Výsledky vyhledávání - deterministic approximate algorithm

Deterministic Approximate Methods for Maximum Consensus Robust Fitting

Autoři: Le, Huu Chin, Tat Jun Eriksson, Anders a další

Zdroj: IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(3):842-857

Témata: Maximum consensus, deterministic algorithm, approximate algorithm, robust fitting

Přístupová URL adresa: https://research.chalmers.se/publication/522416

TOWARD DERANDOMIZING MARKOV CHAIN MONTE CARLO.

Autoři: FENG, WEIMING GUO, HENG WANG, CHUNYANG a další

Zdroj: SIAM Journal on Computing. 2025, Vol. 54 Issue 3, p775-813. 39p.

Témata: *MARKOV chain Monte Carlo, *MONTE Carlo method, *STOCHASTIC processes, *ALGORITHMS, DETERMINISTIC algorithms

Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM

Autoři: Thomas Lartigue Stanley Durrleman Stéphanie Allassonnière

Zdroj: Algorithms, Vol 15, Iss 3, p 78 (2022)

Témata: Expectation Maximisation, exponential family, approximate EM, Riemann sums, tempering, annealing, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95

Popis souboru: electronic resource

Relation: https://www.mdpi.com/1999-4893/15/3/78; https://doaj.org/toc/1999-4893

Přístupová URL adresa: https://doaj.org/article/9f4070037d80482a8674b3942cf472d6

Tight Streaming Lower Bounds for Deterministic Approximate Counting

Autoři: Wang, Yichuan

Témata: Computer Science - Data Structures and Algorithms, Computer Science - Computational Complexity, text

URL: http://arxiv.org/abs/2406.12149

Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM

Autoři: Lartigue, Thomas Durrleman, Stanley Allassonnière, Stéphanie a další

Přispěvatelé: Lartigue, Thomas Durrleman, Stanley Allassonnière, Stéphanie a další

Zdroj: https://hal.inria.fr/hal-02513593 ; 2022.

Témata: Tempering, Annealing, Exponential family, Riemann sum, Approximate EM, Expectation Maximisation algorithm, MSC 2010 subject classifications: 62F99, 49N30, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2003.10126; info:eu-repo/grantAgreement//678304/EU/Learning spatiotemporal patterns in longitudinal image data sets of the aging brain/LEASP; info:eu-repo/grantAgreement//666992/EU/Data-driven models for Progression Of Neurological Disease/EuroPOND; info:eu-repo/grantAgreement//826421/EU/VirtualBrainCloud/TVB-Cloud; hal-02513593; https://hal.inria.fr/hal-02513593; https://hal.inria.fr/hal-02513593v3/document; https://hal.inria.fr/hal-02513593v3/file/main.pdf; ARXIV: 2003.10126

Dostupnost: https://hal.inria.fr/hal-02513593
https://hal.inria.fr/hal-02513593v3/document
https://hal.inria.fr/hal-02513593v3/file/main.pdf

Deterministic approximate counting of colorings with fewer than $2\Delta$ colors via absence of zeros

Autoři: Bencs, Ferenc Berrekkal, Khallil Regts, Guus

Témata: Mathematics - Combinatorics, Computer Science - Discrete Mathematics, Computer Science - Data Structures and Algorithms, text

URL: http://arxiv.org/abs/2408.04727

Lower Bounds for Pseudo-Deterministic Counting in a Stream

Autoři: Braverman, Vladimir Krauthgamer, Robert Krishnan, Aditya a další

Přispěvatelé: Braverman, Vladimir Krauthgamer, Robert Krishnan, Aditya a další

Témata: streaming algorithms, pseudo-deterministic, approximate counting

Popis souboru: application/pdf

Relation: Is Part Of LIPIcs, Volume 261, 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023); https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2023.30

Dostupnost: https://doi.org/10.4230/LIPIcs.ICALP.2023.30
https://nbn-resolving.org/urn:nbn:de:0030-drops-180827
https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2023.30

Tight Space Lower Bound for Pseudo-Deterministic Approximate Counting

Autoři: Grossman, Ofer Gupta, Meghal Sellke, Mark

Témata: Computer Science - Data Structures and Algorithms, Computer Science - Computational Complexity, text

URL: http://arxiv.org/abs/2304.01438

Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM

Autoři: Lartigue, Thomas Durrleman, Stanley Allassonnière, Stéphanie a další

Přispěvatelé: Lartigue, Thomas Durrleman, Stanley Allassonnière, Stéphanie a další

Zdroj: https://hal.inria.fr/hal-02513593 ; 2020.

Témata: Tempering, Annealing, Exponential family, Riemann sum, Approximation, Expectation Maximisation algorithm, MSC 2010 subject classifications: 62F99, 49N30, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2003.10126; info:eu-repo/grantAgreement//678304/EU/Learning spatiotemporal patterns in longitudinal image data sets of the aging brain/LEASP; info:eu-repo/grantAgreement//666992/EU/Data-driven models for Progression Of Neurological Disease/EuroPOND; info:eu-repo/grantAgreement//826421/EU/VirtualBrainCloud/TVB-Cloud; hal-02513593; https://hal.inria.fr/hal-02513593; https://hal.inria.fr/hal-02513593v2/document; https://hal.inria.fr/hal-02513593v2/file/main.pdf; ARXIV: 2003.10126

Dostupnost: https://hal.inria.fr/hal-02513593
https://hal.inria.fr/hal-02513593v2/document
https://hal.inria.fr/hal-02513593v2/file/main.pdf

Auditable Algorithms for Approximate Model Counting

Autoři: Meel, Kuldeep S. Chakraborty, Supratik Akshay, S.

