no code implementations • 15 Mar 2023 • Mehdi Azabou, Michael Mendelson, Nauman Ahad, Maks Sorokin, Shantanu Thakoor, Carolina Urzay, Eva L. Dyer
Natural behavior consists of dynamics that are complex and unpredictable, especially when trying to predict many steps into the future.
1 code implementation • 1 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.
no code implementations • 14 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.
no code implementations • 8 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.
1 code implementation • NeurIPS 2021 • Feng Zhu, Andrew R. Sedler, Harrison A. Grier, Nauman Ahad, Mark A. Davenport, Matthew T. Kaufman, Andrea Giovannucci, Chethan Pandarinath
We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data.
no code implementations • 24 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.