activity
20152026
most citedData Augmentation in Emotion Classification Using Generative Adversarial Networks

78 citations · 104 across the 13 of their papers we have counts for

collaborators

14 papers

cs.LG2026

HeuFouFT: Task-Guided Metaheuristic Coordinate Search for Fourier Fine-Tuning

Ruiheng Wang, Yubo Hou, Yakun Zhu +3

We introduce Heuristic-Guided Fourier Fine-Tuning (HeuFouFT), a task-guided framework for selecting trainable frequency coordinates in Fourier fine-tuning. Existing uniform and Gau…

cs.CL2026

Beyond Sentiment: Structured Information Extraction from Financial News

Daohan Zhu, Sitong Ge, Ruofei Wang +4

Financial sentiment analysis has become a standard component in news-driven stock prediction, yet it reduces rich, multi-dimensional news articles to a single polarity score. We hy…

cs.CL2026

PersonaTree: Structured Lifecycle Memory for Person Understanding in LLM Agents

Yubo Hou, Jingwei Song, Hongbo Zhang +4

Persistent LLM agents require memory representations that make the formation of person understanding explicit across long term interaction. Existing agent memory methods emphasize…

cs.CL2026

FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse

Yubo Hou, Zhisheng Chen, Tao Wan +1

The stateless architecture of Large Language Models inherently lacks the mechanism to preserve dynamic context, compelling agents to redundantly reprocess history to maintain long-…

cs.CV2022★ 2 cited

Reasoning with Multi-Structure Commonsense Knowledge in Visual Dialog

Shunyu Zhang, Xiaoze Jiang, Zequn Yang +2

Visual Dialog requires an agent to engage in a conversation with humans grounded in an image. Many studies on Visual Dialog focus on the understanding of the dialog history or the…

cs.CV2018★ 2 cited

A sequential guiding network with attention for image captioning

Daouda Sow, Zengchang Qin, Mouhamed Niasse +1

The recent advances of deep learning in both computer vision (CV) and natural language processing (NLP) provide us a new way of understanding semantics, by which we can deal with m…