Projects

Winning NeurIPS competition track entry (student leaderboard), on the ML4CO configuration task. We compile a large dataset of the solver performance and train a novel graph neural network that learns to predict a good configuration for a specific instance.

A highly optimized implementation of the FastSLAM algorithm achieving 60x speedup by using AVX (assembly) instructions, data restructuring and extensive benchmarking.

Using multi-level convolutional activations, we increase the robustness of local feature descriptors using a pretrained ResNet backbone with an attention layer for keypoint selection, which we train directly on the quality of the keypoints.