A Variational Autoencoder (VAE) is a generative model that combines principles from deep learning and probabilistic modeling. This project trains a VAE to generate Fashion MNIST images and visualize ...

Poetry VAE: A Variational Autoencoder for Diverse Poetry Generation A deep learning implementation of a Variational Autoencoder (VAE) for generating diverse and creative poetry using PyTorch. This ...

In generative modeling, tokenization simplifies complex data into compact, structured representations, creating a more efficient, learnable space. For high-dimensional visual data, it reduces ...

Moreover, we present an adaptive weighted Transformer, with the weights guided by a variational autoencoder (VAE), thereby enhancing the generalization and robustness of the model. The SGA and VAET ...

Black-box discrete optimization (BB-DO) problems arise in many real-world applications, such as neural architecture search and mathematical model estimation. A key challenge in BB-DO is epistasis ...

FIGURE 1. (A) Structure of a standard autoencoder (AE) with three hidden layers. The middle layer represents the latent space. Curves over the units represent a nonlinear activation function. (B) ...