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149 results on '"Hirata, Roberto"'

1. Improving Self-supervised Dimensionality Reduction: Exploring Hyperparameters and Pseudo-Labeling Strategies

Author

Oliveira, Artur André A. M., Espadoto, Mateus, Hirata, Roberto, Jr., Hirata, Nina S. T., Telea, Alexandru C., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, de Sousa, A. Augusto, editor, Havran, Vlastimil, editor, Paljic, Alexis, editor, Peck, Tabitha, editor, Hurter, Christophe, editor, Purchase, Helen, editor, Farinella, Giovanni Maria, editor, Radeva, Petia, editor, and Bouatouch, Kadi, editor

Published
2023
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2. Concave losses for robust dictionary learning

Author

de Araujo, Rafael Will M, Hirata, Roberto, and Rakotomamonjy, Alain

Subjects
Computer Science - Learning, Statistics - Machine Learning
Abstract

Traditional dictionary learning methods are based on quadratic convex loss function and thus are sensitive to outliers. In this paper, we propose a generic framework for robust dictionary learning based on concave losses. We provide results on composition of concave functions, notably regarding super-gradient computations, that are key for developing generic dictionary learning algorithms applicable to smooth and non-smooth losses. In order to improve identification of outliers, we introduce an initialization heuristic based on undercomplete dictionary learning. Experimental results using synthetic and real data demonstrate that our method is able to better detect outliers, is capable of generating better dictionaries, outperforming state-of-the-art methods such as K-SVD and LC-KSVD.

Published
2017

6. Stability Analysis of Supervised Decision Boundary Maps

Author

Oliveira, Artur, Espadoto, Mateus, Hirata, Roberto, Telea, Alex, Oliveira, Artur, Espadoto, Mateus, Hirata, Roberto, and Telea, Alex

Abstract

Understanding how a machine learning classifier works is an important task in machine learning engineering. However, doing this is for any classifier in general difficult. We propose to leverage visualization methods for this task. For this, we extend a recent technique called Decision Boundary Map (DBM) which graphically depicts how a classifier partitions its input data space into decision zones separated by decision boundaries. We use a supervised, GPU-accelerated technique that computes bidirectional mappings between the data and projection spaces to solve several shortcomings of DBM, such as accuracy and speed. We present several experiments that show that SDBM generates results which are easier to interpret, far less prone to noise, and compute significantly faster than DBM, while maintaining the genericity and ease of use of DBM for any type of single-output classifier. We also show, in addition to earlier work, that SDBM is stable with respect to various types and amounts of changes of the training set used to construct the visualized classifiers. This property was, to our knowledge, not investigated for any comparable method for visualizing classifier decision maps, and is essential for the deployment of such visualization methods in analyzing real-world classification models.

Published
2023

7. Improving Self-Supervised Dimensionality Reduction: Exploring Hyperparameters and Pseudo-labeling Strategies

Author

Oliveira, Artur, Espadoto, Mateus, Hirata, Roberto, Hirata, Nina, Telea, Alex, Oliveira, Artur, Espadoto, Mateus, Hirata, Roberto, Hirata, Nina, and Telea, Alex

Abstract

Dimensionality reduction (DR) is an essential tool for the visualization of high-dimensional data. The recently proposed Self-Supervised Network Projection (SSNP) method addresses DR with a number of attractive features, such as high computational scalability, genericity, stability and out-of-sample support, computation of an inverse mapping, and the ability of data clustering. Yet, SSNP has an involved computational pipeline using self-supervision based on labels produced by clustering methods and two separate deep learning networks with multiple hyperparameters. In this paper we explore the SSNP method in detail by studying its hyperparameter space and pseudo-labeling strategies. We show how these affect SSNP’s quality and how to set them to optimal values based on extensive evaluations involving multiple datasets, DR methods, and clustering algorithms.

Published
2023

8. Improving Self-Supervised Dimensionality Reduction: Exploring Hyperparameters and Pseudo-labeling Strategies

Author

Sub Visualisation and Graphics, Visualisation and Graphics, de Sousa, A. Augusto, Havran, Vlastimil, Paljic, Alexis, Peck, Tabitha, Hurter, Christophe, Purchase, Helen, Farinella, Giovanni Maria, Radeva, Petia, Bouatouch, Kadi, Oliveira, Artur, Espadoto, Mateus, Hirata, Roberto, Hirata, Nina, Telea, Alex, Sub Visualisation and Graphics, Visualisation and Graphics, de Sousa, A. Augusto, Havran, Vlastimil, Paljic, Alexis, Peck, Tabitha, Hurter, Christophe, Purchase, Helen, Farinella, Giovanni Maria, Radeva, Petia, Bouatouch, Kadi, Oliveira, Artur, Espadoto, Mateus, Hirata, Roberto, Hirata, Nina, and Telea, Alex

