Search Results for author:
Found 9 papers, 5 papers with code
QUILL: Quotation Generation Enhancement of Large Language Models
To improve the LLMs' quotation generation abilities, we construct a bilingual knowledge base that is broad in scope and rich in dimensions, containing up to 32, 022 quotes.
Can Large Language Models Understand Real-World Complex Instructions?
To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
A bioinspired three-stage model for camouflaged object detection
Furthermore, considering the significance of multi-scale information, we have designed a multi-scale feature enhancement module that enlarges the receptive field while preserving detailed structural cues.
Accurate RNA 3D structure prediction using a language model-based deep learning approach
Accurate prediction of RNA three-dimensional (3D) structure remains an unsolved challenge.
ProNet DB: A proteome-wise database for protein surface property representations and RNA-binding profiles
The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures challenge users in computational biology for utilizing the structural information and protein surface property representation.
Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations.
An Expert System for Redesigning Software for Cloud Applications
When services are divided into many independent components, they are easier to update.
A Smartphone-based System for Real-time Early Childhood Caries Diagnosis
The identification of ECC in an early stage usually requires expertise in the field, and hence is often ignored by parents.
Multi-Domain Learning for Accurate and Few-Shot Color Constancy
Given a new unseen device with limited number of training samples, our method is capable of delivering accurate color constancy by merely learning the camera-specific parameters from the few-shot dataset.
