Repository for ReVel framework to Measure Local-Linear Explanationsfor Black-Box Models

Local Attention Mechanism for time series forecasting.

Fast and memory-efficient exact attention

This project extends the idea of the innovative architecture of Kolmogorov-Arnold Networks (KAN) to the Convolutional Layers, changing the classic linear transformation of the convolution to learna…

Anomaly detection using Federated Learning with FLEX.

Federated Learning (FL) experiment simulation in Python.

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

A Python toolkit for rule-based/unsupervised anomaly detection in time series

An open-source, low-code machine learning library in Python

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

Time series forecasting with PyTorch

Time Series Feature Extraction using Deep Learning

The lean application framework for Python. Build sophisticated user interfaces with a simple Python API. Run your apps in the terminal and a web browser.

Time-series Industrial Anomaly dataset

Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.

Text completion using the GPT-3 API.

Distance Metric Learning Algorithms for Python

Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!

Temporal generalization of ROC curves for weakly labelled anomalies in time-series scenarios.

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Visualizer for neural network, deep learning and machine learning models

HungaBunga: Brute-Force all sklearn models with all parameters using .fit .predict!

Python wrapper function for the benchmark functions of the CEC 2017 Special Session and Competition on Single Objective Bound Constrained Real-Parameter Numerical Optimization.

Introducción a autoencoders y aplicación a detección anomalías

Supplementary software for the paper 'An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges'

Supplementary source code for the paper "A Showcase of the Use of Autoencoders for Feature Learning Applications"

Prototype Selection and Generation Toolbox based on scikit-learn