Výsledky vyhledávání - "Hengen, Keith B."

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Learning Sinkhorn divergences for supervised change point detection

Autor: Ahad, Nauman, Dyer, Eva L., Hengen, Keith B., Xie, Yao, Davenport, Mark A.

Many modern applications require detecting change points in complex sequential data. Most existing methods for change point detection are unsupervised and, as a consequence, lack any information regarding what kind of changes we want to detect or if

Externí odkaz: http://arxiv.org/abs/2202.04000

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Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity

Autor: Liu, Ran, Azabou, Mehdi, Dabagia, Max, Lin, Chi-Heng, Azar, Mohammad Gheshlaghi, Hengen, Keith B., Valko, Michal, Dyer, Eva L.

Meaningful and simplified representations of neural activity can yield insights into how and what information is being processed within a neural circuit. However, without labels, finding representations that reveal the link between the brain and beha

Externí odkaz: http://arxiv.org/abs/2111.02338

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Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction

Autor: Azabou, Mehdi, Azar, Mohammad Gheshlaghi, Liu, Ran, Lin, Chi-Heng, Johnson, Erik C., Bhaskaran-Nair, Kiran, Dabagia, Max, Avila-Pires, Bernardo, Kitchell, Lindsey, Hengen, Keith B., Gray-Roncal, William, Valko, Michal, Dyer, Eva L.

State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample. Without sufficient diversity in the transformations used to create views, however, it ca

Externí odkaz: http://arxiv.org/abs/2102.10106

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