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Found 9 papers, 1 papers with code
Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning
Learning informative representations from image-based observations is of fundamental concern in deep Reinforcement Learning (RL).
Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks
Despite its great success, deep learning severely suffers from robustness; that is, deep neural networks are very vulnerable to adversarial attacks, even the simplest ones.
Reinforcement Learning with Predictive Consistent Representations
Learning informative representations from image-based observations is a fundamental problem in deep Reinforcement Learning (RL).
EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos
The popularity of racket sports (e. g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance.
Parallel ensemble methods for causal direction inference
Inferring the causal direction between two variables from their observation data is one of the most fundamental and challenging topics in data science.
Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations
Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD).
Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics
The results show that the AUC obtained from clinical demographics alone was 0. 635 while the attention network alone reached an accuracy of 0. 687.
Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols
Recently, we obtained a clinically acquired, multi-sequence MRI brain cohort with 1480 clinically acquired, de-identified brain MRI scans on 395 patients using seven different MRI protocols.
Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning
From the results, the proposed AID-Net achieved the superior performance on classification accuracy (0. 9272) and AUC (0. 9627).
