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Espace Probabilises

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10 documents available in our comprehensive collection of Espace Probabilises resources. Find practical guides, tutorials, and documentation to enhance your knowledge.

Preview of Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
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Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks (2106.00774.pdf)

5.64 MBEN 32 pages
Gradient flows are used to optimize functionals in probability spaces with the Wasserstein metric. A typical approach involves the Jordan-Kinderlehrer-Otto scheme, but this is challenging in high dimensions. We propose using input-convex neural networks to approximate the scheme.
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Preview of Practical solutions
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Practical solutions (exercises_solutions.pdf)

521 KBAuthorEN 7 pages
American Roulette has 38 spaces, and various bets can be placed with different payouts and probabilities.
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another one (ho1-probability.pdf)

147 KBEN 7 pages
The handout covers probability concepts, including sample spaces, events, random variables, and expectation.
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Preview of Introduction to probability.
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Introduction to probability. (5010Notes.pdf)

3.32 MBEN 229 pages
Math 5010 is a course on Introduction to Probability at the University of Utah, taught by Davar Khoshnevisan and Firas Rassoul-Agha.
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Preview of Stochastic Simulation
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Stochastic Simulation (etr077.pdf)

172 KBEN 7 pages
The stochastic simulation algorithm (SSA) is computationally expensive for models with many reaction channels and species, as it executes each reaction...
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pdf (2020.findings-emnlp.70.pdf)

1.18 MBHengyi Cai ; Hongshen Chen ; Yonghao Song ; zhuoye Ding ; Yongjun Bao ; Weipeng Yan ; Xiaofang ZhaoEN 10 pages
Neural dialogue response generation has gained popularity, but models trained with Maximum Likelihood Estimation (MLE) objective function suffer from...
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Preview of arXiv:0907.4010v1 [stat.CO] 23 Jul 2009
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arXiv:0907.4010v1 [stat.CO] 23 Jul 2009 (0907.4010v1.pdf)

101 KBEN 10 pages
It addresses challenges in Bayesian inference for models with censored or order-restricted parameters, where analytical computations are difficult. The methods utilize accept-reject sampling, Gibbs sampling, and Markov Chain Monte-Carlo techniques.
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Preview of mktscore.DVI
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mktscore.DVI (mktscore.pdf)

148 KBEN 12 pages
Document en en
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Preview of Deep Learning and Kernel Machines: RBMs, Deep BMs, RKMs, and Generative Models for Explainable AI
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Deep Learning and Kernel Machines: RBMs, Deep BMs, RKMs, and Generative Models for Explainable AI (deeplearn2.pdf)

841 KBEN 55 pages
Deep Learning and Kernel Machines by Johan Suykens introduces Restricted Boltzmann Machines (RBMs), Deep Boltzmann Machines (Deep BMs), Restricted Kernel Machines (RKMs), and Generative kernel PCA, focusing on explainable AI through generative models like RBMs, GANs, and deep...
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Preview of Inverse Sensor Mapping for Enhanced Agricultural Perception and Interoperability
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Inverse Sensor Mapping for Enhanced Agricultural Perception and Interoperability (agrob_2017_paper_1.pdf)

6.26 MBEN 6 pages
Agricultural applications of Industry 4.0 require accurate perception of surroundings for efficient processing and risk detection, moving beyond single-sensor approaches towards unified, multi-sensor environmental mapping. This paper explores inverse sensor mapping as a solution...
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