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Found 10 papers, 1 papers with code
N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems
Conventional finite element methods are known to be tedious in adaptive refinements due to their conformal regularity requirements.
On Solving Close Enough Orienteering Problem with Overlapped Neighborhoods
To tackle CEOP and CEOP-N, we develop a new approach featuring a Randomized Steiner Zone Discretization (RSZD) scheme coupled with a hybrid algorithm based on Particle Swarm Optimization (PSO) and Ant Colony System (ACS), CRaSZe-AntS.
Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis
After a number of extensive experiments, the experimental results show that the proposed model achieves 99. 06% accuracy in the two-class classification task of tooth-marked tongue identification and 80. 02%.
Data-Driven Modeling of an Unsaturated Bentonite Buffer Model Test Under High Temperatures Using an Enhanced Axisymmetric Reproducing Kernel Particle Method
In deep geological repositories for high level nuclear waste with close canister spacings, bentonite buffers can experience temperatures higher than 100 {\deg}C. In this range of extreme temperatures, phenomenological constitutive laws face limitations in capturing the thermo-hydro-mechanical (THM) behavior of the bentonite, since the pre-defined functional constitutive laws often lack generality and flexibility to capture a wide range of complex coupling phenomena as well as the effects of stress state and path dependency.
Safe Reinforcement Learning as Wasserstein Variational Inference: Formal Methods for Interpretability
Reinforcement learning can provide effective reasoning for sequential decision-making problems with variable dynamics.
Support Vector Machine Guided Reproducing Kernel Particle Method for Image-Based Modeling of Microstructures
The proposed method modifies the smooth kernel functions with a regularized heavy-side function concerning the material interfaces to alleviate Gibb's oscillations.
Trustworthy Reinforcement Learning for Quadrotor UAV Tracking Control Systems
We propose a novel, interpretable trajectory tracker integrating a Distributional Reinforcement Learning disturbance estimator for unknown aerodynamic effects with a Stochastic Model Predictive Controller (SMPC).
Distributional Reinforcement Learning
reinforcement-learning
+1
Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems
The proposed framework integrates a distributional Reinforcement Learning (RL) estimator for unknown aerodynamic effects into a Stochastic Model Predictive Controller (SMPC) for trajectory tracking.
Distributional Reinforcement Learning
Model Predictive Control
+1
Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks
Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease.
Efficient Video Object Segmentation via Network Modulation
Video object segmentation targets at segmenting a specific object throughout a video sequence, given only an annotated first frame.
Ranked #1 on
One-shot visual object segmentation
on YouTube-VOS 2018
(Jaccard (Seen) metric)
