Search Results for author: Yuhao Yang

Found 23 papers, 16 papers with code

GTA1: GUI Test-time Scaling Agent

no code implementations8 Jul 2025 Yan Yang, Dongxu Li, Yutong Dai, Yuhao Yang, Ziyang Luo, Zirui Zhao, Zhiyuan Hu, Junzhe Huang, Amrita Saha, Zeyuan Chen, ran Xu, Liyuan Pan, Caiming Xiong, Junnan Li

Specifically, a user instruction is decomposed into a sequence of action proposals, each corresponding to an interaction with the GUI.

Reinforcement Learning (RL) Task Planning +1

Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

no code implementations4 Mar 2025 Yuhao Yang, Zhi Ji, Zhaopeng Li, Yi Li, Zhonglin Mo, Yue Ding, Kai Chen, Zijian Zhang, Jie Li, Shuanglong Li, Lin Liu

To address this, we introduce the Cascaded Organized Bi-Represented generAtive retrieval (COBRA) framework, which innovatively integrates sparse semantic IDs and dense vectors through a cascading process.

Quantization Recommendation Systems +1

RecLM: Recommendation Instruction Tuning

1 code implementation26 Dec 2024 Yangqin Jiang, Yuhao Yang, Lianghao Xia, Da Luo, Kangyi Lin, Chao Huang

Modern recommender systems aim to deeply understand users' complex preferences through their past interactions.

Collaborative Filtering Diversity +2

GraphAgent: Agentic Graph Language Assistant

no code implementations22 Dec 2024 Yuhao Yang, Jiabin Tang, Lianghao Xia, Xingchen Zou, Yuxuan Liang, Chao Huang

Real-world data is represented in both structured (e. g., graph connections) and unstructured (e. g., textual, visual information) formats, encompassing complex relationships that include explicit links (such as social connections and user behaviors) and implicit interdependencies among semantic entities, often illustrated through knowledge graphs.

Knowledge Graphs Node Classification +2

Aria-UI: Visual Grounding for GUI Instructions

1 code implementation20 Dec 2024 Yuhao Yang, Yue Wang, Dongxu Li, Ziyang Luo, Bei Chen, Chao Huang, Junnan Li

Digital agents for automating tasks across different platforms by directly manipulating the GUIs are increasingly important.

Natural Language Visual Grounding Visual Grounding

SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation

1 code implementation19 Oct 2024 Jingxuan Chen, Derek Yuen, Bin Xie, Yuhao Yang, Gongwei Chen, Zhihao Wu, Li Yixing, Xurui Zhou, Weiwen Liu, Shuai Wang, Kaiwen Zhou, Rui Shao, Liqiang Nie, Yasheng Wang, Jianye Hao, Jun Wang, Kun Shao

SPA-Bench offers three key contributions: (1) A diverse set of tasks covering system and third-party apps in both English and Chinese, focusing on features commonly used in daily routines; (2) A plug-and-play framework enabling real-time agent interaction with Android devices, integrating over ten agents with the flexibility to add more; (3) A novel evaluation pipeline that automatically assesses agent performance across multiple dimensions, encompassing seven metrics related to task completion and resource consumption.

AI Agent Benchmarking +2

Learning Geospatial Region Embedding with Heterogeneous Graph

no code implementations23 May 2024 Xingchen Zou, Jiani Huang, Xixuan Hao, Yuhao Yang, Haomin Wen, Yibo Yan, Chao Huang, Yuxuan Liang

In this paper, we present GeoHG, an effective heterogeneous graph structure for learning comprehensive region embeddings for various downstream tasks.

Graph Learning Representation Learning

HiGPT: Heterogeneous Graph Language Model

1 code implementation25 Feb 2024 Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Long Xia, Dawei Yin, Chao Huang

However, existing frameworks for heterogeneous graph learning have limitations in generalizing across diverse heterogeneous graph datasets.

Graph Learning Language Modeling +3

DiffKG: Knowledge Graph Diffusion Model for Recommendation

1 code implementation28 Dec 2023 Yangqin Jiang, Yuhao Yang, Lianghao Xia, Chao Huang

To bridge this research gap, we propose a novel knowledge graph diffusion model for recommendation, referred to as DiffKG.

