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pro vyhledávání: '"Ahad, Nauman"'
Autor:
Azabou, Mehdi, Mendelson, Michael, Ahad, Nauman, Sorokin, Maks, Thakoor, Shantanu, Urzay, Carolina, Dyer, Eva L.
Natural behavior consists of dynamics that are complex and unpredictable, especially when trying to predict many steps into the future. While some success has been found in building representations of behavior under constrained or simplified task-bas
Externí odkaz:
http://arxiv.org/abs/2303.08811
Autor:
Quesada, Jorge, Sathidevi, Lakshmi, Liu, Ran, Ahad, Nauman, Jackson, Joy M., Azabou, Mehdi, Xiao, Jingyun, Liding, Christopher, Jin, Matthew, Urzay, Carolina, Gray-Roncal, William, Johnson, Erik C., Dyer, Eva L.
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 buil
Externí odkaz:
http://arxiv.org/abs/2301.00345
Detecting change points sequentially in a streaming setting, especially when both the mean and the variance of the signal can change, is often a challenging task. A key difficulty in this context often involves setting an appropriate detection thresh
Externí odkaz:
http://arxiv.org/abs/2210.17353
Autor:
Azabou, Mehdi, Mendelson, Michael, Sorokin, Maks, Thakoor, Shantanu, Ahad, Nauman, Urzay, Carolina, Dyer, Eva L.
Natural behavior consists of dynamics that are both unpredictable, can switch suddenly, and unfold over many different timescales. While some success has been found in building representations of behavior under constrained or simplified task-based co
Externí odkaz:
http://arxiv.org/abs/2206.07041
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
Autor:
Zhu, Feng, Sedler, Andrew R., Grier, Harrison A., Ahad, Nauman, Davenport, Mark A., Kaufman, Matthew T., Giovannucci, Andrea, Pandarinath, Chethan
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 sampli
Externí odkaz:
http://arxiv.org/abs/2111.00070
Autor:
Ahad, Nauman, Davenport, Mark A.
Sequential sensor data is generated in a wide variety of practical applications. A fundamental challenge involves learning effective classifiers for such sequential data. While deep learning has led to impressive performance gains in recent years in
Externí odkaz:
http://arxiv.org/abs/2009.11829
Publikováno v:
EURASIP JWCN, October, 2014
In recent times, there have been a lot of efforts for improving the ossified Internet architecture in a bid to sustain unstinted growth and innovation. A major reason for the perceived architectural ossification is the lack of ability to program the
Externí odkaz:
http://arxiv.org/abs/1310.0251
Akademický článek
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Publikováno v:
Sequential Analysis; 2024, Vol. 43 Issue 1, p1-27, 27p
