GitHub

Virtual Sparse Convolution for Multimodal 3D Object Detection

VirConv-L: A light-weight multimodal 3D detector based on Virtual Sparse Convolution. VirConv-T: A improved multimodal 3D detector based on Virtual Sparse Convolution and transformed refinement scheme ...
GitHub

3D U-Net Convolution Neural Network

We designed 3DUnetCNN to make it easy to apply and control the training and application of various deep learning models to medical imaging data. The links above give examples/tutorials for how to use ...
IEEE

Convolution Constraints for Broadband Antenna Arrays

Abstract: Directional constraints are imposed on the weights of an antenna array to obtain solutions of some constrained beamforming problems. For broadband antenna arrays, formulation of a ...
Frontiers

A semi-supervised weighted SPCA- and convolution KAN-based model for drug response prediction

Predicting the response of cell lines to characteristic drugs based on multi-omics gene information has become the core problem of precision oncology. At present, drug response prediction using ...
IEEE

Multi-Scale Temporal Cues Learning for Video Person Re-Identification

Abstract: Temporal cues embedded in videos provide important clues for person Re-Identification (ReID). To efficiently exploit temporal cues with a compact neural network, this work proposes a novel ...
Frontiers

Accurate Tumor Segmentation via Octave Convolution Neural Network

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many ...
Scientific Research Publishing

ExpDes: An R Package for ANOVA and Experimental Designs ()

Analysis of variance (ANOVA) is a usual way for analysing experiments. However, depending on the design and/or the analysis scheme, it can be a hard task. ExpDes, acronym for Experimental Designs, is ...
PNAS

Homophily modulates double descent generalization in graph convolution networks

Graph neural networks (GNNs) have been applied with great success across science and engineering, but we do not understand why they work so well. Motivated by ...