AI’s Memorization Crisis

O n Tuesday, researchers at Stanford and Yale revealed something that AI companies would prefer to keep hidden. Four popular ...
IEEE

A One-Shot Learning Approach for Fault Classification of Bearings via Multi-Autoencoder Reconstruction

Abstract: In industrial scenarios, the scarcity of labeled data for fault diagnosis of rotating machinery poses a significant challenge to the development of reliable data-driven models. This paper ...
Hosted on MSN

What LSTMs really do | Simple explanation

LSTM Recurrent Neural Network is a special version of the RNN model. It stands for Long Short-Term Memory. The simple RNN has a problem that it cannot remember the context in a long sentence because ...
GitHub

Diffusion Transformers with Representation Autoencoders (RAE)

We present Representation Autoencoders (RAE), a class of autoencoders that utilize pretrained, frozen representation encoders such as DINOv2 and SigLIP2 as encoders with trained ViT decoders. RAE can ...
GitHub

SVG: Latent Diffusion Model without Variational Autoencoder

SVG Autoencoder - Uses a frozen representation encoder with a residual branch to compensate the information loss and a learned convolutional decoder to transfer the SVG latent space to pixel space.
IEEE

Hybrid Approach Integrating Deep Learning-Autoencoder With Statistical Process Control Chart for Anomaly Detection: Case Study in Injection Molding Process

Abstract: Detecting anomalies in the injection molding process remains a challenging task, demanding significant resources, data, and expertise due to their impact on cost and time reduction. While ...
Hosted on MSN

A simple explanation of Bayes’ theorem anyone can understand

Get a simple explanation of Bayes’ Theorem that anyone can understand, even with no advanced math background. This video breaks the idea down using clear examples and intuitive reasoning to show how ...