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This is the repository with the baseline code for the "Small Object Detection Challenge for Spotting Birds" at MVA2023.

Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution (CSSR) was accepted to international conference on MVA2021 (oral), and selected for the Best Practical Paper Award.

This project explores deep reinforcement learning, hybrid actor-critic approach with A3C/PPO combined with curiosity for playing Super Mario Bros

Labs and homeworks done during the Master Mathematics, Vision, Learning (MVA) at ENS Paris-Saclay.

Convert the MVA charts from faa.gov into KML files and content packs for Foreflight

This repository contains all the code samples of "Developing Intelligent Bots from Zero to Hero" channel9/MVA course.

Official repository for the MVA2025 SMOT4SB Challenge. Provides dataset, baseline code, and evaluation scripts for the Small Multi-Object Tracking for Spotting Birds (SMOT4SB) competition, focusing on UAV-based small bird tracking.

Assignments and Projects from the MVA Master at ENS Paris-Saclay

Probabilistic graphical models home works (MVA - ENS Cachan)

Implementation of Forward Initial Margin Calculation with ISDA SIMM for MVA. This Project is based on finmath-lib: https://github.com/finmath/finmath-lib and makes use of Adjoint Algorithmic Differentiation (AAD) provided here: https://github.com/finmath/finmath-lib-automaticdifferentiation-extensions

MVA - Kaggle Challenge - Bird Image Recognition

Notes from the “Advanced Tools & Scripting with PowerShell 3.0” jumpstart, originally published in Microsoft MVA. Now available in Channel 9: https://channel9.msdn.com/Series/advpowershell3

Secure and anonymous messaging application. Fully decentralized peer-peer and fully anonymous.

Work done for the Fall 2018 class of Convex Optimization at MVA Master's degree

Unsepervised learning - MVA course 2017/2018

Tercer proyecto del curso Lenguajes para aplicaciones comerciales de la carrera de Informatica Empresarial de la UCR.

Work done for the Fall 2018 class of Probabilistic Graphical Models at MVA Master's degree