GitHub

CNN-AutoEncoder in pytorch

This is a pytorch implementation of the Muti-task Learning using CNN + AutoEncoder. Cifar10 is available for the datas et by default. You can also use your own dataset. epoch,train loss,train accuracy ...
GitHub

SVG-T2I: Scaling up Text-to-Image Latent Diffusion Model

Important Note: This repository implements SVG-T2I, a text-to-image diffusion framework that performs visual generation directly in Visual Foundation Model (VFM) representation space, rather than ...
IEEE

Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder

Abstract: A fault detection method for power conversion circuits using thermal images and a convolutional autoencoder is presented. The autoencoder is trained on thermal images captured from a ...
Scientific Research Publishing

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J. et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information ...

ABSTRACT: Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management. Conventional asset pricing models, such as the Fama-French three-factor ...
IEEE

Conv4Rec: A 1-by-1 Convolutional Autoencoder for User Profiling Through Joint Analysis of Implicit and Explicit Feedback

Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...
VentureBeat

DeepMind makes big jump toward interpreting LLMs with sparse autoencoders

Large language models (LLMs) have made remarkable progress in recent years. But understanding how they work remains a challenge and scientists at artificial intelligence labs are trying to peer into ...