Inference algorithms
Inference algorithms for the regression approach to sequence prediction
... There is a significant difference between our results and the results of other approaches on the word recognition task. While finding the exact pre-image can improve the results, this differ- ence can also be attributed ...
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(Hyper)-Graphs Inference via Convex Relaxations and Move Making Algorithms: Contributions and Applications in artificial vision
... mathematical inference problems where the objective is to determine the set of parameters corresponding to the lowest potential of a task-specific objective ...efficient inference algorithms they ...
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Attention as a Bayesian inference process
... network, inference can be performed using any of several inference algorithms such as junction tree, variable elimination, MCMC (Markov-chain Monte carlo) and belief ...the inference mechanism ...
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Recurrent machines for likelihood-free inference
... 1 Introduction Modern science often relies on the modeling of complex data generation process by means of computer simulators. While the forward generation of observables is often straightforward and well- motivated, ...
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Cutting plane algorithms for variational inference in graphical models
... ational inference algorithm for probabilistic inference in discrete Markov Random Fields ...probabilistic inference, showing how certain results in either field can carry over to the ...
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Composable Probabilistic Inference with Blaise
... Carlo inference algorithms seek to produce states ...Carlo inference, also differs from classical search algorithms in how solution states are ...search algorithms such as A* typically ...
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Bayesian inference with optimal maps
... In this paper, we present a novel approach to Bayesian inference that relies on the construction of maps. We entirely avoid Markov chain simulation, and instead explic- itly construct a map that pushes forward the ...
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Composable inference metaprogramming using subproblems
... The key challenge is converting the entangled subproblem (which is typically incomplete and therefore not a valid trace of any program) into a valid trace by transf[r] ...
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Causal inference : a Tensor's perspective
... In fact, across the different SC variants, it is typically assumed that a synthetic control for the target unit exists within the universe of donors (this is the fundamental hypothesis t[r] ...
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Active inference of network neutrality
... by performing passive measurements and using causal inference to quantify the causal relationship between an ISP and the observed service degradation. Causal inference. Let the action taken by an ISP be ...
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Causal Inference and Impact Evaluation
... The recent publication of several reference articles and books also shows just how developed and diverse econometric evaluation methods have become. 4 These include the books by Imbens & Rubin (2015), Lee (2016), and ...
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A Survey on Internet Topology Inference
... Unité de recherche INRIA Sophia Antipolis 2004, route des Lucioles - BP 93 - 06902 Sophia Antipolis Cedex France Unité de recherche INRIA Futurs : Parc Club Orsay Université - ZAC des Vi[r] ...
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Partial evaluation with inference rules
... L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r] ...
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Polymorphic type inference and assignment
... L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r] ...
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Inference for the Generalization Error
... the inference procedure as gures in this section are the most informative in that ...an inference procedure that is not liberal, the obvious choice is the conservative Z ...in inference procedure ...
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Revisiting Subnet Inference WISE-ly
... We however note two things. First, WISE processes back- wards the addresses list. Indeed, many of our observations showed that contra-pivots are usually found among the first subnet addresses. Going backwards therefore ...
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Statistical Inference for Oscillation Processes
... t|t(`) are computed with the Kalman smoother. Smoothing by marginalization has been criticized for causing sample impoverishment (Doucet et.al., 1999). While this is true in general, it is not an issue in the setting of ...
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Variational inference for probabilistic Poisson PCA
... Organization of the paper is as follows: in Section 2 we introduce pPCA for the exponential family and the variational framework considered. Section 3 generalizes the model in the manner of a generalized linear model, in ...
24
Ontology-based inference for causal explanation
... an inference system to capture explanations based on causal statements, using an ontology in the form of an IS-A ...formal inference patterns from causal statements to explanation ...an inference ...
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Accelerating CNN inference on FPGAs: A Survey
... 5.1.1 Fixed point arithmetic: In a general way, CNN models are deployed in CPUs and GPUs using the same numerical precision they were trained with, relying on simple-precision floating point representation. This format ...
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