Search Results for author:
Found 5 papers, 3 papers with code
Conversational AI for Positive-sum Retailing under Falsehood Control
Retailing combines complicated communication skills and strategies to reach an agreement between buyer and seller with identical or different goals.
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning
In this work, we first demonstrate that sample complexity increases with the quantity of absorbing states in a toy supervised learning task for a fully connected network, while attention is more robust to variable size input.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Reading Between the Lines: Exploring Infilling in Visual Narratives
In this paper, we tackle this problem by using \textit{infilling} techniques involving prediction of missing steps in a narrative while generating textual descriptions from a sequence of images.
Induction and Reference of Entities in a Visual Story
We also conduct human evaluation from which it is concluded that the visual stories generated by our model are preferred 82% of the times.
Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks
Motivated by the observation that named entities are highly related to linguistic constituents, we propose a constituent-based BRNN-CNN for named entity recognition.
