6 citations · 15 across the 6 of their papers we have counts for
6 papers
Behavior-dLDS: A decomposed linear dynamical systems model for neural activity partially constrained by behavior
Eva Yezerets, En Yang, Misha B. Ahrens +1
Brain-wide recordings of large-scale networks of neurons now provide an unprecedented view into how the brain drives behavior. However, brain activity contains both information dir…
Stable Filtering for Efficient Dimensionality Reduction of Streaming Manifold Data
Nicholas P. Bertrand, Eva Yezerets, Han Lun Yap +2
Many areas in science and engineering now have access to technologies that enable the rapid collection of overwhelming data volumes. While these datasets are vital for understandin…
LINOCS: Lookahead Inference of Networked Operators for Continuous Stability
Noga Mudrik, Eva Yezerets, Yenho Chen +2
Identifying latent interactions within complex systems is key to unlocking deeper insights into their operational dynamics, including how their elements affect each other and contr…
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Noga Mudrik, Yenho Chen, Eva Yezerets +2
Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and beha…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…
Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities
Ronan Perry, Ronak Mehta, Richard Guo +6
Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty. Current widely used approaches for co…