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20182022
most citedEfficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space

16 citations · 28 across the 10 of their papers we have counts for

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cs.LG20217 cited

FaVoA: Face-Voice Association Favours Ambiguous Speaker Detection

Hugo Carneiro, Cornelius Weber, Stefan Wermter

The strong relation between face and voice can aid active speaker detection systems when faces are visible, even in difficult settings, when the face of a speaker is not clear or w…

cs.LG2021

Generalization in Multimodal Language Learning from Simulation

Aaron Eisermann, Jae Hee Lee, Cornelius Weber +1

Neural networks can be powerful function approximators, which are able to model high-dimensional feature distributions from a subset of examples drawn from the target distribution.…

cs.LG20211 cited

Analyzing the Influence of Dataset Composition for Emotion Recognition

A. Sutherland, S. Magg, C. Weber +1

Recognizing emotions from text in multimodal architectures has yielded promising results, surpassing video and audio modalities under certain circumstances. However, the method by…

cs.LG2020

Improving Robot Dual-System Motor Learning with Intrinsically Motivated Meta-Control and Latent-Space Experience Imagination

Muhammad Burhan Hafez, Cornelius Weber, Matthias Kerzel +1

Combining model-based and model-free learning systems has been shown to improve the sample efficiency of learning to perform complex robotic tasks. However, dual-system approaches…

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.LG201916 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…