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Found 12 papers, 3 papers with code
Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancement
Deep learning has made significant progress in addressing challenges in various fields including computational pathology (CPath).
Tuning LLMs by RAG Principles: Towards LLM-native Memory
Memory, additional information beyond the training of large language models (LLMs), is crucial to various real-world applications, such as personal assistant.
FilterLLM: Text-To-Distribution LLM for Billion-Scale Cold-Start Recommendation
Specifically, we present FilterLLM, a framework that extends the next-word prediction capabilities of LLMs to billion-scale filtering tasks.
When Graph meets Multimodal: Benchmarking on Multimodal Attributed Graphs Learning
However, the absence of meaningful benchmark datasets and standardized evaluation procedures for MAG representation learning has impeded progress in this field.
Training Interactive Agent in Large FPS Game Map with Rule-enhanced Reinforcement Learning
To address the challenges of navigation and combat in modern 3D FPS games, we introduce a method that combines navigation mesh (Navmesh) and shooting-rule with deep reinforcement learning (NSRL).
Breaking the mold: The challenge of large scale MARL specialization
This paper introduces Comparative Advantage Maximization (CAM), a method designed to enhance individual agent specialization in multiagent systems.
Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator
Owing to the unprecedented capability in semantic understanding and logical reasoning, the pre-trained large language models (LLMs) have shown fantastic potential in developing the next-generation recommender systems (RSs).
Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting
First, we extract the content from an image; then, we verbalize the extracted content into a report that matches the style of a specific radiologist.
sasdim: self-adaptive noise scaling diffusion model for spatial time series imputation
Spatial time series imputation is critically important to many real applications such as intelligent transportation and air quality monitoring.
Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem
Next, a task transfer strategy is established to select seeds from source tasks and correct unsuitable knowledge in seeds to suppress negative transfer.
MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing
Video-language models (VLMs), large models pre-trained on numerous but noisy video-text pairs from the internet, have revolutionized activity recognition through their remarkable generalization and open-vocabulary capabilities.
Ranked #2 on
Few Shot Action Recognition
on MOMA-LRG
(using extra training data)
EMA-VIO: Deep Visual-Inertial Odometry with External Memory Attention
Accurate and robust localization is a fundamental need for mobile agents.
