Selected recent doctoral thesis projects
Mengdi Li
Active Vision for Embodied Agents Using Reinforcement Learning (2024)
Leyuan Qu
Neural Network Learning for Robust Speech Recognition (2021)
Tayfun Alpay
Periodicity, Surprisal, Attention: Skip Conditions for Recurrent Neural Networks (2021)
Tobias Hinz
Disentanglement, Compositionality, Specification: Representation Learning with Generative Adversarial Networks (2021)
Chandrakant Bothe
Conversational Language Learning for Human-Robot Interaction (2020)
Johannes Twiefel
Robust Bidirectional Processing for Speech-controlled Robotic Scenarios (2020)
Muhammad Burhan Hafez
Intrinsically Motivated Actor-Critic for Robot Motor Learning (2020)
Nils Meins
Diversity-driven Hopfield Neural Network Ensembles for Face Detection (2019)
Jorge Dávila Chacón
Biomimetic Computation and Embodied Embedded Cognition for Spatial Audition in Humanoids (2019)
Luiza Mici
Unsupervised Learning of Human-Object Interactions with Neural Network Self-Organization (2018)
Francisco Javier Cruz Naranjo
Teaching Robots With Interactive Reinforcement Learning (2017)
Doreen Jirak
Inspection of Echo State Networks for Dynamic Gestures (2017)
We investigate Echo State Networks (ESN), which implement a new training paradigm for recurrent neural networks. We first demonstrate their gesture classification performance considering two feature sets with very distinct complexity. Second, we introduce the recurrence analysis for qualitative and quantitative description of the gesture input and the system dynamics of an ESN, and show that the methodology complements classic stability measures. Finally, we address the reservoir itself and propose an algorithm for pruning connectivity in a one-shot learning scenario. |