Thomas Goerttler

Thomas Goerttler
Technische Universität Berlin | TUB

Master of Science

About

5
Publications
293
Reads
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4
Citations
Introduction
Thomas ist currently working at Technische Universität Berlin. Thomas does research in Statistics, Data Mining and Machine Learning. Thomas Goerttler worked at Hasso-Plattner Institute before. The former project was 'Missing value invariant data mining'.

Publications

Publications (5)
Article
Full-text available
Real-time load information in public transport is of high importance for both passengers and service providers. Neural algorithms have shown a high performance on various object counting tasks and play a continually growing methodological role in developing automated passenger counting systems. However, the publication of public-space video footage...
Conference Paper
In this article, we give an interactive introduction to model-agnostic meta-learning (MAML), a well-establish method in the area of meta-learning. Meta-learning is a research field that attempts to equip conventional machine learning architectures with the power to gain meta-knowledge about a range of tasks to solve problems like the one above on a...
Preprint
Full-text available
In past years model-agnostic meta-learning (MAML) has been one of the most promising approaches in meta-learning. It can be applied to different kinds of problems, e.g., reinforcement learning, but also shows good results on few-shot learning tasks. Besides their tremendous success in these tasks, it has still not been fully revealed yet, why it wo...
Conference Paper
Full-text available
In past years model-agnostic meta-learning (MAML) has been one of the most promising approaches in meta-learning. It can be applied to different kinds of problems, e.g., reinforcement learning, but also shows good results on few-shot learning tasks. Besides their tremendous success in these tasks, it has still not been fully revealed yet, why it w...
Conference Paper
Full-text available
Generative adversarial nets (GANs) have shown their potential in various tasks like image generation, 3D object generation, image super-resolution, and video prediction. Nevertheless, they are still considered as highly unstable to train and are endangered to miss modes. One problem is that real data is usually discontinuous, whereas the prior dist...

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Projects

Projects (2)
Project
Quantify Deep Networks - Towards Understanding of Transferable Representations We want to understand why deep networks learn and are transferable
Project
Traditional data mining techniques cannot be applied on incomplete datasets without requiring data imputation. The goal of the project is to adapt and develop new data mining algorithm which are invariant to missing data.