Results 21 to 30 of about 25,214,365 (363)
Variational methods for the Dirichlet process [PDF]
Twenty-first international conference on Machine learning - ICML '04, 2004 Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While often less accurate than MCMC, variational methods provide a fast deterministic approximation to marginal and conditional probabilities.
David M. Blei, Michael I. Jordan
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Novel level shifter based physical unclonable function circuit design
网络与信息安全学报, 2021 Level shifters are widely used in low-power, multi-threshold integrated circuit chips.A novel physical unclonable function (PUF) design based on cross-coupled level shifter was proposed.In this work, a single switching transistor was inserted in the ...
Lijuan HAN +5 more
doaj +3 more sources
Modeling the Impact of Process Variation on Resistive Bridge Defects [PDF]
, 2010 Recent research has shown that tests generated without taking process variation into account may lead to loss of test quality. At present there is no efficient device-level modeling technique that models the effect of process variation on resistive ...
Robert Aitken +9 more
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IEEE Access, 2020 In-memory computing (IMC) is a promising approach for energy cost reduction due to data movement between memory and processor for running data-intensive deep learning applications on the computing systems.
Gobinda Saha +5 more
doaj +1 more source
Monitoring process variation using modified EWMA
Quality and Reliability Engineering International, 2019 A new control chart, namely, modified exponentially weighted moving average (EWMA) control chart, for monitoring the process variance is introduced in this work by following the recommendations of Khan et al.15 The proposed control chart deduces the ...
A. Saghir +4 more
semanticscholar +1 more source
Spatio-Temporal Variational Gaussian Processes [PDF]
CoRR, 2021 Peer ...
Hamelijnck, Oliver +4 more
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Variational sums for additive processes [PDF]
Proceedings of the American Mathematical Society, 1976 Let X ( t ) , 0
Hudson, William N., Mason, J. David
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Stein Variational Gaussian Processes [PDF]
CoRR, 2020 We show how to use Stein variational gradient descent (SVGD) to carry out inference in Gaussian process (GP) models with non-Gaussian likelihoods and large data volumes. Markov chain Monte Carlo (MCMC) is extremely computationally intensive for these situations, but the parametric assumptions required for efficient variational inference (VI) result in ...
Thomas Pinder +2 more
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LIBRA: Thermal and Process Variation Aware Reliability Management in Photonic Networks-on-Chip
IEEE Transactions on Multi-Scale Computing Systems, 2018 Silicon nanophotonics technology is being considered for future networks-on-chip (NoCs) as it can enable high bandwidth density and lower latency with traversal of data at the speed of light.
Sai Vineel Reddy Chittamuru +2 more
semanticscholar +1 more source
Variation aware analysis of bridging fault testing [PDF]
, 2008 This paper investigates the impact of process variation on test quality with regard to resistive bridging faults. The input logic threshold voltage and gate drive strength parameters are analyzed regarding their process variation induced influence on ...
Ingelsson, Urban +5 more
core +1 more source