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Sebastian Jäger

Sebastian Jäger
Berliner Hochschule für Technik

Master of Science
Currently working on conformal predictors and applying them to data quality and data cleaning problems.

About

7
Publications
328
Reads
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22
Citations

Publications

Publications (7)
Conference Paper
Full-text available
The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Machine Learning (ML) can help to foster sustainable consumption patterns by accounting for sustainability aspects in product search or recommendations of modern retail platforms. However, the lack of...
Article
Full-text available
A transition toward a sustainable way of living is more pressing than ever. One link to achieving this transition is to increase the currently low level of sustainable consumption, and sustainability labeling has been shown to directly influence sustainable purchasing decisions. E-commerce retailers have recently picked up on a means to inform onli...
Preprint
Full-text available
The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Modern retail platforms rely heavily on Machine Learning (ML) for their search and recommender systems. Thus, ML can potentially support efforts towards more sustainable consumption patterns, for exam...
Article
Full-text available
With the increasing importance and complexity of data pipelines, data quality became one of the key challenges in modern software applications. The importance of data quality has been recognized beyond the field of data engineering and database management systems (DBMSs). Also, for machine learning (ML) applications, high data quality standards are...
Thesis
Using pre-trained Language Models (LMs), such as ELMo, BERT, or GPT models show good empirical results on a wide range of Natural Language Processing (NLP) downstream tasks. However, these large models consist of millions or even billions of parameters, making training and inference slow and computationally expensive, especially in resource-constra...
Conference Paper
Horizontal scalability is a major facilitator of recent advances in deep learning. Common deep learning frameworks offer different approaches for scaling the training process. We operationalize the execution of distributed training using Kubernetes and helm templates. This way we lay ground for a systematic comparison of deep learning frameworks. F...
Thesis
In dieser Arbeit werden generische Deep Learning Frameworks für die Programmiersprache Python auf deren horizontale Skalierbarkeit beim Training von Neuronalen Netzen experimentell evaluiert. Hierzu werden zuerst Kriterien definiert, die dafür von Nutzen sind, um TensorFlow und MXNet für die Evaluation auszuwählen. Um eine möglichst umfangreiche Be...

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