28 citations · 41 across the 9 of their papers we have counts for
6 papers · 1 filter
Active Query Synthesis for Preference Learning
Namrata Nadagouda, Nauman Ahad, Maegan Tucker +1
Efficient learning of user preferences is crucial for many modern decision making systems but typically requires costly labeled data. Active learning reduces this cost, yet standar…
Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis
Mehdi Azabou, Michael Mendelson, Nauman Ahad +4
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 buildin…
Learning Behavior Representations Through Multi-Timescale Bootstrapping
Mehdi Azabou, Michael Mendelson, Maks Sorokin +4
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 rep…
Learning Sinkhorn divergences for supervised change point detection
Nauman Ahad, Eva L. Dyer, Keith B. Hengen +2
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 a…
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time
Feng Zhu, Andrew R. Sedler, Harrison A. Grier +5
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 s…
Semi-supervised sequence classification through change point detection
Nauman Ahad, Mark A. Davenport
Sequential sensor data is generated in a wide variety of practical applications. A fundamental challenge involves learning effective classifiers for such sequential data. While dee…