An organoid-based screening platform that allows one-gene-at-a-time knockdown across a whole tissue has been used to identify the genes that regulate closure of the neural tube in humans.

Recently, researchers introduced a new representation learning framework that integrates causal inference with graph neural networks—CauSkelNet, which can be used to model the causal relationships and ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy ...

Understanding the brain's functional architecture is a fundamental challenge in neuroscience. The connections between neurons ...

Abstract: Recent neural networks based surface reconstruction can be roughly divided into two categories, one warping templates explicitly and the other representing 3D surfaces implicitly. To enjoy ...

This is a general purpose aimbot, which uses a neural network for enemy/target detection. The aimbot doesn't read/write memory from/to the target process. It is essentially a "pixel bot", designed ...

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech ...

Abstract: Due to their synaptic-like characteristics and memory properties, memristors are often used in neuromorphic circuits, particularly neural network circuits. However, most of the existing ...

in this video, we will understand what is Recurrent Neural Network in Deep Learning. Recurrent Neural Network in Deep Learning is a model that is used for Natural Language Processing tasks. It can be ...

This repository contains a time series forecasting project using the Google Play Store dataset. It systematically compares RNN, LSTM, and GRU models, optimized via Keras Tuner, to predict future app ...