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Found 8 papers, 1 papers with code
A Quantitative Approach for Evaluating Disease Focus and Interpretability of Deep Learning Models for Alzheimer's Disease Classification
To bridge such gap, in this study, we developed a quantitative disease-focusing strategy to first enhance the interpretability of DL models using saliency maps and brain segmentations; then we propose a disease-focus (DF) score that quantifies how much a DL model focuses on brain areas relevant to AD pathology based on clinically known MRI-based pathological regions of AD.
Robo360: A 3D Omnispective Multi-Material Robotic Manipulation Dataset
Building robots that can automate labor-intensive tasks has long been the core motivation behind the advancements in computer vision and the robotics community.
Reparameterized Policy Learning for Multimodal Trajectory Optimization
We then present a practical model-based RL method, called Reparameterized Policy Gradient (RPG), which leverages the multimodal policy parameterization and learned world model to achieve strong exploration capabilities and high data efficiency.
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction
Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction.
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
On a set of 26 benchmark Atari environments, MeanQ outperforms all tested baselines, including the best available baseline, SUNRISE, at 100K interaction steps in 16/26 environments, and by 68% on average.
Target Entropy Annealing for Discrete Soft Actor-Critic
Soft Actor-Critic (SAC) is considered the state-of-the-art algorithm in continuous action space settings.
Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates
Under the belief that $\beta$ is closely related to the (state dependent) model uncertainty, Entropy Regularized Q-Learning (EQL) further introduces a principled scheduling of $\beta$ by maintaining a collection of the model parameters that characterizes model uncertainty.
Modular Framework for Visuomotor Language Grounding
Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research.
