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pro vyhledávání: '"Rozell, Christopher"'
Autor:
Chen, Yenho, Mudrik, Noga, Johnsen, Kyle A., Alagapan, Sankaraleengam, Charles, Adam S., Rozell, Christopher J.
Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according
Externí odkaz:
http://arxiv.org/abs/2408.16862
Identifying latent interactions within complex systems is key to unlocking deeper insights into their operational dynamics, including how their elements affect each other and contribute to the overall system behavior. For instance, in neuroscience, d
Externí odkaz:
http://arxiv.org/abs/2404.18267
We analyze the popular ``state-space'' class of algorithms for detecting casual interaction in coupled dynamical systems. These algorithms are often justified by Takens' embedding theorem, which provides conditions under which relationships involving
Externí odkaz:
http://arxiv.org/abs/2308.06855
Self-supervised learning of deep neural networks has become a prevalent paradigm for learning representations that transfer to a variety of downstream tasks. Similar to proposed models of the ventral stream of biological vision, it is observed that t
Externí odkaz:
http://arxiv.org/abs/2306.13544
Deep generative models have the capacity to render high fidelity images of content like human faces. Recently, there has been substantial progress in conditionally generating images with specific quantitative attributes, like the emotion conveyed by
Externí odkaz:
http://arxiv.org/abs/2304.00185
When interacting in a three dimensional world, humans must estimate 3D structure from visual inputs projected down to two dimensional retinal images. It has been shown that humans use the persistence of object shape over motion-induced transformation
Externí odkaz:
http://arxiv.org/abs/2303.17776
Autor:
Loeffler, Christoffer, Fallah, Kion, Fenu, Stefano, Zanca, Dario, Eskofier, Bjoern, Rozell, Christopher John, Mutschler, Christopher
Publikováno v:
Transactions on Machine Learning Research 04/2023 https://openreview.net/forum?id=oq3tx5kinu
Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as proxy for similarity in information retrieval of similar instances. Learning a good similarity functio
Externí odkaz:
http://arxiv.org/abs/2207.12710
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 behavior. Models of neural dynamics often focus on either low-dimensional
Externí odkaz:
http://arxiv.org/abs/2206.02972
A brain-computer interface (BCI) is a system that allows a human operator to use only mental commands in controlling end effectors that interact with the world around them. Such a system consists of a measurement device to record the human user's bra
Externí odkaz:
http://arxiv.org/abs/2205.14265
Autor:
Fallah, Kion, Rozell, Christopher J.
Sparse coding strategies have been lauded for their parsimonious representations of data that leverage low dimensional structure. However, inference of these codes typically relies on an optimization procedure with poor computational scaling in high-
Externí odkaz:
http://arxiv.org/abs/2205.03665