Témata: Computer Science - Logic in Computer Science, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2312.12362

Upper and Lower Bounds for Deterministic Approximate Objects

Autoři: Hendler, Danny Khattabi, Adnane Milani, Alessia a další

Přispěvatelé: Hendler, Danny Khattabi, Adnane Milani, Alessia a další

Zdroj: ICDCS 2021 - 41st IEEE International Conference on Distributed Computing Systems ; https://hal.science/hal-03202712 ; ICDCS 2021 - 41st IEEE International Conference on Distributed Computing Systems, Jul 2021, Washington (virtual), United States. pp.438-448, ⟨10.1109/ICDCS51616.2021.00049⟩

Témata: Relaxed specifications, Shared memory, Fault tolerance, Concurrent data structures, Distributed algorithms, Distributed computing, [INFO]Computer Science [cs]

Geografické téma: Washington (virtual), United States

Time: Washington (virtual), United States

Dostupnost: https://hal.science/hal-03202712
https://hal.science/hal-03202712v2/document
https://hal.science/hal-03202712v2/file/ICDCS.pdf
https://doi.org/10.1109/ICDCS51616.2021.00049

Deterministic Distributed Algorithms and Lower Bounds in the Hybrid Model

Autoři: Anagnostides, Ioannis Gouleakis, Themis

Témata: Distributed Computing, Hybrid Model, Sparse Graphs, Deterministic Algorithms, All-Pairs Shortest Paths, Minimum Cut, Radius, Data processing Computer science, InProceedings, publishedVersion

Deterministic Approximate Counting of Polynomial Threshold Functions via a Derandomized Regularity Lemma

Autoři: Rocco A. Servedio and Li-Yang Tan Servedio, Rocco A. Tan, Li-Yang

Témata: Derandomization, Polynomial threshold functions, deterministic approximate counting, InProceedings, Text, doc-type:ResearchArticle, publishedVersion

URL: https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2021.37
Is Part Of LIPIcs, Volume 207, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)

Deterministic Approximate Counting of Polynomial Threshold Functions via a Derandomized Regularity Lemma

Autoři: Servedio, Rocco A. Tan, Li-Yang Rocco A. Servedio and Li-Yang Tan

Přispěvatelé: Servedio, Rocco A. Tan, Li-Yang Rocco A. Servedio and Li-Yang Tan

Témata: Derandomization, Polynomial threshold functions, deterministic approximate counting

Popis souboru: application/pdf

Relation: Is Part Of LIPIcs, Volume 207, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021); https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2021.37

Dostupnost: https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.37
https://nbn-resolving.org/urn:nbn:de:0030-drops-147304
https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2021.37

Efficient deterministic approximate Bayesian inference for Gaussian process models

Autoři: Bui, Thang Duc Turner, Richard E.

Přispěvatelé: Bui, Thang Duc Turner, Richard E.

Témata: 519.2, machine learning, Gaussian process, approximate inference, Bayesian statistics, supervised learning, unsupervised learning

Přístupová URL adresa: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600

A practical streaming approximate matrix multiplication algorithm.

Autoři: Francis, Deena P. Raimond, Kumudha

Zdroj: Journal of King Saud University - Computer & Information Sciences. Jan2022, Vol. 34 Issue 1, p1455-1465. 11p.

Témata: Matrix multiplications, Algorithms

A New Quadratic Matrix Inequality Approach to Robust Adaptive Beamforming for General-rank Signal Model

Autoři: Huang, Y. Vorobyov, Sergiy Luo a další

Zdroj: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :4335-4339

Témata: deterministic approximate algorithm, Robust adaptive beamforming, ta213, quadratic matrix inequality problem, 0202 electrical engineering, electronic engineering, information engineering, general-rank signal model, 02 engineering and technology, linear matrix inequality relaxation

Přístupová URL adresa: http://juuli.fi/Record/0339563419
https://doi.org/10.1109/ICASSP.2019.8683645
https://dblp.uni-trier.de/db/conf/icassp/icassp2019.html#HuangVL19
https://ieeexplore.ieee.org/document/8683645
https://experts.umn.edu/en/publications/a-new-quadratic-matrix-inequality-approach-to-robust-adaptive-bea
http://dblp.uni-trier.de/db/conf/icassp/icassp2019.html#HuangVL19
https://research.aalto.fi/en/publications/a-new-quadratic-matrix-inequality-approach-to-robust-adaptive-beamforming-for-generalrank-signal-model(57cfc9fe-8a6a-4aeb-a417-329fdea29612).html

Nearly Linear Time Deterministic Algorithms for Submodular Maximization Under Knapsack Constraint and Beyond

Autoři: Li, Wenxin

Témata: Computer Science - Data Structures and Algorithms, text

URL: http://arxiv.org/abs/1804.08178

Deterministic Fully Dynamic Approximate Vertex Cover and Fractional Matching in $O(1)$ Amortized Update Time

Autoři: Bhattacharya, Sayan Chakrabarty, Deeparnab Henzinger, Monika

Témata: Computer Science - Data Structures and Algorithms, text

URL: http://arxiv.org/abs/1611.00198

A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV

Autoři: Kreger, Jesse Komarova, Natalia L Wodarz, Dominik

Zdroj: PLOS Computational Biology. 17(12)

Témata: Medical Microbiology, Biomedical and Clinical Sciences, Immunology, HIV/AIDS, Bioengineering, Infectious Diseases, Sexually Transmitted Infections, Genetics, 2.2 Factors relating to the physical environment, Infection, Algorithms, Cells, Computational Biology, Evolution, Molecular, HIV Infections, HIV-1, Host-Pathogen Interactions, Humans, Mutation, Stochastic Processes, Virus Replication, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/6mz412gv
https://escholarship.org/content/qt6mz412gv/qt6mz412gv.pdf