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Good-enough brain model - ACM Digital Library

In this work we present a simple, novel good-enough brain model, or GeBM in short, and a novel algorithm Sparse-SysId, which are able to effectively model the dynamics of the neuron interactions and infer the functional connectivity.

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Extraction of Event Structures from Text - lti.cs.cmu.edu

2019-8-1 · Acknowledgments I have been greatly fortunate to have interacted with excellent individuals who have had a profound impact on my Ph.D. training. In this note of acknowledgment, I

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Metadata: May 2018 - muratbuffalo.blogspot.com

2019-8-22 · We wrapped up the distributed systems seminar with two more papers discussed last month. The "SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies" paper appeared in 2015. Although the cryptocurrency scene has seen a lot

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Proceedings of the 54th Annual Meeting of the Association

2016-8-1 · Preface: Program Committee Co-Chairs Welcome to the 54th Annual Meeting of the Association for Computational Linguistics! This year, ACL received 825 long paper submissions (a new record) and 463 short paper submissions. 1 Of the long papers, 2

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Victoria's ML Notes - persagen.com

2019-1-14 · We believe this to be largely due to the lack of objective ways to measure progress towards broad machine intelligence. In order to fill this gap, we propose here a set of concrete desiderata for general AI, together with a platform to test ma

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2019-9-5 · We build concrete batch plants, mixers, dust collectors, water meters

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The No-U-turn sampler: adaptively setting path lengths in

Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient informati

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rust never sleeps: 2015 - blogspot.com

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(PDF) -ADVANCED MACHINE LEARNING WITH PYTHON (1)

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On Sampling Strategies for Neural Network-based

Muhao Chen , Yingtao Tian , Kai-Wei Chang , Steven Skiena , Carlo Zaniolo, Co-training embeddings of knowledge graphs and entity descriptions for cross-lingual entity alignment, Proceedings of the 27th International Joint Conference on Artificial Intellig

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