Yuxuan Liang

papers

1,490

total citations

papers (24)

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

ICLR 2024arXiv

765

citations

MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting

AAAI 2024arXiv

185

citations

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

ICML 2025arXiv

citations

Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching

ICML 2024arXiv

citations

Towards Neural Scaling Laws for Time Series Foundation Models

ICLR 2025arXiv

citations

DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition

ECCV 2022arXiv

citations

Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems

ICLR 2025arXiv

citations

Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach

AAAI 2025arXiv

citations

Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness

ICML 2024arXiv

citations

Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting

ICLR 2025arXiv

citations

SENCR: A Span Enhanced Two-Stage Network with Counterfactual Rethinking for Chinese NER

AAAI 2024

citations

Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classifications

AAAI 2025arXiv

citations

Towards Scalable and Deep Graph Neural Networks via Noise Masking

AAAI 2025arXiv

citations

Improving Bilinear RNN with Closed-loop Control

NEURIPS 2025arXiv

citations

UniTR: A Unified Framework for Joint Representation Learning of Trajectories and Road Networks

AAAI 2025

citations

Earthfarsser: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model

AAAI 2024

citations

Adaptive Data Augmentation on Temporal Graphs

NEURIPS 2021

citations

Aeolus: A Multi-structural Flight Delay Dataset

NEURIPS 2025arXiv

citations

Directed Graph Contrastive Learning

NEURIPS 2021

citations

Digraph Inception Convolutional Networks

NEURIPS 2020

citations

Yuxuan Liang

papers (24)

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting

LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting

Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment

Unlocking the Power of LSTM for Long Term Time Series Forecasting

Position: What Can Large Language Models Tell Us about Time Series Analysis

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching

Towards Neural Scaling Laws for Time Series Foundation Models

DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition

Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems

Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach

Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness

Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting

SENCR: A Span Enhanced Two-Stage Network with Counterfactual Rethinking for Chinese NER

Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classifications

Towards Scalable and Deep Graph Neural Networks via Noise Masking

Improving Bilinear RNN with Closed-loop Control

UniTR: A Unified Framework for Joint Representation Learning of Trajectories and Road Networks

Earthfarsser: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model

Adaptive Data Augmentation on Temporal Graphs

Aeolus: A Multi-structural Flight Delay Dataset

Directed Graph Contrastive Learning

Digraph Inception Convolutional Networks

papers (24)

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting

LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting

Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment

Unlocking the Power of LSTM for Long Term Time Series Forecasting

Position: What Can Large Language Models Tell Us about Time Series Analysis

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching

Towards Neural Scaling Laws for Time Series Foundation Models

DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition

Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems

Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach

Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness

Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting

SENCR: A Span Enhanced Two-Stage Network with Counterfactual Rethinking for Chinese NER

Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classifications

Towards Scalable and Deep Graph Neural Networks via Noise Masking

Improving Bilinear RNN with Closed-loop Control

UniTR: A Unified Framework for Joint Representation Learning of Trajectories and Road Networks

Earthfarsser: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model

Adaptive Data Augmentation on Temporal Graphs

Aeolus: A Multi-structural Flight Delay Dataset

Directed Graph Contrastive Learning

Digraph Inception Convolutional Networks