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Nauman Ahad

5 accepted papers

2023

Relax, it doesn’t matter how you get there: A new self-supervised approach for multi-timescale behavior analysis

Mehdi Azabou, Michael Jacob Mendelson, Nauman Ahad, Maks Sorokin, Shantanu Thakoor, Carolina Urzay +1
NeurIPS 2023spotlight

Unconstrained and natural behavior consists of dynamics that are complex and unpredictable, especially when trying to predict what will happen multiple steps into the future. While some success has been found in building representations of animal behavior under constrained or simplified task-base…

Cited by 8Source ↗PDF ↗Scholar ↗
2022

Delta Distancing: A Lifting Approach to Localizing Items from User Comparisons

Andrew D. McRae, Austin Xu, Jihui Jin, Namrata Nadagouda, Nauman Ahad, Peimeng Guan +2
ICASSP 2022accepted

A common problem in recommendation systems is to learn a model of user preferences based only on comparisons of the relative attractiveness of different items. We consider this problem in the context of an ideal point model of user preference, where each user can be represented as a point in a low-d…

Cited by 0Source ↗Scholar ↗
2022

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

Jorge Quesada, Lakshmi Sathidevi, Ran Liu, Nauman Ahad, Joy M Jackson, Mehdi Azabou +7
NeurIPS 2022accept

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2021

Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time

Feng Zhu, Andrew R Sedler, Harrison A Grier, Nauman Ahad, Mark A. Davenport, Matthew Kaufman +2
NeurIPS 2021poster

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obt…

Cited by 14Source ↗PDF ↗code ↗Scholar ↗
2021

Semi-supervised Sequence Classification through Change Point Detection

Nauman Ahad, Mark A. Davenport
AAAI 2021technical

Sequential sensor data is generated in a wide variety of real-world applications. A fundamental machine learning challenge involves learning effective classifiers for such sequential data. While deep learning has led to impressive performance gains in recent years within domains such as speech, this…

Cited by 12Source ↗PDF ↗code ↗Scholar ↗