Modeling Conversational Systems with a Spoken Dialogue Model – Inference of conversational language from spoken utterances is a challenge in spoken dialogue, which has been discussed in numerous works. This paper investigates the task of inferring a conversational phrase from language utterances. We formulate this task as a dialogue system where the system learns a translation vector and then a natural language translation. To the best of our knowledge, this paper shows that our approach can accurately infer a conversational phrase from speech utterances, and not from language. We present an application of this approach to a conversational dialogue system by using a text-to-speech system.
The goal of this paper is to provide an efficient and robust implementation of a new distributed inference methodology that is able to capture and model the dependencies among agents. We describe the algorithm and the implementation for a new policy architecture, which supports many agents, including many robots. We also discuss the possibility of a future vision for our methodology, which is based on learning to reason.
Fuzzy Classification of Human Activity with the Cyborg Astrobiologist on the Web
The Classification, GAN and Supervised Learning of Movement Recognition Systems
Modeling Conversational Systems with a Spoken Dialogue Model
Learning to Rank for Nonverbal Instruction in Instructional Videos
Convergent Inference Policies for Reinforcement LearningThe goal of this paper is to provide an efficient and robust implementation of a new distributed inference methodology that is able to capture and model the dependencies among agents. We describe the algorithm and the implementation for a new policy architecture, which supports many agents, including many robots. We also discuss the possibility of a future vision for our methodology, which is based on learning to reason.