Search Results for author:
Found 38 papers, 13 papers with code
Hypergraph Self-supervised Learning with Sampling-efficient Signals
Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels.
Seeing Text in the Dark: Algorithm and Benchmark
Localizing text in low-light environments is challenging due to visual degradations.
Construction of Functional Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model
The convergence of materials science and artificial intelligence has unlocked new opportunities for gathering, analyzing, and generating novel materials sourced from extensive scientific literature.
STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model
In this work, we introduce Spatial-Temporal Graph Mamba (STG-Mamba) as the first exploration of leveraging the powerful selective state space models for STG learning by treating STG Network as a system, and employing the Graph Selective State Space Block (GS3B) to precisely characterize the dynamic evolution of STG networks.
What is Wrong with End-to-End Learning for Phase Retrieval?
For nonlinear inverse problems that are prevalent in imaging science, symmetries in the forward model are common.
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems
Recently, large language models (LLMs) have notably positioned them as capable tools for addressing complex optimization challenges.
Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies
There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
DARWIN Series: Domain Specific Large Language Models for Natural Science
To add new capabilities in natural science, enabling the acceleration and enrichment of automation of the discovery process, we present DARWIN, a series of tailored LLMs for natural science, mainly in physics, chemistry, and material science.
Denoising Variational Graph of Graphs Auto-Encoder for Predicting Structured Entity Interactions
The interactions between structured entities play important roles in a wide range of applications such as chemistry, material science, biology, and medical science.
Query2GMM: Learning Representation with Gaussian Mixture Model for Reasoning over Knowledge Graphs
Research along this line suggests that using multi-modal distribution to represent answer entities is more suitable than uni-modal distribution, as a single query may contain multiple disjoint answer subsets due to the compositional nature of multi-hop queries and the varying latent semantics of relations.
Addressing Variable Dependency in GNN-based SAT Solving
Boolean satisfiability problem (SAT) is fundamental to many applications.
Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT
The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches.
WISK: A Workload-aware Learned Index for Spatial Keyword Queries
In this paper, we propose WISK, a learned index for spatial keyword queries, which self-adapts for optimizing querying costs given a query workload.
LabelPrompt: Effective Prompt-based Learning for Relation Classification
Specifically, associating natural language words that fill the masked token with semantic relation labels (\textit{e. g.} \textit{``org:founded\_by}'') is difficult.
Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks
In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data.
Towards Higher-order Topological Consistency for Unsupervised Network Alignment
Network alignment task, which aims to identify corresponding nodes in different networks, is of great significance for many subsequent applications.
GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs
Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path.
iEmoTTS: Toward Robust Cross-Speaker Emotion Transfer and Control for Speech Synthesis based on Disentanglement between Prosody and Timbre
The emotion encoder extracts the identity of emotion type as well as the respective emotion intensity from the mel-spectrogram of input speech.
Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching
In recent years, many advanced techniques for query vertex ordering (i. e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules.
GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]
In this paper, we study a region partitioning problem, namely optimal grid size selection problem (OGSS), which aims to minimize the real error of spatiotemporal prediction models by selecting the optimal grid size.
Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model
With the surge of graph embedding mechanism, it has also been adopted to community detection.
Lyra: A Benchmark for Turducken-Style Code Generation
Since a declarative language is typically embedded in an imperative language (i. e., the turducken-style programming) in real-world software development, the promising results on declarative languages can hardly lead to significant reduction of manual software development efforts.
A Syntax-Guided Edit Decoder for Neural Program Repair
Our results show that Recoder repairs 53 bugs on Defects4J v1. 2, which achieves 21. 4% improvement over the previous state-of-the-art approach for single-hunk bugs (TBar).
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference
Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e. g., in reinforcement learning based recommender systems.
Generalized Equivariance and Preferential Labeling for GNN Node Classification
Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content.
X-ray flares from the stellar tidal disruption by a candidate supermassive black hole binary
Optical transient surveys have led to the discovery of dozens of stellar tidal disruption events (TDEs) by massive black hole in the centers of galaxies.
High Energy Astrophysical Phenomena Astrophysics of Galaxies
PEFP: Efficient k-hop Constrained s-t Simple Path Enumeration on FPGA
Motivated by this, in this paper, we propose the first FPGA-based algorithm PEFP to solve the problem of k-hop constrained s-t simple path enumeration efficiently.
Databases
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy.
NLocalSAT: Boosting Local Search with Solution Prediction
However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers.
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning
To this end, we propose a novel heterogeneous graph-based knowledge transfer method (HGKT) for GZSL, agnostic to unseen classes and instances, by leveraging graph neural network.
A Survey and Experimental Analysis of Distributed Subgraph Matching
We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.
Databases
TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes
Driven by deep neural networks and large scale datasets, scene text detection methods have progressed substantially over the past years, continuously refreshing the performance records on various standard benchmarks.
Ranked #2 on
Curved Text Detection
on SCUT-CTW1500
Efficient Graph Edit Distance Computation and Verification via Anchor-aware Lower Bound Estimation
Graph edit distance (GED) is an important similarity measure adopted in a similarity-based analysis between two graphs, and computing GED is a primitive operator in graph database analysis.
Databases Data Structures and Algorithms
Deep Extreme Multi-label Learning
Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data.
Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval
Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo.
Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)
Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision.
Databases
Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem.
