154 citations · 510 across the 52 of their papers we have counts for
9 papers · 1 filter
Deep Intrinsically Motivated Continuous Actor-Critic for Efficient Robotic Visuomotor Skill Learning
Muhammad Burhan Hafez, Cornelius Weber, Matthias Kerzel +1
In this paper, we present a new intrinsically motivated actor-critic algorithm for learning continuous motor skills directly from raw visual input. Our neural architecture is compo…
Discourse-Wizard: Discovering Deep Discourse Structure in your Conversation with RNNs
Chandrakant Bothe, Sven Magg, Cornelius Weber +1
Spoken language understanding is one of the key factors in a dialogue system, and a context in a conversation plays an important role to understand the current utterance. In this w…
Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks
Chandrakant Bothe, Sven Magg, Cornelius Weber +1
Recent approaches for dialogue act recognition have shown that context from preceding utterances is important to classify the subsequent one. It was shown that the performance impr…
A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
Chandrakant Bothe, Cornelius Weber, Sven Magg +1
Dialogue act recognition is an important part of natural language understanding. We investigate the way dialogue act corpora are annotated and the learning approaches used so far.…
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
German I. Parisi, Jun Tani, Cornelius Weber +1
Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability…
On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks
Egor Lakomkin, Mohammad Ali Zamani, Cornelius Weber +2
Speech emotion recognition (SER) is an important aspect of effective human-robot collaboration and received a lot of attention from the research community. For example, many neural…