Spiking neural networks (SNNs) running on neuromorphic computers offer an energy-efficient alternative for AI tasks. Recently, spiking graph neural networks (S-GNNs) have been shown to produce ...
This repository contains a PyTorch implementation of the Lottery Ticket algorithm introduced by Frankle et al. in "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" [1] and ...
ROS, or Robot Operating System, is an open-source framework designed to facilitate the development of robotic applications. It provides a collection of tools, libraries, and conventions that simplify ...
Artificial Intelligence (AI) has transformed how we interact with technology, but at its core, AI relies on a fundamental building block: tensors. Think of tensors as the unsung heroes that make data ...
Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan ...
TAIM: Tool for Analyzing Root Images to Calculate the Infection Rate of Arbuscular Mycorrhizal Fungi
Arbuscular mycorrhizal fungi (AMF) infect plant roots and are hypothesized to improve plant growth. Recently, AMF is now available for axenic culture. Therefore, AMF is expected to be used as a ...
In the last few years, spiking neural networks (SNNs) have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained ...
Five ILSVRC-2010 test images in the first column. Remaining columns show the training images that produce feature vectors in the last hidden layer with the smallest Euclidean distance from the feature ...
High-throughput analysis of animal behavior requires software to analyze videos. Such software analyzes each frame individually, detecting animals’ body parts. But the image analysis rarely attempts ...
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