Search Results for author: Thilo Stadelmann

Found 10 papers, 4 papers with code

A Theory of Natural Intelligence

no code implementations22 Apr 2022 Christoph von der Malsburg, Thilo Stadelmann, Benjamin F. Grewe

Introduction: In contrast to current AI technology, natural intelligence -- the kind of autonomous intelligence that is realized in the brains of animals and humans to attain in their natural environment goals defined by a repertoire of innate behavioral schemata -- is far superior in terms of learning speed, generalization capabilities, autonomy and creativity.

ImageNet as a Representative Basis for Deriving Generally Effective CNN Architectures

no code implementations16 Mar 2021 Lukas Tuggener, Jürgen Schmidhuber, Thilo Stadelmann

We investigate and improve the representativeness of ImageNet as a basis for deriving generally effective convolutional neural network (CNN) architectures that perform well on a diverse set of datasets and application domains.

Image Classification

Improving Sample Efficiency and Multi-Agent Communication in RL-based Train Rescheduling

no code implementations28 Apr 2020 Dano Roost, Ralph Meier, Stephan Huschauer, Erik Nygren, Adrian Egli, Andreas Weiler, Thilo Stadelmann

We present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement learning (RL) training efficiency, and two hypotheses with respect to the prospect of deep RL for complex real-world control tasks: first, that current state of the art policy gradient methods seem inappropriate in the domain of high-consequence environments; second, that learning explicit communication actions (an emerging machine-to-machine language, so to speak) might offer a remedy.

Policy Gradient Methods reinforcement-learning

Automated Machine Learning in Practice: State of the Art and Recent Results

no code implementations19 Jul 2019 Lukas Tuggener, Mohammadreza Amirian, Katharina Rombach, Stefan Lörwald, Anastasia Varlet, Christian Westermann, Thilo Stadelmann

A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions.

AutoML Decision Making

Deep Learning in the Wild

no code implementations13 Jul 2018 Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger, Stefan Lörwald, Benjamin Bruno Meier, Katharina Rombach, Lukas Tuggener

Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks.

Learning Neural Models for End-to-End Clustering

1 code implementation11 Jul 2018 Benjamin Bruno Meier, Ismail Elezi, Mohammadreza Amirian, Oliver Durr, Thilo Stadelmann

We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass.

Metric Learning

Deep Watershed Detector for Music Object Recognition

no code implementations26 May 2018 Lukas Tuggener, Ismail Elezi, Jurgen Schmidhuber, Thilo Stadelmann

Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functionality of any OMR pipeline.

Information Retrieval Music Information Retrieval +2

Speaker Clustering Using Dominant Sets

1 code implementation21 May 2018 Feliks Hibraj, Sebastiano Vascon, Thilo Stadelmann, Marcello Pelillo

We report on a comprehensive set of experiments on the TIMIT dataset against standard clustering techniques and specific speaker clustering methods.

Sound Audio and Speech Processing

DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects

2 code implementations27 Mar 2018 Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann

We present the DeepScores dataset with the goal of advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.

General Classification Object Recognition +2

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