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Found 37 papers, 14 papers with code
Tag2Vec: Learning Tag Representations in Tag Networks
Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks.
DANE: Domain Adaptive Network Embedding
Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data.
Inference for Network Structure and Dynamics from Time Series Data via Graph Neural Network
We carried out experiments on discrete and continuous time series data.
TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting
Further analysis indicates that the locality and globality of the traffic networks are critical to traffic flow prediction and the proposed TSSRGCN model can adapt to the various temporal traffic patterns.
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective
Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks.
TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
To simultaneously extract spatial and relational information from tables, we propose a novel neural network architecture, TabularNet.
Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code Search
On the other hand, as a specific query may focus on one or several perspectives, it is difficult for a single query representation module to represent different user intents.
On the Evaluation of Commit Message Generation Models: An Experimental Study
We find that: (1) Different variants of the BLEU metric are used in previous works, which affects the evaluation and understanding of existing methods.
On the Evaluation of Neural Code Summarization
To achieve a profound understanding of how far we are from solving this problem and provide suggestions to future research, in this paper, we conduct a systematic and in-depth analysis of 5 state-of-the-art neural code summarization models on 6 widely used BLEU variants, 4 pre-processing operations and their combinations, and 3 widely used datasets.
Neuron Campaign for Initialization Guided by Information Bottleneck Theory
Initialization plays a critical role in the training of deep neural networks (DNN).
GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily
For node-level tasks, GNNs have strong power to model the homophily property of graphs (i. e., connected nodes are more similar) while their ability to capture the heterophily property is often doubtful.
Neuron with Steady Response Leads to Better Generalization
Based on the analysis results, we articulate the Neuron Steadiness Hypothesis: the neuron with similar responses to instances of the same class leads to better generalization.
Source Free Unsupervised Graph Domain Adaptation
To address the non-trivial adaptation challenges in this practical scenario, we propose a model-agnostic algorithm called SOGA for domain adaptation to fully exploit the discriminative ability of the source model while preserving the consistency of structural proximity on the target graph.
HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window Training Strategy for Temporal Link Prediction
The Temporal Link Prediction task of WSDM Cup 2022 expects a single model that can work well on two kinds of temporal graphs simultaneously, which have quite different characteristics and data properties, to predict whether a link of a given type will occur between two given nodes within a given time span.
RACE: Retrieval-Augmented Commit Message Generation
Furthermore, RACE can boost the performance of existing Seq2Seq models in commit message generation.
Accelerating Code Search with Deep Hashing and Code Classification
Code search is to search reusable code snippets from source code corpus based on natural languages queries.
CoCoSoDa: Effective Contrastive Learning for Code Search
However, there is still a lot of room for improvement in using contrastive learning for code search.
MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution
Graph Neural Networks (GNNs) have shown expressive performance on graph representation learning by aggregating information from neighbors.
Learning Rate Perturbation: A Generic Plugin of Learning Rate Schedule towards Flatter Local Minima
Learning rate is one of the most important hyper-parameters that has a significant influence on neural network training.
Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach
Graph Neural Networks (GNNs) are popular machine learning methods for modeling graph data.
Ranked #6 on
Node Classification
on Squirrel
Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing
The recent prevalence of pretrained language models (PLMs) has dramatically shifted the paradigm of semantic parsing, where the mapping from natural language utterances to structured logical forms is now formulated as a Seq2Seq task.
DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms
Furthermore, based on the dynamic user intent representations, we propose a meta predictor to perform differentiated user engagement forecasting.
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy
In more detail, penultimate layer outputs on the training set are considered as the representations of in-distribution (ID) data.
Ranked #11 on
Out-of-Distribution Detection
on ImageNet-1k vs Places
Homophily-oriented Heterogeneous Graph Rewiring
To this end, we propose HDHGR, a homophily-oriented deep heterogeneous graph rewiring approach that modifies the HG structure to increase the performance of HGNN.
Robust Mid-Pass Filtering Graph Convolutional Networks
Theoretical analyses guarantee the robustness of signals through the mid-pass filter, and we also shed light on the properties of different frequency signals under adversarial attacks.
Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond
Our experimental study shows that (1) lexical, syntactic and structural properties of source code are encoded in the lower, intermediate, and higher layers, respectively, while the semantic property spans across the entire model.
Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention
Recent years have witnessed the great potential of attention mechanism in graph representation learning.
Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs
We propose an Automated Financial Information Extraction (AFIE) framework that enhances LLMs' ability to comprehend and extract information from financial reports.
GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking
In this study, we conduct an extensive investigation to assess the proficiency of LLMs in comprehending graph data, employing a diverse range of structural and semantic-related tasks.
On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
Graph CF has attracted more and more attention in recent years due to its effectiveness in leveraging high-order information in the user-item bipartite graph for better recommendations.
SoTaNa: The Open-Source Software Development Assistant
To meet the demands of this dynamic field, there is a growing need for an effective software development assistant.
Text-to-Image Generation for Abstract Concepts
Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts.
TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning
Table-based reasoning has shown remarkable progress in combining deep models with discrete reasoning, which requires reasoning over both free-form natural language (NL) questions and semi-structured tabular data.
Professional Network Matters: Connections Empower Person-Job Fit
Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions.
Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries
We evaluate five state-of-the-art models using three different metrics and the results show that our benchmark presents introduces considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.
TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit
Person-job fit is an essential part of online recruitment platforms in serving various downstream applications like Job Search and Candidate Recommendation.
Prompting with Divide-and-Conquer Program Makes Large Language Models Discerning to Hallucination and Deception
Foundation models, such as Large language Models (LLMs), have attracted significant amount of interest due to their large number of applications.
