Search Results for author:
Found 38 papers, 19 papers with code
AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning (PEFT) method validated across NLP and CV domains.
EAVIT: Efficient and Accurate Human Value Identification from Text data via LLMs
The rapid evolution of large language models (LLMs) has revolutionized various fields, including the identification and discovery of human values within text data.
Large Language Model Psychometrics: A Systematic Review of Evaluation, Validation, and Enhancement
The rapid advancement of large language models (LLMs) has outpaced traditional evaluation methodologies.
Generative Psycho-Lexical Approach for Constructing Value Systems in Large Language Models
Despite growing efforts in evaluating, understanding, and aligning LLM values, a psychologically grounded LLM value system remains underexplored.
DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach
This paper identifies two key challenges in adapting graph prompting methods for complex graphs: (1) adapting the model to new distributions in downstream tasks to mitigate pre-training and fine-tuning discrepancies from heterophily and (2) customizing prompts for hop-specific node requirements.
TransPlace: Transferable Circuit Global Placement via Graph Neural Network
Global placement, a critical step in designing the physical layout of computer chips, is essential to optimize chip performance.
Measuring Human and AI Values Based on Generative Psychometrics with Large Language Models
Human values and their measurement are long-standing interdisciplinary inquiry.
ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language Models
This work introduces ValueBench, the first comprehensive psychometric benchmark for evaluating value orientations and value understanding in LLMs.
RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network
Placement is a critical and challenging step of modern chip design, with routability being an essential indicator of placement quality.
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers
Graph Transformers (GTs) have significantly advanced the field of graph representation learning by overcoming the limitations of message-passing graph neural networks (GNNs) and demonstrating promising performance and expressive power.
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design.
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
To fill this gap, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems.
On Structural Expressive Power of Graph Transformers
Graph Transformer has recently received wide attention in the research community with its outstanding performance, yet its structural expressive power has not been well analyzed.
Continual Learning on Dynamic Graphs via Parameter Isolation
Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item.
Hierarchical Transformer for Scalable Graph Learning
Graph Transformer is gaining increasing attention in the field of machine learning and has demonstrated state-of-the-art performance on benchmarks for graph representation learning.
LEReg: Empower Graph Neural Networks with Local Energy Regularization
With Intra-Energy Reg, we strengthen the message passing within each part, which is beneficial for getting more useful information.
Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily
These experiments show the excellent expressive power of MWGNN in dealing with graph data with various distributions.
Deep Molecular Representation Learning via Fusing Physical and Chemical Information
To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules.
Equivalent Distance Geometry Error for Molecular Conformation Comparison
Straight-forward conformation generation models, which generate 3-D structures directly from input molecular graphs, play an important role in various molecular tasks with machine learning, such as 3D-QSAR and virtual screening in drug design.
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
However, the representation ability of such models is limited due to: (1) shallow GNNs are incapable to capture long-range spatial correlations, (2) only spatial connections are considered and a mass of semantic connections are ignored, which are of great importance for a comprehensive understanding of traffic networks.
Ranked #5 on
Traffic Prediction
on LargeST
HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning.
Lorentzian Graph Convolutional Networks
We also find that the performance of some hyperbolic GCNs can be improved by simply replacing the graph operations with those we defined in this paper.
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
However, the inability of GNNs to model substructures in graphs remains a significant drawback.
Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning.
Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks
To address this weakness, we introduce a novel framework of conducting graph convolutions, where nodes are discretely selected among multi-hop neighborhoods to construct adaptive receptive fields (ARFs).
Learning Node Representations from Noisy Graph Structures
Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting.
EPNE: Evolutionary Pattern Preserving Network Embedding
In this paper, we propose EPNE, a temporal network embedding model preserving evolutionary patterns of the local structure of nodes.
Streaming Graph Neural Networks via Continual Learning
In this paper, we propose a streaming GNN model based on continual learning so that the model is trained incrementally and up-to-date node representations can be obtained at each time step.
Graph Structural-topic Neural Network
Specifically, we build topic models upon graphs using anonymous walks and Graph Anchor LDA, an LDA variant that selects significant structural patterns first, so as to alleviate the complexity and generate structural topics efficiently.
Multi-Component Graph Convolutional Collaborative Filtering
The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph.
GraLSP: Graph Neural Networks with Local Structural Patterns
Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns.
Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets
Time series modeling has attracted extensive research efforts; however, achieving both reliable efficiency and interpretability from a unified model still remains a challenging problem.
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.
Tag2Vec: Learning Tag Representations in Tag Networks
Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks.
GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes.
SepNE: Bringing Separability to Network Embedding
We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes.
Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality
The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing.
