In this article, we propose an algorithm that combines actor–critic-based off-policy method with consensus-based distributed training to deal with multiagent deep reinforcement learning problems.

Delaware-based TheStage AI is changing this paradigm with their innovative approach to neural network optimization. The startup recently announced a $4.5 million funding round to commercialize ...

The current state-of-the-art time series modeling architectures include Recurrent Neural Networks (RNN), ordinary differential equation (ODE) based, and flow-matching methods. They have successfully ...

The phrase deep learning refers to that network depth, the hierarchical structure of the neural network on which today’s whole deep-learning revolution has been built.” ...

The training of neural networks (NNs) is a computationally intensive task requiring significant time and resources. This article presents a novel approach to NN training using adiabatic quantum ...

The result not only illuminates the inner workings of neural networks, but gestures toward the possibility of developing hyper-efficient algorithms that could classify images in a fraction of the ...

Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

Based on this, a PolSAR classification algorithm is designed. The whole proposed PolSAR classification algorithm includes three parts: the WLCE algorithm, the convolution neural network (NN) training ...