activity
20232026
most citedShared Attention-based Autoencoder with Hierarchical Fusion-based Graph Convolution Network for sEEG SOZ Identification

1 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2026

FragFlex: Fragment-Based Flexible Molecular Generation for Exploring Vast Chemical Space

Sihyun Park, David Ryan Koes

Drug discovery requires efficient exploration of an astronomically large chemical space. Molecular graph diffusion models offer a promising approach to this task, but many require…

cs.IR2026

Geometric Gain Graph: Zero-Token Graph Construction for Multi-Hop RAG

Zeliang Li, Xiaofen Xing, Kailing Guo +2

Existing Retrieval-Augmented Generation (RAG) systems struggle with multi-hop reasoning due to a fundamental inability to balance relevance and novelty. Traditional dense retriever…

cs.CL2026

MERRY: Semantically Decoupled Evaluation of Multimodal Emotional and Role Consistencies of Role-Playing Agents

Zhenyu Wang, Xiaofen Xing, Yirong Chen +1

Multimodal Role-Playing Agents (MRPAs) are attracting increasing attention due to their ability to deliver more immersive multimodal emotional interactions. However, existing studi…

cs.LG2024★ 1 cited

Shared Attention-based Autoencoder with Hierarchical Fusion-based Graph Convolution Network for sEEG SOZ Identification

Huachao Yan, Kailing Guo, Shiwei Song +4

Diagnosing seizure onset zone (SOZ) is a challenge in neurosurgery, where stereoelectroencephalography (sEEG) serves as a critical technique. In sEEG SOZ identification, the existi…

cs.CV2023

CorrTalk: Correlation Between Hierarchical Speech and Facial Activity Variances for 3D Animation

Zhaojie Chu, Kailing Guo, Xiaofen Xing +3

Speech-driven 3D facial animation is a challenging cross-modal task that has attracted growing research interest. During speaking activities, the mouth displays strong motions, whi…

cs.CV2023★ 1 cited

Dynamic Shuffle: An Efficient Channel Mixture Method

Kaijun Gong, Zhuowen Yin, Yushu Li +2

The redundancy of Convolutional neural networks not only depends on weights but also depends on inputs. Shuffling is an efficient operation for mixing channel information but the s…