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On the performance of learned data structures

Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra
2021 Theoretical Computer Science  
As a corollary of this general analysis, we show that plugging this result in the (learned) PGM-index, we get a learned data structure which is provably better than B-trees.  ...  A recent trend in algorithm design consists of augmenting classic data structures with machine learning models, which are better suited to reveal and exploit patterns and trends in the input data so to  ...  Part of this work has been supported by the Italian MIUR PRIN project "Multicriteria data structures and algorithms: from compressed to learned indexes, and beyond" (Prot. 2017WR7SHH), by Regione Toscana  ... 
doi:10.1016/j.tcs.2021.04.015 fatcat:telzdfuh35hddhhcejh5tp4vie

Supplementary Data for all experiments and analyses from A possible structural correlate of learning performance on a colour discrimination task in the brain of the bumblebee

Li Li, HaDi MaBouDi, Michaela Egertová, Maurice R. Elphick, Lars Chittka, Clint J. Perry
2017 Figshare  
in learning and memory performance on a visual discrimination task.  ...  These results reveal the varying roles that visual experience, visual learning and foraging activity have on neural structure.  ...  = learning (5 color + 5 color -)  ... 
doi:10.6084/m9.figshare.5414506.v1 fatcat:6nfsmdvj4zf4xbjbiyukmflzhe

Supplementary Data for all experiments and analyses from A possible structural correlate of learning performance on a colour discrimination task in the brain of the bumblebee

Li Li, HaDi MaBouDi, Michaela Egertová, Maurice R. Elphick, Lars Chittka, Clint J. Perry
2017 Figshare  
in learning and memory performance on a visual discrimination task.  ...  These results reveal the varying roles that visual experience, visual learning and foraging activity have on neural structure.  ...  = activity control (clear) 2 = colour control (clear + 10 colour not landed on) 3 = colour learning (5 colour + 5 colour -)  ... 
doi:10.6084/m9.figshare.5437114.v1 fatcat:iy7ns4g2jrcm3miyhg7y4z252e

A machine-learning model driven by geometry, material and structural performance data in architectural design process

Sevil Yazici
2020 Proceedings of the 29th International Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe)   unpublished
The proposed workflow consists of three stages, including generation of the parametric model; running structural performance simulations to collect the data, and operating the ML algorithms, including  ...  geometry with material properties and structural performance data towards decision making in the early-design phase.  ...  Thanks to Emre Toprak from ARUP Istanbul office for his contribution during our collaboration at Ozyegin University, Faculty of Architecture and Design.  ... 
doi:10.52842/conf.ecaade.2020.1.411 fatcat:2i7vpazgq5cmdk6gnjbuoutieu

Innovative ways of teaching research for students

G Seytniyazova, Sh Nizamiddinova
2022 Ренессанс в парадигме новаций образования и технологий в XXI веке  
The purpose of this article is to assist an educator in teaching and creating innovative methods for facilitating research.  ...  A brief background and discussion of the research process is being presented followed by how and why innovative ways are important for doing a research work.  ...  Scientific research is the research performed by applying systematic and constructed scientific methods to obtain, analyze, and interpret data.  ... 
doi:10.47689/innovations-in-edu-vol-iss1-pp189-191 fatcat:fhzlqsp2nreodfda4loh6v3u54

Smart data structures

Jonathan Eastep, David Wingate, Anant Agarwal
2011 Proceedings of the 8th ACM international conference on Autonomic computing - ICAC '11  
As multicores become prevalent, the complexity of programming is skyrocketing. One major difficulty is efficiently orchestrating collaboration among threads through shared data structures.  ...  Unfortunately, choosing and hand-tuning data structure algorithms to get good performance across a variety of machines and inputs is a herculean task to add to the fundamental difficulty of getting a parallel  ...  The second study quantifies the impact of the scancount value on data structure performance.  ... 
doi:10.1145/1998582.1998587 dblp:conf/icac/EastepWA11 fatcat:nn5edmoxjvdlhl7sdwbgnfy5wa

Dynamic Hypergraph Structure Learning

Zizhao Zhang, Haojie Lin, Yue Gao
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
In all these works, the performance of hypergraph learning highly depends on the generated hypergraph structure. A good hypergraph structure can represent the data correlation better, and vice versa.  ...  Although hypergraph learning has attracted much attention recently, most of existing works still rely on a static hypergraph structure, and little effort concentrates on optimizing the hypergraph structure  ...  In all these works, the performance of hypergraph learning highly depends on the generated hypergraph structure. A good hypergraph structure can represent the data correlation better, and vice versa.  ... 
doi:10.24963/ijcai.2018/439 dblp:conf/ijcai/ZhangLG18 fatcat:l7kwwneimrg47owi4npw7xzudu

