Now showing items 1-4 of 4
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 ...
Human-guided task transfer for interactive robots
(Georgia Institute of Technology, 2020-07-06)
Adaptability is an essential skill in human cognition, enabling us to draw from our extensive, life-long experiences with various objects and tasks in order to address novel problems. To date, robots do not have this kind ...
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 ...