Search Results for author: Nauman Ahad

Found 6 papers, 2 papers with code

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction

1 code implementation1 Jan 2023 Jorge Quesada, Lakshmi Sathidevi, Ran Liu, Nauman Ahad, Joy M. Jackson, Mehdi Azabou, Jingyun Xiao, Christopher Liding, Matthew Jin, Carolina Urzay, William Gray-Roncal, Erik C. Johnson, Eva L. Dyer

To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image.

Attribute Semantic Segmentation

Learning Behavior Representations Through Multi-Timescale Bootstrapping

no code implementations14 Jun 2022 Mehdi Azabou, Michael Mendelson, Maks Sorokin, Shantanu Thakoor, Nauman Ahad, Carolina Urzay, Eva L. Dyer

Natural behavior consists of dynamics that are both unpredictable, can switch suddenly, and unfold over many different timescales.

Disentanglement

Learning Sinkhorn divergences for supervised change point detection

no code implementations8 Feb 2022 Nauman Ahad, Eva L. Dyer, Keith B. Hengen, Yao Xie, Mark A. Davenport

We present a novel change point detection framework that uses true change point instances as supervision for learning a ground metric such that Sinkhorn divergences can be then used in two-sample tests on sliding windows to detect change points in an online manner.

Change Detection Change Point Detection +1

Semi-supervised sequence classification through change point detection

no code implementations24 Sep 2020 Nauman Ahad, Mark A. Davenport

We show that change points provide examples of similar/dissimilar pairs of sequences which, when coupled with labeled, can be used in a semi-supervised classification setting.

Change Point Detection Classification +2

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