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Found 9 papers, 5 papers with code
Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation
Specifically, we introduce a multi-view longitudinal contrastive learning method that integrates spatial information from current multi-view images and temporal information from longitudinal data.
Towards More Accurate Full-Atom Antibody Co-Design
Antibody co-design represents a critical frontier in drug development, where accurate prediction of both 1D sequence and 3D structure of complementarity-determining regions (CDRs) is essential for targeting specific epitopes.
Fast and Accurate Antibody Sequence Design via Structure Retrieval
Recent advancements in protein design have leveraged diffusion models to generate structural scaffolds, followed by a process known as protein inverse folding, which involves sequence inference on these scaffolds.
MCL: Multi-view Enhanced Contrastive Learning for Chest X-ray Report Generation
Radiology reports are crucial for planning treatment strategies and enhancing doctor-patient communication, yet manually writing these reports is burdensome for radiologists.
Drone Data Analytics for Measuring Traffic Metrics at Intersections in High-Density Areas
This study employed over 100 hours of high-altitude drone video data from eight intersections in Hohhot to generate a unique and extensive dataset encompassing high-density urban road intersections in China.
KPG: Key Propagation Graph Generator for Rumor Detection based on Reinforcement Learning
In this paper, we propose Key Propagation Graph Generator (KPG), a novel reinforcement learning-based rumor detection framework that generates contextually coherent and informative propagation patterns for events with insufficient topology information, while also identifies indicative substructures for events with redundant and noisy propagation structures.
Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation
In Stage 1, we introduce factuality-guided contrastive learning for visual representation by maximizing the semantic correspondence between radiographs and corresponding factual descriptions.
Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning
Lane changes are complex driving behaviors and frequently involve safety-critical situations.
Learning Based Proximity Matrix Factorization for Node Embedding
Recent progress on node embedding shows that proximity matrix factorization methods gain superb performance and scale to large graphs with millions of nodes.
