activity
20172026
most citedSurvey on reinforcement learning for language processing

154 citations · 510 across the 52 of their papers we have counts for

collaborators
Showing 2019Show all

7 papers · 1 filter

cs.CL2019★ 2 cited

EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators

Chandrakant Bothe, Cornelius Weber, Sven Magg +1

The recognition of emotion and dialogue acts enriches conversational analysis and help to build natural dialogue systems. Emotion interpretation makes us understand feelings and di…

cs.LG2019

Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search

Mehmet Süzen, J. J. Cerdà, Cornelius Weber

Establishing associations between the structure and the generalisation ability of deep neural networks (DNNs) is a challenging task in modern machine learning. Producing solutions…

cs.LG2019★ 16 cited

Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space

Muhammad Burhan Hafez, Cornelius Weber, Matthias Kerzel +1

Combining model-based and model-free deep reinforcement learning has shown great promise for improving sample efficiency on complex control tasks while still retaining high perform…

cs.CV2019

What can computational models learn from human selective attention? A review from an audiovisual crossmodal perspective

Di Fu, Cornelius Weber, Guochun Yang +6

Selective attention plays an essential role in information acquisition and utilization from the environment. In the past 50 years, research on selective attention has been a centra…

cs.LG2019

Curious Meta-Controller: Adaptive Alternation between Model-Based and Model-Free Control in Deep Reinforcement Learning

Muhammad Burhan Hafez, Cornelius Weber, Matthias Kerzel +1

Recent success in deep reinforcement learning for continuous control has been dominated by model-free approaches which, unlike model-based approaches, do not suffer from representa…

cs.CL2019★ 2 cited

KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos

Egor Lakomkin, Sven Magg, Cornelius Weber +1

In this paper, we describe KT-Speech-Crawler: an approach for automatic dataset construction for speech recognition by crawling YouTube videos. We outline several filtering and pos…