160 citations · 344 across the 9 of their papers we have counts for
4 papers · 1 filter
Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning
Saurabh Kumar, Pararth Shah, Dilek Hakkani-Tur +1
We present a framework combining hierarchical and multi-agent deep reinforcement learning approaches to solve coordination problems among a multitude of agents using a semi-decentr…
End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning
Bing Liu, Gokhan Tur, Dilek Hakkani-Tur +2
In this paper, we present a neural network based task-oriented dialogue system that can be optimized end-to-end with deep reinforcement learning (RL). The system is able to track d…
Towards Zero-Shot Frame Semantic Parsing for Domain Scaling
Ankur Bapna, Gokhan Tur, Dilek Hakkani-Tur +1
State-of-the-art slot filling models for goal-oriented human/machine conversational language understanding systems rely on deep learning methods. While multi-task training of such…
Learning and Evaluating Musical Features with Deep Autoencoders
Mason Bretan, Sageev Oore, Doug Eck +1
In this work we describe and evaluate methods to learn musical embeddings. Each embedding is a vector that represents four contiguous beats of music and is derived from a symbolic…