Dr. Maja Rudolph

(Bosch Center for Artificial Intelligence, Pittsburgh)
hosted by Machine Learning Group of Prof. Marius Kloft

"Self-Supervised Learning beyond Images and Text"

Self-supervised learning has emerged as a powerful paradigm for machine learning, especially for drawing insights from unlabeled data. The key idea is to introduce auxiliary prediction tasks and to train a deep model to solve these auxiliary tasks. If the tasks are designed well, the trained model will be useful for a number of purposes such as anomaly detection, feature extraction, and forecasting. Unfortunately, most successful approaches for SSL rely on domain-specific indictive biases and are therefore limited to individual use-cases. In this talk, I present advanced self-supervised learning losses that facilitate domain-general self-supervised learning beyond images and text. Exponential family embeddings, for example, generalize word embeddings to provide insight into a wide range of applications. They are a useful tool for studying zebrafish brains in neuro science, for studying shopping behavior in economics, or for studying language evolution in computational social science. Similarly, neural transformation learning (NTL), is a new general-purpose tool for self-supervised anomaly detection. While related methods in computer vision typically require image transformations such as rotations, blurring, or flipping, NTL automatically learns the best transformations from the data and therefore generalizesself-supervised AD to almost any data type.

Bio: As a Senior Research Scientist at the Bosch Center for AI, Maja Rudolph develops machine learning methodsfor drawing valuable insights from unlabeled, noisy data. The domain-general methods she has developed can be usedto identify interpretable patterns, form scientific theories, and detect anomalies.She holds a Ph.D. in computer science from Columbia University and a BS in mathematics from MIT.

Time: Thursday, 09.02.2023, 14:00
Place: room 48/680

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