Now showing items 1-3 of 3
Manipulating state space distributions for sample-efficient imitation-learning
(Georgia Institute of Technology, 2020-03-16)
Imitation learning has emerged as one of the most effective approaches to train agents to act intelligently in unstructured and unknown domains. On its own or in combination with reinforcement learning, it enables agents ...
Interactive Scalable Interfaces for Machine Learning Interpretability
(Georgia Institute of Technology, 2020-12-01)
Data-driven paradigms now solve the world's hardest problems by automatically learning from data. Unfortunately, what is learned is often unknown to both the people who train the models and the people they impact. This has ...
Interval Deep Learning for Uncertainty Quantification in Engineering Problems
(Georgia Institute of Technology, 2021-05-06)
Deep neural networks are becoming more common in important real-world safety-critical applications where reliability in the predictions is paramount. Despite their exceptional prediction capabilities, current deep neural ...