Search Results for author:
Found 42 papers, 27 papers with code
Large Language Models Are Innate Crystal Structure Generators
Crystal structure generation is fundamental to materials discovery, enabling the prediction of novel materials with desired properties.
SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training
Recent advancements highlight the potential of end-to-end real-time spoken dialogue systems, showcasing their low latency and high quality.
Bi-Level Graph Structure Learning for Next POI Recommendation
To address the aforementioned issues, this paper presents a novel Bi-level Graph Structure Learning (BiGSL) for next POI recommendation.
MIRAI: Evaluating LLM Agents for Event Forecasting
Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems.
Efficient Evolutionary Search Over Chemical Space with Large Language Models
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable.
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly annotated biomedical data and computational resources.
An Evaluation of Large Language Models in Bioinformatics Research
Large language models (LLMs) such as ChatGPT have gained considerable interest across diverse research communities.
GSLB: The Graph Structure Learning Benchmark
To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms.
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks
Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design.
Uncovering Neural Scaling Laws in Molecular Representation Learning
Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design.
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
Most of the existing Large Language Model (LLM) benchmarks on scientific problem reasoning focus on problems grounded in high-school subjects and are confined to elementary algebraic operations.
Neighborhood-Regularized Self-Training for Learning with Few Labels
Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36. 8% and saves 57. 3% of the time when compared with the best baseline.
A Survey on Pretrained Language Models for Neural Code Intelligence
As the complexity of modern software continues to escalate, software engineering has become an increasingly daunting and error-prone endeavor.
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.
A Systematic Survey of Chemical Pre-trained Models
Deep learning has achieved remarkable success in learning representations for molecules, which is crucial for various biochemical applications, ranging from property prediction to drug design.
Code Recommendation for Open Source Software Developers
In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects.
Improving Molecular Pretraining with Complementary Featurizations
Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery.
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience.
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning
Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets.
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs.
A Survey on Deep Graph Generation: Methods and Applications
In this paper, we conduct a comprehensive review on the existing literature of deep graph generation from a variety of emerging methods to its wide application areas.
A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications
Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of Natural Language Processing (NLP).
Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships. To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity).
An Empirical Study of Graph Contrastive Learning
We envision this work to provide useful empirical evidence of effective GCL algorithms and offer several insights for future research.
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data.
Deep Contrastive Multiview Network Embedding
In our work, different views can be obtained based on the various relations among nodes.
Deep Active Learning for Text Classification with Diverse Interpretations
To train high-performing models with the minimal annotation cost, active learning is proposed to select and label the most informative samples, yet it is still challenging to measure informativeness of samples used in DNNs.
BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis
Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience.
Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis
Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis.
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange
In this paper, we propose to substitute these redundant channels with other informative channels to achieve this goal.
Mining Latent Structures for Multimedia Recommendation
To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs.
Ranked #3 on
Multi-modal Recommendation
on Amazon Clothing
A Survey on Graph Structure Learning: Progress and Opportunities
Specifically, we first formulate a general pipeline of GSL and review state-of-the-art methods classified by the way of modeling graph structures, followed by applications of GSL across domains.
Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
To better model complex feature interaction, in this paper we propose a novel DisentanglEd Self-atTentIve NEtwork (DESTINE) framework for CTR prediction that explicitly decouples the computation of unary feature importance from pairwise interaction.
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision
To this end, we present a minimax selection scheme that explicitly harnesses neighborhood information and discover homophilous subgraphs to facilitate active selection.
Graph Contrastive Learning with Adaptive Augmentation
On the node attribute level, we corrupt node features by adding more noise to unimportant node features, to enforce the model to recognize underlying semantic information.
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning
In CAGNN, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments.
Deep Graph Contrastive Representation Learning
Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.
Ranked #1 on
Node Classification
on DBLP
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
However, these methods compress a session into one fixed representation vector without considering the target items to be predicted.
Ranked #3 on
Session-Based Recommendations
on yoochoose1
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Then, in order to better capture the complicated non-linearity of graph data, we present a novel GraphAIR framework which models the neighborhood interaction in addition to neighborhood aggregation.
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining.
Session-based Recommendation with Graph Neural Networks
To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i. e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity.
Ranked #1 on
Session-Based Recommendations
on Gowalla
Active Learning for Wireless IoT Intrusion Detection
This paper discusses the human-in-the-loop active learning approach for wireless intrusion detection.
