Search Results for author:
Found 12 papers, 6 papers with code
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model
Generating trajectory data is among promising solutions to addressing privacy concerns, collection costs, and proprietary restrictions usually associated with human mobility analyses.
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
In this paper, we propose two model editing studies and validate them in the medical domain: (1) directly editing the factual medical knowledge and (2) editing the explanations to facts.
Large Language Model Distilling Medication Recommendation Model
In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER).
MOELoRA: An MOE-based Parameter Efficient Fine-Tuning Method for Multi-task Medical Applications
Additionally, we propose a task-motivated gate function for all MOELoRA layers that can regulate the contributions of each expert and generate distinct parameters for various tasks.
InvKA: Gait Recognition via Invertible Koopman Autoencoder
Most current gait recognition methods suffer from poor interpretability and high computational cost.
Diffusion Augmentation for Sequential Recommendation
However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems.
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction
We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes.
Multimodal Recommender Systems: A Survey
In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition
Besides, these methods directly calculate the pair-wise global self-attention equally for all the joints in both the spatial and temporal dimensions, undervaluing the effect of discriminative local joints and the short-range temporal dynamics.
A Comprehensive Survey on Trustworthy Recommender Systems
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.
The Butterfly Effect in Primary Visual Cortex
Experimental results on image segmentation indicate that the CCNN model has better performance than the state-of-the-art of visual cortex neural network models.
Global Optimal Path-Based Clustering Algorithm
The results on synthetic datasets show that the GOPC algorithm can recognize all kinds of clusters regardless of their shapes, sizes, or densities.
