6 days ago · We propose and analyze a numerical algorithm for solving a class of optimal control problems for learning-informed semilinear partial differential equations ...

2 hours ago · The training process is an iterative update of the synaptic connection weights. The straightforward way is to model the process as a discrete dynamical system, ...

6 days ago · The pyramidal training approach is proposed to the PINN community as a dual-impact method: it facilitates the initialization of training when dealing with ...

4 days ago · Training an ANN is to iteratively adjust the values of the parameters W to optimize its goal function . The purpose of gradient descent algorithms is to ...

4 days ago · The variational approach for BNNs involves selecting prior distributions and approximate posterior distributions over neural network weights, offering a more ...

4 days ago · In this study, a neural-network-based traffic signal control optimization method under the MPC framework is proposed. A dynamic correlation matrix is introduced ...

2 days ago · The schedule for 2024 is still being finalized. Recheck this page again closer to the conference start date. Show Detail.

4 days ago · In this work, we propose a neural network autofocus method with the capability of dynamically selecting the region of interest (ROI). Our main work is as ...

6 days ago · Gradient descent can be applied to various machine learning algorithms, including linear regression, logistic regression, neural networks, and support vector ...

6 days ago · Episodic control aims to assist Deep Reinforcement Learning (DRL) agents in making appropriate decisions by leveraging past experiences in unseen environments.