Semi-supervised learning for facial expression recognition

Ira Cohen, Nicu Sebe, Fabio G. Cozman, Thomas S. Huang
2003 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval - MIR '03  
We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a structure learning algorithm that can utilize unlabeled data to improve classification  ...  We provide an analysis which shows under what conditions unlabeled data can be used in learning to improve classification performance.  ...  We thank Marcelo Cirelo for his help, Jeffery Cohn for the use of the facial expression database, and Michael Lew for discussions on various parts of this work.  ... 
doi:10.1145/973264.973268 dblp:conf/mir/CohenSCH03 fatcat:m5dhbl4rsjcpnjn7i2d2wapmbu

Model averaging strategies for structure learning in Bayesian networks with limited data

Bradley M Broom, Kim-Anh Do, Devika Subramanian
2012 BMC Bioinformatics  
However, the problem of determining the quality and robustness of learned structures in the context of limited data remains largely open.  ...  Figure 2 shows the impact of λ on the performance of the DPSM scoring function on ALARM.  ... 
doi:10.1186/1471-2105-13-s13-s10 pmid:23320818 pmcid:PMC3426799 fatcat:yq74yf67evh3bpxlhde6whlnha

Prediction Models Generation by Machine Learning for Structural Materials Performance by Utilizing the Mi System

Satoshi Minamoto, Takuya Kadohira, Kaita Ito, Makoto Watanabe, Masahiko Demura
2019 International Conference on Computational & Experimental Engineering and Sciences  
The performance on structural materials having complicated inputs/outputs would be solved with the combination of different scientific programs or data from experiment.  ...  One of the merits of constructing a combined model (here we call workflow) is that calculations are performed and the data would be stored in the system automatically.  ...  The performance on structural materials having complicated inputs/outputs would be solved with the combination of different scientific programs or data from experiment.  ... 
doi:10.32604/icces.2019.05447 fatcat:fzcwc6675zbb7e3cj4uwtqkt6m

algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD [article]

Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui
2020 arXiv   pre-print
In this report we describe a tool for comparing the performance of graphical causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis methods.  ...  Performance on this simulated data can then be compared for a number of algorithms, with parameters varied and with performance statistics as selected, producing a publishable report.  ...  Acknowledgments Research reported in this publication was supported by grant U54HG008540 awarded by the National Human Genome Research Institute through funds provided by the trans-NIH Big Data to Knowledge  ... 
arXiv:1607.08110v7 fatcat:g3x2o635tneopgl6zdpdwxjbki

Perpetual Learning Framework based on Type-2 Fuzzy Logic System for a Complex Manufacturing Process

Ali Baraka, George Panoutsos, Stephen Cater
2016 IFAC-PapersOnLine  
It is demonstrated that the proposed structure can effectively learn complex dynamics of input-output data in an adaptive way and maintain good predictive performance in the metal processing case study  ...  The proposed method relies on the creation of new fuzzy rules which are updated and optimised during the incremental learning process.  ...  UK for the financial support and for providing expert knowledge and data for the case study, and also The University of Sheffield for the financial support.  ... 
doi:10.1016/j.ifacol.2016.10.111 fatcat:wmwsnf65d5c2dcnrih4l3r63w4

A Novel on Conditional Min Pooling and Restructured Convolutional Neural Network

Jun Park, Jun-Yeong Kim, Jun-Ho Huh, Han-Sung Lee, Se-Hoon Jung, Chun-Bo Sim
2021 Electronics  
Some Caltech 101 and crawling data were used to test the performance of the conditional min pooling and restructured convolutional neural network.  ...  The pooling performance test based on Caltech 101 increased in accuracy by 0.16~0.52% and decreased in loss by 19.98~28.71% compared with the old pooling technique.  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics10192407 fatcat:te6krwelsjdi5p5mdrm7kuh6xm

A SMILE web-based interface for learning the causal structure and performing a diagnosis of a Bayesian network

Nipat Jongsawat, Wichian Premchaiswadi
2009 2009 IEEE International Conference on Systems, Man and Cybernetics  
This paper focuses on using a SMILE web-based interface for building the structure of BN models from a dataset by using different structural learning algorithms.  ...  Learning the structure of a Bayesian network model and causal relations from a dataset or database is important for large BNs analysis.  ...  ACKNOWLEDGMENT The authors would like to thank the Decision Systems Laboratory, University of Pittsburgh for supporting documents, and source file of the engines: Structural Modeling, Inference, and Learning  ... 
doi:10.1109/icsmc.2009.5346198 dblp:conf/smc/JongsawatP09a fatcat:q5ncofq5n5crlgajgmh734wrhi

Margin-Based Active Learning for Structured Output Spaces [chapter]

Dan Roth, Kevin Small
2006 Lecture Notes in Computer Science  
based on a global margin or a combination of the margin of local classifiers.  ...  In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to improve the global performance  ...  Acknowledgments The authors would like to thank Ming-Wei Chang, Vasin Punyakanok, Alex Klementiev, Nick Rizzolo, and the reviewers for helpful comments and/or dis-  ... 
doi:10.1007/11871842_40 fatcat:gpb3d3gy3zbtddebonuor3vupu
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