Q-Cogni: An Integrated Causal Reinforcement Learning Framework

Information Science, Computer Science, Artificial Intelligence,
causal inference   reinforcement learning   vehicle routing problem   autonomous causal structure discovery   model-free learning   model-based learning   interpretability  

Research Insight Report: Q-Cogni—An Integrated Causal Reinforcement Learning Framework In recent years, the rapid advancement of artificial intelligence (AI) has propelled researchers to explore the development of more efficient and interpretable reinforcement learning (RL) systems. Due to its ability to mimic human decision-making, reinforcement l...

Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation

Information Science, Computer Science, Artificial Intelligence,
Neural Machine Translation   Low-Resource Domain Adaptation   Curriculum Learning   Domain Shift   Robustness   Adaptability   Denoising  

Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation Research Background and Problem Statement In recent years, Neural Machine Translation (NMT) has become a benchmark technology in natural language processing. However, while NMT achieves near-human translation performance on large-scale parallel corpora, its effectivenes...

Enhancing Aerial Object Detection with Selective Frequency Interaction Network

Engineering, Aerospace Engineering, Information Science, Computer Science, Artificial Intelligence,
aerial object detection   frequency-domain features   selective frequency interaction   convolutional neural networks   feature extraction   deep learning   computer vision  

Selective Frequency Interaction Network for Improved Aerial Object Detection Background and Problem Statement With the advancements in computer vision, aerial object detection has become a critical research focus in remote sensing. This task aims to identify targets such as vehicles or buildings from aerial images captured at varying angles and alt...

RADIFF: Controllable Diffusion Models for Radio Astronomical Maps Generation

Physics, Astrophysics, Information Science, Computer Science, Artificial Intelligence,
diffusion models   radio astronomy   data augmentation   generative models   semantic segmentation  

RaDiff: Controllable Diffusion Models for Radio Astronomical Map Generation” Comprehensive Academic News Analysis Background Introduction With the near completion of the Square Kilometer Array (SKA) telescope, radio astronomy is poised for revolutionary advancements in the study of the universe. Boasting unprecedented sensitivity and spatial resolu...

Unsupervised Domain Adaptation on Point Clouds via High-Order Geometric Structure Modeling

Information Science, Computer Science, Artificial Intelligence,
point clouds   unsupervised domain adaptation   high-order geometric structures   contrastive learning   self-supervised learning   3D vision   feature alignment  

High-Order Geometric Structure Modeling-Based Unsupervised Domain Adaptation for Point Clouds Research Background and Motivation Point cloud data is a key data form for describing three-dimensional spaces, widely used in real-world applications such as autonomous driving and remote sensing. Point clouds can capture precise geometric information, bu...

AugDiff: Diffusion-Based Feature Augmentation for Multiple Instance Learning in Whole Slide Image

Information Science, Computer Science, Artificial Intelligence, Medicine, Oncology,
feature augmentation   diffusion model   multiple instance learning   whole slide image   computational pathology   deep learning   image generation  

Diffusion-Based Feature Augmentation: A Novel Approach for Multiple Instance Learning in Whole Slide Images Academic Background and Research Motivation In computational pathology, effectively analyzing Whole Slide Images (WSIs) is a burgeoning area of research. WSIs are ultra-high-resolution images with a broad field of view and are widely employed...