Search Results for author:
Found 23 papers, 9 papers with code
Stacking Networks Dynamically for Image Restoration Based on the Plug-and-Play Framework
Recently, stacked networks show powerful performance in Image Restoration, such as challenging motion deblurring problems.
Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series
Complementing the dataset, we introduce IMM-TSF, a benchmark library for forecasting on irregular multimodal time series, enabling asynchronous integration and realistic evaluation.
Advanced long-term earth system forecasting by learning the small-scale nature
Reliable long-term forecast of Earth system dynamics is heavily hampered by instabilities in current AI models during extended autoregressive simulations.
FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
Data-driven modeling of fluid dynamics has advanced rapidly with neural PDE solvers, yet a fair and strong benchmark remains fragmented due to the absence of unified PDE datasets and standardized evaluation protocols.
CellVerse: Do Large Language Models Really Understand Cell Biology?
Recent studies have demonstrated the feasibility of modeling single-cell data as natural languages and the potential of leveraging powerful large language models (LLMs) for understanding cell biology.
Toward Super Agent System with Hybrid AI Routers
With advances in multi-modality models and edge hardware, we envision that most computations can be handled locally, with cloud collaboration only as needed.
Protein Large Language Models: A Comprehensive Survey
Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design.
Omni-Mol: Exploring Universal Convergent Space for Omni-Molecular Tasks
Within the realm of molecular learning, several studies have explored unifying diverse tasks across diverse domains.
Efficient Traffic Prediction Through Spatio-Temporal Distillation
Graph neural networks (GNNs) have gained considerable attention in recent years for traffic flow prediction due to their ability to learn spatio-temporal pattern representations through a graph-based message-passing framework.
Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics
Accurately predicting long-horizon molecular dynamics (MD) trajectories remains a significant challenge, as existing deep learning methods often struggle to retain fidelity over extended simulations.
Graph Masked Autoencoder for Spatio-Temporal Graph Learning
To address these challenges, we propose a novel spatio-temporal graph masked autoencoder paradigm that explores generative self-supervised learning for effective spatio-temporal data augmentation.
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security
The widespread adoption of smartphones and Location-Based Social Networks has led to a massive influx of spatio-temporal data, creating unparalleled opportunities for enhancing Point-of-Interest (POI) recommendation systems.
Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey
This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques.
A Survey of Generative Techniques for Spatial-Temporal Data Mining
By integrating generative techniques and providing a standardized framework, the paper contributes to advancing the field and encourages researchers to explore the vast potential of generative techniques in spatial-temporal data mining.
BENO: Boundary-embedded Neural Operators for Elliptic PDEs
Here we introduce Boundary-Embedded Neural Operators (BENO), a novel neural operator architecture that embeds the complex geometries and inhomogeneous boundary values into the solving of elliptic PDEs.
When Parameter-efficient Tuning Meets General-purpose Vision-language Models
Instruction tuning has shown promising potential for developing general-purpose AI capabilities by using large-scale pre-trained models and boosts growing research to integrate multimodal information for creative applications.
PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction
In this paper, we investigate the challenge of spatio-temporal video prediction task, which involves generating future video frames based on historical spatio-temporal observation streams.
Visual Tuning
With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer.
LION: Implicit Vision Prompt Tuning
Despite recent competitive performance across a range of vision tasks, vision Transformers still have an issue of heavy computational costs.
Prototypical Mixing and Retrieval-Based Refinement for Label Noise-Resistant Image Retrieval
This paper investigates a realistic but understudied problem of image retrieval under label noise, which could lead to severe overfitting or memorization of noisy samples during optimization.
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
Obj2Seq is able to flexibly determine input categories to satisfy customized requirements, and be easily extended to different visual tasks.
A Survey on Deep Hashing Methods
Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining.
Deep Dynamic Boosted Forest
Specically, we propose to measure the quality of each leaf node of every decision tree in the random forest to determine hard examples.
