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Found 6 papers, 3 papers with code
Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population
As the aging population grows, particularly for the baby boomer generation, the United States is witnessing a significant increase in the elderly population experiencing multifunctional disabilities.
The New Agronomists: Language Models are Experts in Crop Management
A novel aspect of our approach is the conversion of these state variables into more informative language, facilitating the language model's capacity to understand states and explore optimal management practices.
Residual-based Language Models are Free Boosters for Biomedical Imaging
In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of language or textual data.
Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection
The results indicate the effectiveness, good generalization ability, and great potential of adaptive ensemble algorithms in LLM-generated text detection.
SwitchTab: Switched Autoencoders Are Effective Tabular Learners
Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or semantic dependencies.
ReConTab: Regularized Contrastive Representation Learning for Tabular Data
In response to this challenge, we introduce ReConTab, a deep automatic representation learning framework with regularized contrastive learning.
