Personalized course recommendations represent a significant problem in extensive e-learning platforms, as student preferences are in constant flux and educational environments are increasingly ...
How VAEs improve over vanilla autoencoders, a working 3-hidden-layer implementation, and a practical blueprint for defect detection in industrial coils. Variational Autoencoders (VAEs) are ...
Autoencoders and Variational Autoencoders often look almost identical in diagrams, an encoder, a latent space, and a decoder, but the difference between them completely changes what these models can ...
├── src/vae/ # Core VAE implementation │ ├── models.py # VAE model architectures │ ├── data.py # Data loading and preprocessing │ ├── training.py # Training utilities and Lightning module │ ├── ...
Turbulent flows are an important and ubiquitous phenomenon in nature and engineering, with applications ranging from aircraft design to weather forecasting. Understanding the behaviour of fluid flows ...
Variational Autoencoders (VAEs) are an artificial neural network architecture to generate new data. They are similar to regular autoencoders, which consist of an encoder and decoder. The encoder takes ...
Abstract: We conducted an in-depth investigation into the impact of Conditional Variational Autoencoders (CVAE) and Bayesian Neural Networks (BNN) on high dynamic range (HDR) image reconstruction. A ...
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