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Found 6 papers, 3 papers with code
Transformers4NewsRec: A Transformer-based News Recommendation Framework
Pre-trained transformer models have shown great promise in various natural language processing tasks, including personalized news recommendations.
Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain Hyperelasticity
The present study aims to extend the novel physics-informed machine learning approach, specifically the neural-integrated meshfree (NIM) method, to model finite-strain problems characterized by nonlinear elasticity and large deformations.
RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large Language Models
The results show LLM's effectiveness in accurately identifying topics of interest and delivering comprehensive topic-based explanations.
Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics
We present the neural-integrated meshfree (NIM) method, a differentiable programming-based hybrid meshfree approach within the field of computational mechanics.
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases
To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples.
Multi-Source Transfer Learning for Non-Stationary Environments
To speed up recovery from concept drift and improve predictive performance in data stream mining, this work proposes a novel approach called Multi-sourcE onLine TrAnsfer learning for Non-statIonary Environments (Melanie).
