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Found 13 papers, 1 papers with code
Vertex-based Networks to Accelerate Path Planning Algorithms
Path planning plays a crucial role in various autonomy applications, and RRT* is one of the leading solutions in this field.
Joint ANN-SNN Co-training for Object Localization and Image Segmentation
The field of machine learning has been greatly transformed with the advancement of deep artificial neural networks (ANNs) and the increased availability of annotated data.
Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation
Over the past decade, artificial neural networks (ANNs) have made tremendous advances, in part due to the increased availability of annotated data.
Individualized Conditioning and Negative Distances for Speaker Separation
Speaker separation aims to extract multiple voices from a mixed signal.
A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models
In this paper, we explore the capabilities of a number of deep neural network models in generating whole-brain 3T-like MR images from clinical 1. 5T MRIs.
Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning
In this work, we proposed several novel agent state and reward function designs to tackle two critical issues in DRL-based navigation solutions: 1) smoothness of the trained flight trajectories; and 2) model generalization to handle unseen environments.
Identifying Autism Spectrum Disorder Based on Individual-Aware Down-Sampling and Multi-Modal Learning
To further recalibrate the distribution of the extracted features under phenotypic information, we subsequently embed the sparse feature vectors into a population graph, where the hidden inter-subject heterogeneity and homogeneity are explicitly expressed as inter- and intra-community connectivity differences, and utilize Graph Convolutional Networks to learn the node embeddings.
Trace-back along capsules and its application on semantic segmentation
In this paper, we propose a capsule-based neural network model to solve the semantic segmentation problem.
Residual Pyramid FCN for Robust Follicle Segmentation
In this paper, we propose a pyramid network structure to improve the FCN-based segmentation solutions and apply it to label thyroid follicles in histology images.
TraceCaps: A Capsule-based Neural Network for Semantic Segmentation
We model this procedure as a traceback pipeline and take it as a central piece to build an end-to-end segmentation network.
Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation
In this paper, we develop a two-stage neural network solution for the challenging task of white-matter lesion segmentation.
Nonlinear Metric Learning through Geodesic Interpolation within Lie Groups
In this paper, we propose a nonlinear distance metric learning scheme based on the fusion of component linear metrics.
Nonlinear Metric Learning for kNN and SVMs through Geometric Transformations
In recent years, research efforts to extend linear metric learning models to handle nonlinear structures have attracted great interests.
