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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
Short description of portfolio item number 1
Research Experience
During my master’s study, I was fascinated by social computing, when I started a project in Prof. Krcmar’s group at the Technical University of Munich about analyzing developers’ reactions towards any change in application programming interfaces (APIs). This project aimed to bridge the gap between API providers and consumers to benefit both of them by generating a list of optimal criteria an API provider should follow to gain the popularity in developers’ circles. I employed rule-based feature engineering for pinpointing those criteria over annotated conversations of developers. which turned out to be time-consuming and low quality. Through intensive research, I realized that the distributed representation of a word or sentence in a text is essential. This prompted me to explore neural network-based models over social media to figure out intrinsic patterns over conversations under the auspices of deep learning; inspiring me to join PD Dr. Georg Groh’s Social Computing group at the Technical University of Munich.
publications
An Evaluation of Progressive Neural Networks for Transfer Learning in Natural Language Processing
Published in International Conference on Language Resources and Evaluation (LREC), 2020
A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning. Fine-tuning, the most widely used method for achieving this, suffers from catastrophic forgetting. The problem is often exacerbated in natural language processing (NLP). In this work, we assess progressive neural networks (PNNs) as an alternative to fine-tuning. The evaluation is based on common NLP tasks such as sequence labeling and text classification. By gauging PNNs across a range of architectures, datasets, and tasks, we observe improvements over the baselines throughout all experiments.
Have a look here
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.