Zobrazeno 1 - 10
of 189
pro vyhledávání: '"Krishnan, Giri P"'
Publikováno v:
2024 International Joint Conference on Neural Networks (IJCNN)
Artificial neural networks (ANNs) show limited performance with scarce or imbalanced training data and face challenges with continuous learning, such as forgetting previously learned data after new tasks training. In contrast, the human brain can lea
Externí odkaz:
http://arxiv.org/abs/2410.16154
Embeddings produced by pre-trained deep neural networks (DNNs) are widely used; however, their efficacy for downstream tasks can vary widely. We study the factors influencing transferability and out-of-distribution (OOD) generalization of pre-trained
Externí odkaz:
http://arxiv.org/abs/2405.15018
The performance of artificial neural networks (ANNs) degrades when training data are limited or imbalanced. In contrast, the human brain can learn quickly from just a few examples. Here, we investigated the role of sleep in improving the performance
Externí odkaz:
http://arxiv.org/abs/2402.10956
Autor:
Hayes, Tyler L., Krishnan, Giri P., Bazhenov, Maxim, Siegelmann, Hava T., Sejnowski, Terrence J., Kanan, Christopher
Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a
Externí odkaz:
http://arxiv.org/abs/2104.04132
Sleep plays an important role in incremental learning and consolidation of memories in biological systems. Motivated by the processes that are known to be involved in sleep generation in biological networks, we developed an algorithm that implements
Externí odkaz:
http://arxiv.org/abs/1908.02240
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Tacad, Debra K M, Tovar, Ashley P, Richardson, Christine E, Horn, William F, Krishnan, Giri P, Keim, Nancy L *, Krishnan, Sridevi *
Publikováno v:
In Advances in Nutrition May 2022 13(3):792-820
Autor:
Tacad, Debra K M, Tovar, Ashley P, Richardson, Christine E, Horn, William F, Keim, Nancy L, Krishnan, Giri P, Krishnan, Sridevi *
Publikováno v:
In Advances in Nutrition May 2022 13(3):758-791
Measuring functional connectivity from fMRI is important in understanding processing in cortical networks. However, because brain's connection pattern is complex, currently used methods are prone to produce false connections. We introduce here a new
Externí odkaz:
http://arxiv.org/abs/1711.03000
With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship between multiple neural signals. Correlation-based methods are a set of currently
Externí odkaz:
http://arxiv.org/abs/1706.02451