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Found 10 papers, 6 papers with code
A Two Stage Adaptation Framework for Frame Detection via Prompt Learning
Frame detection aims to automatically analyze framing strategy.
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering
Proteins are essential to life's processes, underpinning evolution and diversity.
DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation
We propose DISC-MedLLM, a comprehensive solution that leverages Large Language Models (LLMs) to provide accurate and truthful medical response in end-to-end conversational healthcare services.
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations
Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient.
A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience.
Curriculum Learning for Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) is a task where an agent navigates in an embodied indoor environment under human instructions.
Automatic Term Name Generation for Gene Ontology: Task and Dataset
Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine.
Hierarchical Reinforcement Learning for Automatic Disease Diagnosis
Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks.
Deep Feature Learning of Multi-Network Topology for Node Classification
Network Embedding, aiming to learn non-linear and low-dimensional feature representation based on network topology, has been proved to be helpful on tasks of network analysis, especially node classification.
