperate with humans must be effective at communicating with them. However, people have varied preferences for communication based on many contextual factors, such as…
LDM: A Large Decision Model Imitating Human Cognition with Dynamic Memory Enhancement
Xingjin Wang, Linjing Li, Daniel Zeng
With the rapid development of large language models (LLMs), it is highly demanded that LLMs can be adopted to make decisions to enable the artificial general intelligence. Most app…