This project provides an unsupervised learning solution for detecting anomalies in time-series data, specifically demonstrated on synthetic network traffic. It leverages a Long Short-Term Memory (LSTM ...

This project explores segmentation and reconstruction of noisy synthetic particles (circles with triangle distractors) using a convolutional autoencoder (VAE-style latent representation). It includes ...

Abstract: In this paper we present a new implementation of a Variational Autoencoder (VAE) for the calibration of sensors. We propose that the VAE can be used to calibrate sensor data by training the ...

Abstract: Recently masked autoencoder (MAE) has achieved great success in visual representation learning and delivered promising potential in many downstream vision tasks. However, due to the lack of ...

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Written in Rust, the PyApp utility wraps up Python programs into self-contained click-to-run executables. It might be the easiest Python packager yet. Every developer knows how hard it is to ...