A tiny optical booster built directly onto a chip is pointing toward a future where light, not just electricity, does the ...

Edge AI addresses high-performance, low-latency requirements by embedding intelligence directly into industrial devices.

Neural relation extraction aims to extract relations from plain text with neural models, which has been the state-of-the-art methods for relation extraction. In this project, we provide our ...

🎮 Train a Deep Q-Learning agent using TensorFlow to master Atari Breakout with efficient experience replay and modular architecture for easy customization.

Abstract: This study investigates the application of Spiking Neural Network (SNN) in seismic signal denoising by developing a Convolutional Neural Network (CNN) to SNN conversion framework. We focus ...

In this tutorial, we take a hands-on approach to building an advanced convolutional neural network for DNA sequence classification. We focus on simulating real biological tasks, such as promoter ...

1 Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia 2 Department of Learning, Data Analytics and Technology, Section Cognition, Data and ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

Researchers in China have created a dataset of various PV faults and normalized it to accommodate different array sizes and typologies. After testing the new approach in combination with the 1D-CNN ...