Data Augmentation Graph Representation Learning +3

GraphPro: Graph Pre-training and Prompt Learning for Recommendation

2 code implementations28 Nov 2023 Yuhao Yang, Lianghao Xia, Da Luo, Kangyi Lin, Chao Huang

The temporal prompt mechanism encodes time information on user-item interaction, allowing the model to naturally capture temporal context, while the graph-structural prompt learning mechanism enables the transfer of pre-trained knowledge to adapt to behavior dynamics without the need for continuous incremental training.

Prompt Learning

GraphGPT: Graph Instruction Tuning for Large Language Models

1 code implementation19 Oct 2023 Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, Chao Huang

The open-sourced model implementation of our GraphGPT is available at https://github. com/HKUDS/GraphGPT.

Data Augmentation Graph Learning +2

Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction

no code implementations16 Aug 2023 Yuhao Yang, Jun Wu, Yue Wang, Guangjian Zhang, Rong Xiong

Traditional geometric registration based estimation methods only exploit the CAD model implicitly, which leads to their dependence on observation quality and deficiency to occlusion.

6D Pose Estimation using RGB

SSLRec: A Self-Supervised Learning Framework for Recommendation

1 code implementation10 Aug 2023 Xubin Ren, Lianghao Xia, Yuhao Yang, Wei Wei, Tianle Wang, Xuheng Cai, Chao Huang

Our SSLRec platform covers a comprehensive set of state-of-the-art SSL-enhanced recommendation models across different scenarios, enabling researchers to evaluate these cutting-edge models and drive further innovation in the field.

Collaborative Filtering Data Augmentation +2

Knowledge Graph Self-Supervised Rationalization for Recommendation

1 code implementation6 Jul 2023 Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang

By masking important knowledge with high rational scores, KGRec is trained to rebuild and highlight useful knowledge connections that serve as rationales.

Contrastive Learning Graph Learning +1

HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level

1 code implementation ACL 2023 Haoran Luo, Haihong E, Yuhao Yang, Yikai Guo, Mingzhi Sun, Tianyu Yao, Zichen Tang, Kaiyang Wan, Meina Song, Wei Lin

The global-level attention can model the graphical structure of HKG using hypergraph dual-attention layers, while the local-level attention can learn the sequential structure inside H-Facts via heterogeneous self-attention layers.

Attribute Knowledge Graphs +2

Knowledge Graph Contrastive Learning for Recommendation

1 code implementation2 May 2022 Yuhao Yang, Chao Huang, Lianghao Xia, Chenliang Li

However, the success of such methods relies on the high quality knowledge graphs, and may not learn quality representations with two challenges: i) The long-tail distribution of entities results in sparse supervision signals for KG-enhanced item representation; ii) Real-world knowledge graphs are often noisy and contain topic-irrelevant connections between items and entities.

Contrastive Learning General Knowledge +3

Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform

no code implementations29 Mar 2022 Mingjun Li, Jianlei Yang, Yingjie Qi, Meng Dong, Yuhao Yang, Runze Liu, Weitao Pan, Bei Yu, Weisheng Zhao

In this paper, Eventor is proposed as a fast and efficient EMVS accelerator by realizing the most critical and time-consuming stages including event back-projection and volumetric ray-counting on FPGA.

Quantization

BigDL: A Distributed Deep Learning Framework for Big Data

1 code implementation16 Apr 2018 Jason Dai, Yiheng Wang, Xin Qiu, Ding Ding, Yao Zhang, Yanzhang Wang, Xianyan Jia, Cherry Zhang, Yan Wan, Zhichao Li, Jiao Wang, Shengsheng Huang, Zhongyuan Wu, Yang Wang, Yuhao Yang, Bowen She, Dongjie Shi, Qi Lu, Kai Huang, Guoqiong Song

This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms.

Deep Learning Fraud Detection +2

Learning Social Circles in Ego Networks based on Multi-View Social Graphs

no code implementations16 Jul 2016 Chao Lan, Yuhao Yang, Xiao-Li Li, Bo Luo, Jun Huan

Based on extensive automatic and manual experimental evaluations, we deliver two major findings: first, multi-view clustering techniques perform better than common single-view clustering techniques, which only use one view or naively integrate all views for detection, second, the standard multi-view clustering technique is less robust than our modified technique, which selectively transfers information across views based on an assumption that sparse network structures are (potentially) incomplete.

Clustering

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