Now showing items 1-5 of 5
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 ...
Learning dynamic processes over graphs
(Georgia Institute of Technology, 2020-07-09)
Graphs appear as a versatile representation of information across domains spanning social networks, biological networks, transportation networks, molecular structures, knowledge networks, web information network and many ...
Learning neural algorithms with graph structures
(Georgia Institute of Technology, 2020-01-13)
Graph structures, like syntax trees, social networks, and programs, are ubiquitous in many real world applications including knowledge graph inference, chemistry and social network analysis. Over the past several decades, ...
EvalAI: Evaluating AI systems at scale
(Georgia Institute of Technology, 2018-12-06)
Artificial Intelligence research has progressed tremendously in the last few years. There has been the introduction of several new multi-modal datasets and tasks due to which it is becoming much harder to compare new ...
Towards tighter integration of machine learning and discrete optimization
(Georgia Institute of Technology, 2019-03-28)
Discrete Optimization algorithms underlie intelligent decision-making in a wide variety of domains. From airline fleet scheduling to data center resource management and matching in ride-sharing services, decisions are often ...