Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Chandra, Sarthak"'
Autor:
Schaeffer, Rylan, Zahedi, Nika, Khona, Mikail, Pai, Dhruv, Truong, Sang, Du, Yilun, Ostrow, Mitchell, Chandra, Sarthak, Carranza, Andres, Fiete, Ila Rani, Gromov, Andrey, Koyejo, Sanmi
Associative memory and probabilistic modeling are two fundamental topics in artificial intelligence. The first studies recurrent neural networks designed to denoise, complete and retrieve data, whereas the second studies learning and sampling from pr
Externí odkaz:
http://arxiv.org/abs/2402.10202
A hallmark of biological intelligence and control is combinatorial generalization: animals are able to learn various things, then piece them together in new combinations to produce appropriate outputs for new tasks. Inspired by the ability of primate
Externí odkaz:
http://arxiv.org/abs/2210.02521
Recurrent neural networks (RNNs) are often used to model circuits in the brain, and can solve a variety of difficult computational problems requiring memory, error-correction, or selection [Hopfield, 1982, Maass et al., 2002, Maass, 2011]. However, f
Externí odkaz:
http://arxiv.org/abs/2207.03523
Content-addressable memory (CAM) networks, so-called because stored items can be recalled by partial or corrupted versions of the items, exhibit near-perfect recall of a small number of information-dense patterns below capacity and a 'memory cliff' b
Externí odkaz:
http://arxiv.org/abs/2202.00159
Autor:
Eisen, Adam J., Kozachkov, Leo, Bastos, André M., Donoghue, Jacob A., Mahnke, Meredith K., Brincat, Scott L., Chandra, Sarthak, Tauber, John, Brown, Emery N., Fiete, Ila R., Miller, Earl K.
Publikováno v:
In Neuron 21 August 2024 112(16):2799-2813
Publikováno v:
Phys. Rev. E 101, 062304 (2020)
Network science is a rapidly expanding field, with a large and growing body of work on network-based dynamical processes. Most theoretical results in this area rely on the so-called \emph{locally tree-like approximation}. This is, however, usually an
Externí odkaz:
http://arxiv.org/abs/1905.07433
Autor:
Chandra, Sarthak, Ott, Edward
We consider a recently introduced $D$-dimensional generalized Kuramoto model for many $(N\gg 1)$ interacting agents in which the agent states are $D$-dimensional unit vectors. It was previously shown that, for even $D$, similar to the original Kuramo
Externí odkaz:
http://arxiv.org/abs/1902.05159
Previous results have shown that a large class of complex systems consisting of many interacting heterogeneous phase oscillators exhibit an attracting invariant manifold. This result has enabled reduced analytic system descriptions from which all the
Externí odkaz:
http://arxiv.org/abs/1809.00783
Publikováno v:
Phys. Rev. X 9, 011002 (2019)
The Kuramoto model, originally proposed to model the dynamics of many interacting oscillators, has been used and generalized for a wide range of applications involving the collective behavior of large heterogeneous groups of dynamical units whose sta
Externí odkaz:
http://arxiv.org/abs/1806.01314
Autor:
Pathak, Jaideep, Wikner, Alexander, Fussell, Rebeckah, Chandra, Sarthak, Hunt, Brian, Girvan, Michelle, Ott, Edward
A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the physical processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated p
Externí odkaz:
http://arxiv.org/abs/1803.04779