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Found 23 papers, 16 papers with code
GTA1: GUI Test-time Scaling Agent
Specifically, a user instruction is decomposed into a sequence of action proposals, each corresponding to an interaction with the GUI.
Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations
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.
RecLM: Recommendation Instruction Tuning
Modern recommender systems aim to deeply understand users' complex preferences through their past interactions.
GraphAgent: Agentic Graph Language Assistant
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.
Aria-UI: Visual Grounding for GUI Instructions
Digital agents for automating tasks across different platforms by directly manipulating the GUIs are increasingly important.
Ranked #4 on
Natural Language Visual Grounding
on ScreenSpot
SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation
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.
Learning Geospatial Region Embedding with Heterogeneous Graph
In this paper, we present GeoHG, an effective heterogeneous graph structure for learning comprehensive region embeddings for various downstream tasks.
HiGPT: Heterogeneous Graph Language Model
However, existing frameworks for heterogeneous graph learning have limitations in generalizing across diverse heterogeneous graph datasets.
DiffKG: Knowledge Graph Diffusion Model for Recommendation
To bridge this research gap, we propose a novel knowledge graph diffusion model for recommendation, referred to as DiffKG.
GraphPro: Graph Pre-training and Prompt Learning for Recommendation
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.
GraphGPT: Graph Instruction Tuning for Large Language Models
The open-sourced model implementation of our GraphGPT is available at https://github. com/HKUDS/GraphGPT.
Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction
However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable arity of entities.
Event-based N-ary Relaiton Extraction
Hypergraph-based N-ary Relaiton Extraction
+3
Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction
Traditional geometric registration based estimation methods only exploit the CAD model implicitly, which leads to their dependence on observation quality and deficiency to occlusion.
SSLRec: A Self-Supervised Learning Framework for Recommendation
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.
Knowledge Graph Self-Supervised Rationalization for Recommendation
By masking important knowledge with high rational scores, KGRec is trained to rebuild and highlight useful knowledge connections that serve as rationales.
HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level
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.
Ranked #1 on
Link Prediction
on Wikipeople
Debiased Contrastive Learning for Sequential Recommendation
This solution is designed to tackle the popularity bias issue in recommendation systems.
NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs
Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs).
Ranked #1 on
Complex Query Answering
on WD50K-QE
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation
Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework.
Knowledge Graph Contrastive Learning for Recommendation
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.
Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform
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.
BigDL: A Distributed Deep Learning Framework for Big Data
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.
Learning Social Circles in Ego Networks based on Multi-View Social Graphs
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.