Published
2023

9. Stability Analysis of Supervised Decision Boundary Maps

Author

Sub Visualisation and Graphics, Visualisation and Graphics, Oliveira, Artur, Espadoto, Mateus, Hirata, Roberto, Telea, Alex, Sub Visualisation and Graphics, Visualisation and Graphics, Oliveira, Artur, Espadoto, Mateus, Hirata, Roberto, and Telea, Alex

Published
2023

10. Data from Early Changes in Gene Expression Induced by Tobacco Smoke: Evidence for the Importance of Estrogen within Lung Tissue

Author

Meireles, Sibele I., primary, Esteves, Gustavo H., primary, Hirata, Roberto, primary, Peri, Suraj, primary, Devarajan, Karthik, primary, Slifker, Michael, primary, Mosier, Stacy L., primary, Peng, Jing, primary, Vadhanam, Manicka V., primary, Hurst, Harrell E., primary, Neves, E. Jordao, primary, Reis, Luiz F., primary, Gairola, C. Gary, primary, Gupta, Ramesh C., primary, and Clapper, Margie L., primary

Published
2023
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11. Perspective on this Article from Early Changes in Gene Expression Induced by Tobacco Smoke: Evidence for the Importance of Estrogen within Lung Tissue

Author

Meireles, Sibele I., primary, Esteves, Gustavo H., primary, Hirata, Roberto, primary, Peri, Suraj, primary, Devarajan, Karthik, primary, Slifker, Michael, primary, Mosier, Stacy L., primary, Peng, Jing, primary, Vadhanam, Manicka V., primary, Hurst, Harrell E., primary, Neves, E. Jordao, primary, Reis, Luiz F., primary, Gairola, C. Gary, primary, Gupta, Ramesh C., primary, and Clapper, Margie L., primary

Published
2023
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12. Supplementary Figure 2 from Molecular Classifiers for Gastric Cancer and Nonmalignant Diseases of the Gastric Mucosa

Author

Meireles, Sibele I., primary, Cristo, Elier B., primary, Carvalho, Alex F., primary, Hirata, Roberto, primary, Pelosof, Adriane, primary, Gomes, Luciana I., primary, Martins, Waleska K., primary, Begnami, Maria D., primary, Zitron, Cláudia, primary, Montagnini, André L., primary, Soares, Fernando A., primary, Neves, E. Jordão, primary, and Reis, Luiz F. L., primary

Published
2023
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13. Supplementary Figure 5 from Molecular Classifiers for Gastric Cancer and Nonmalignant Diseases of the Gastric Mucosa

Author

Meireles, Sibele I., primary, Cristo, Elier B., primary, Carvalho, Alex F., primary, Hirata, Roberto, primary, Pelosof, Adriane, primary, Gomes, Luciana I., primary, Martins, Waleska K., primary, Begnami, Maria D., primary, Zitron, Cláudia, primary, Montagnini, André L., primary, Soares, Fernando A., primary, Neves, E. Jordão, primary, and Reis, Luiz F. L., primary

Published
2023
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14. Supplementary Figure 11 from Molecular Classifiers for Gastric Cancer and Nonmalignant Diseases of the Gastric Mucosa

Author

Meireles, Sibele I., primary, Cristo, Elier B., primary, Carvalho, Alex F., primary, Hirata, Roberto, primary, Pelosof, Adriane, primary, Gomes, Luciana I., primary, Martins, Waleska K., primary, Begnami, Maria D., primary, Zitron, Cláudia, primary, Montagnini, André L., primary, Soares, Fernando A., primary, Neves, E. Jordão, primary, and Reis, Luiz F. L., primary

Published
2023
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15. Supplementary Figure 6 from Molecular Classifiers for Gastric Cancer and Nonmalignant Diseases of the Gastric Mucosa

Author

Meireles, Sibele I., primary, Cristo, Elier B., primary, Carvalho, Alex F., primary, Hirata, Roberto, primary, Pelosof, Adriane, primary, Gomes, Luciana I., primary, Martins, Waleska K., primary, Begnami, Maria D., primary, Zitron, Cláudia, primary, Montagnini, André L., primary, Soares, Fernando A., primary, Neves, E. Jordão, primary, and Reis, Luiz F. L., primary

