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Found 18 papers, 12 papers with code
Large Language Model Empowered Embedding Generator for Sequential Recommendation
RAT refines the embeddings to be optimally suited for SRS.
Bidirectional Gated Mamba for Sequential Recommendation
In various domains, Sequential Recommender Systems (SRS) have become essential due to their superior capability to discern intricate user preferences.
Logic Distillation: Learning from Code Function by Function for Planning and Decision-making
Subsequently, based on the function base, LD fine-tunes S-LLMs to learn the logic employed by L-LLMs in planning and decision-making.
Improving Large Models with Small models: Lower Costs and Better Performance
On the other hand, certain tasks can be broken down into multiple subtasks, some of which can be completed without powerful capabilities.
Large Language Models Enhanced Sequential Recommendation for Long-tail User and Item
These challenges, termed as the long-tail user and long-tail item dilemmas, often create obstacles for traditional SRS methods.
M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework
M3oE integrates multi-domain information, maps knowledge across domains and tasks, and optimizes multiple objectives.
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
To evaluate the editing impact on the behaviours of LLMs, we propose two model editing studies for medical domain: (1) editing factual knowledge for medical specialization and (2) editing the explanatory ability for complex knowledge.
Large Language Model Distilling Medication Recommendation Model
In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER).
When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications
To address these two problems, we propose a novel parameter efficient fine-tuning framework for multi-task medical applications, dubbed as MOELoRA.
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.
