Predicting Uncertainty with Bayesian Neural Networks on MNIST Dataset
Least Confident PredictionsDescription
The objective of the project is class predictions with uncertainty with an implementation of a Bayesian Neural Network, trained and tested on the Rotated MNIST and Fashion MNIST datasets. The training process minimizes a loss function that considers both a Cross-Entropy loss and a Kullback-Leibler divergence loss term.
Chenhao Li
Reinforcement Learning for Robotics
My research interests focus on the general field of robot learning, including reinforcement learning, developmental robotics and legged intelligence.