We propose a personalized car-following strategy framework based on a one-dimensional convolutional variational autoencoder (conVAE) and mildly conservative Q-learning (MCQ). The conVAE extracts ...

with the aim of building Variational Quantum Circuits embedded in CV architecture. In this way, it is possible not only to quantumize classical neural networks, but also to design various ...

This study investigates the use of Variational Autoencoder (VAE)-derived features from electrocardiogram (ECG) data for predicting dementia, offering a cost-effective alternative to traditional ...

Here are the most common: Convolutional neural networks (CNNs ... AI techniques include generative adversarial networks (GANs), variational autoencoders (VAEs), and large language models ...

Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional ... topic these days because they are the architecture of LLMs like ChatGPT and ...

This model is optimized implementation of Variational Autoencoder proposed in Variational Autoencoders for Collaborative Filtering paper. Figure 1. The architecture of an Autoencoder. The following ...

how to select the neural-network architecture; and how synthetic data can improve convolutional-neural-network performance. The concept of a perception neural network was first described as ...