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pro vyhledávání: '"RAO, RAJESH"'
Autor:
N'dri, Antony W., Gebhardt, William, Teulière, Céline, Zeldenrust, Fleur, Rao, Rajesh P. N., Triesch, Jochen, Ororbia, Alexander
In this article, we review a class of neuro-mimetic computational models that we place under the label of spiking predictive coding. Specifically, we review the general framework of predictive processing in the context of neurons that emit discrete a
Externí odkaz:
http://arxiv.org/abs/2409.05386
Autor:
Oliveira, Nigini, Li, Jasmine, Khalvati, Koosha, Barragan, Rodolfo Cortes, Reinecke, Katharina, Meltzoff, Andrew N., Rao, Rajesh P. N.
Constructing a universal moral code for artificial intelligence (AI) is difficult or even impossible, given that different human cultures have different definitions of morality and different societal norms. We therefore argue that the value system of
Externí odkaz:
http://arxiv.org/abs/2312.17479
In sampling-based Bayesian models of brain function, neural activities are assumed to be samples from probability distributions that the brain uses for probabilistic computation. However, a comprehensive understanding of how mechanistic models of neu
Externí odkaz:
http://arxiv.org/abs/2308.11809
Autor:
Salvatori, Tommaso, Mali, Ankur, Buckley, Christopher L., Lukasiewicz, Thomas, Rao, Rajesh P. N., Friston, Karl, Ororbia, Alexander
Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, the
Externí odkaz:
http://arxiv.org/abs/2308.07870
Autor:
Zhang, Sheng, Xu, Yanbo, Usuyama, Naoto, Xu, Hanwen, Bagga, Jaspreet, Tinn, Robert, Preston, Sam, Rao, Rajesh, Wei, Mu, Valluri, Naveen, Wong, Cliff, Tupini, Andrea, Wang, Yu, Mazzola, Matt, Shukla, Swadheen, Liden, Lars, Gao, Jianfeng, Lungren, Matthew P., Naumann, Tristan, Wang, Sheng, Poon, Hoifung
Biomedical data is inherently multimodal, comprising physical measurements and natural language narratives. A generalist biomedical AI model needs to simultaneously process different modalities of data, including text and images. Therefore, training
Externí odkaz:
http://arxiv.org/abs/2303.00915
Predictive coding has emerged as a prominent model of how the brain learns through predictions, anticipating the importance accorded to predictive learning in recent AI architectures such as transformers. Here we propose a new framework for predictiv
Externí odkaz:
http://arxiv.org/abs/2210.13461
Objective: A major challenge in designing closed-loop brain-computer interfaces is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and objectives. Approach: To achieve goal-directed closed-loop neu
Externí odkaz:
http://arxiv.org/abs/2210.11478
Inspired by Gibson's notion of object affordances in human vision, we ask the question: how can an agent learn to predict an entire action policy for a novel object or environment given only a single glimpse? To tackle this problem, we introduce the
Externí odkaz:
http://arxiv.org/abs/2207.03593
Autor:
Fisher, Ares, Rao, Rajesh P. N.
Human vision involves parsing and representing objects and scenes using structured representations based on part-whole hierarchies. Computer vision and machine learning researchers have recently sought to emulate this capability using capsule network
Externí odkaz:
http://arxiv.org/abs/2206.08462