Abstract: This article investigates the problems of exponential stability and dissipativity for neural networks with time-varying delays. To capture more information on the delay and its derivative in ...

OpenAI researchers are experimenting with a new approach to designing neural networks, with the aim of making AI models easier to understand, debug, and govern. Sparse models can provide enterprises ...

Abstract: In this paper, we consider the design of model predictive control (MPC) algorithms based on deep operator neural networks (DeepONets) (Lu et al. 2021). These neural networks are capable of ...

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...

3D rendering—the process of converting three-dimensional models into two-dimensional images—is a foundational technology in computer graphics, widely used across gaming, film, virtual reality, and ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead ...

Blockchain networks, like any complex software system, require regular improvements to remain secure, scalable, and functional. These improvements often come in the ...

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 ...

1 School of Smart Health Care, Zhejiang Dongfang Polytechnic, Wenzhou, China 2 Nursing Teaching and Research Department, Wenzhou People's Hospital, Wenzhou, China Introduction: Postpartum depression ...