Researchers at the National University of Singapore (NUS) have fabricated ultra-thin memtransistor arrays from ...

TinyML is an incredibly powerful piece of software, and you can easily train your own model and deploy it on an ESP32.

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

Abstract: Equivariant quantum graph neural networks (EQGNNs) offer a potentially powerful method to process graph data. However, existing EQGNN models only consider the permutation symmetry of graphs, ...

Understanding the brain's functional architecture is a fundamental challenge in neuroscience. The connections between neurons ultimately dictate how information is processed, transmitted, stored, and ...

This project contains implementations of simple neural network models, including training scripts for PyTorch and Lightning frameworks. The goal is to provide a modular, easy-to-understand codebase ...

Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of the ...

The study investigates using 3D neural organoids made from human pluripotent stem cells (hPSCs) to assess neurotoxicity and neural function. Organoids were imaged to measure spontaneous calcium ...

Abstract: This paper introduces an innovative approach utilizing a deep neural network (DNN) to optimize the modulation scheme for time-modulated antenna array (TMAA) to verify specific side lobe and ...

Background and objectives: This paper introduces a novel lightweight MM-3DUNet (Multi-task Mobile 3D UNet) network designed for efficient and accurate segmentation of breast cancer tumors masses from ...