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group-convolutional-neural-networks

Group Convolutional Neural Networks (G-CNNs) are a generalization of CNNs that reduces the data complexity by exploiting symmetries using Group Theory (you can learn more on the original paper at https://arxiv.org/abs/1602.07576). In this repository you will see the result of applying G-CNNs on the Fashion-MNIST dataset with 90° rotations

mcts

🚕 Explore the world of Monte Carlo Control, Monte Carlo Tree Search, and Rapid Action Value Estimation in this interactive tutorial tailored to the Taxi domain. Dive into detailed visual demos, accompanied by a comprehensive table tracking algorithmic metrics at each step. Perfect for beginners and enthusiasts alike!

pdf2csv

slam

SLAM with g2o: Generate g2o log files for Pose Graph Optimization (PGO) to refine robot trajectory and landmark positions. Utilizes AprilTags for landmark representation.

universal-adversarial-attack

🎯 Enhanced Adversarial Patch: Extends adversarial attacks on TartanVO model with a new loss function, rotation attack, and momentum optimizer