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Home Research Publications People Code/Resources Contact us Home Research Publications People Code/Resources Contact us See our Publications tab for a more comprehensive list of papers and preprints. Our research is supported by funding from the NIH (NIBIB and NIA) and NSF. Brain networks are typically modeled as interactions between neural elements. We have developed a framework for generating networks based on the interactivity of edges. This enables us to investigate circuit-level interactions with exqui
Posted on 7/11/2011 by RichardSocher # Interesting Neural Network Papers at ICML 2011 Maybe it’s too early to call, but with four separate Neural Network sessions at this year’s ICML , it looks like Neural Networks are making a comeback. Here are my highlights of these sessions. In general, my feeling is that these papers both demystify deep learning and show its broader applicability. The first observation I made is that the once disreputable “Neural” nomenclature is being used again in lieu of
https://www.techopedia.com/how-are-logic-gates-precursors-to-ai-and-building-blocks-for-neural-networks/7/33018 Share
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If you're looking to improve the interpretability of your neural networks, you may want to consider using Pytorch LRP. In this blog post, we'll show you how
NeurIPS Proceedings Search Rapid Quality Estimation of Neural Network Input Representations Kevin J. Cherkauer, Jude W. Shavlik Advances in Neural Information Processing Systems 8 (NIPS 1995) Abstract The choice of an input representation for a neural network can have a profound impact on its accuracy in classifying novel instances. However, neural networks are typically computationally expensive to train, making it difficult to test large numbers of alternative representations. This paper introduces fast q
A team of neuroscientists at the Champalimaud Centre for the Unknown, in Lisbon, has been able to map single neural connections over long distances in the brain. "These are the first measurements of neural inputs between local circuits and faraway sites", says Leopoldo Petreanu, who led the research. In doing so, Petreanu and co-authors Nicolás Morgenstern and Jacques Bourg have also discovered that the wiring of the brain is more complex than previously thought. Their results have been published in the
A statistical physics framework decodes large-scale neural activity by identifying key connections among thousands of neurons, including the feedback loops others ignore
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