Published
2023
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16. Supplementary Figure 7 from Molecular Classifiers for Gastric Cancer and Nonmalignant Diseases of the Gastric Mucosa

Author

Meireles, Sibele I., primary, Cristo, Elier B., primary, Carvalho, Alex F., primary, Hirata, Roberto, primary, Pelosof, Adriane, primary, Gomes, Luciana I., primary, Martins, Waleska K., primary, Begnami, Maria D., primary, Zitron, Cláudia, primary, Montagnini, André L., primary, Soares, Fernando A., primary, Neves, E. Jordão, primary, and Reis, Luiz F. L., primary

Published
2023
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17. Supplementary Figure 9 from Molecular Classifiers for Gastric Cancer and Nonmalignant Diseases of the Gastric Mucosa

Author

Meireles, Sibele I., primary, Cristo, Elier B., primary, Carvalho, Alex F., primary, Hirata, Roberto, primary, Pelosof, Adriane, primary, Gomes, Luciana I., primary, Martins, Waleska K., primary, Begnami, Maria D., primary, Zitron, Cláudia, primary, Montagnini, André L., primary, Soares, Fernando A., primary, Neves, E. Jordão, primary, and Reis, Luiz F. L., primary

Published
2023
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18. Supplementary Figure 8 from Molecular Classifiers for Gastric Cancer and Nonmalignant Diseases of the Gastric Mucosa

Author

Meireles, Sibele I., primary, Cristo, Elier B., primary, Carvalho, Alex F., primary, Hirata, Roberto, primary, Pelosof, Adriane, primary, Gomes, Luciana I., primary, Martins, Waleska K., primary, Begnami, Maria D., primary, Zitron, Cláudia, primary, Montagnini, André L., primary, Soares, Fernando A., primary, Neves, E. Jordão, primary, and Reis, Luiz F. L., primary

Published
2023
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19. Supplementary Figure 3 from Molecular Classifiers for Gastric Cancer and Nonmalignant Diseases of the Gastric Mucosa

Author

Meireles, Sibele I., primary, Cristo, Elier B., primary, Carvalho, Alex F., primary, Hirata, Roberto, primary, Pelosof, Adriane, primary, Gomes, Luciana I., primary, Martins, Waleska K., primary, Begnami, Maria D., primary, Zitron, Cláudia, primary, Montagnini, André L., primary, Soares, Fernando A., primary, Neves, E. Jordão, primary, and Reis, Luiz F. L., primary

Published
2023
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20. Data from Molecular Classifiers for Gastric Cancer and Nonmalignant Diseases of the Gastric Mucosa

Author

Meireles, Sibele I., primary, Cristo, Elier B., primary, Carvalho, Alex F., primary, Hirata, Roberto, primary, Pelosof, Adriane, primary, Gomes, Luciana I., primary, Martins, Waleska K., primary, Begnami, Maria D., primary, Zitron, Cláudia, primary, Montagnini, André L., primary, Soares, Fernando A., primary, Neves, E. Jordão, primary, and Reis, Luiz F. L., primary

Published
2023
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21. Supplementary Figure 4 from Molecular Classifiers for Gastric Cancer and Nonmalignant Diseases of the Gastric Mucosa

Author

Meireles, Sibele I., primary, Cristo, Elier B., primary, Carvalho, Alex F., primary, Hirata, Roberto, primary, Pelosof, Adriane, primary, Gomes, Luciana I., primary, Martins, Waleska K., primary, Begnami, Maria D., primary, Zitron, Cláudia, primary, Montagnini, André L., primary, Soares, Fernando A., primary, Neves, E. Jordão, primary, and Reis, Luiz F. L., primary

Published
2023
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22. A Granulometry Based Descriptor for Object Categorization

Author

Lara, Arnaldo Câmara, Hirata, Roberto, Jr., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hendriks, Cris L. Luengo, editor, Borgefors, Gunilla, editor, and Strand, Robin, editor

Published
2013
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23. An Open-Source Medical Image Processing and Visualization Tool to Analyze Cardiac SPECT Images

Author

de Paula, Luis Roberto Pereira, da Silva dos Santos, Carlos, Gutierrez, Marco Antonio, Hirata, Roberto, Jr., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Wang, Song, editor, Kyungnam, Kim, editor, Benes, Bedrich, editor, Moreland, Kenneth, editor, Borst, Christoph, editor, DiVerdi, Stephen, editor, Yi-Jen, Chiang, editor, and Ming, Jiang, editor

Published
2011
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24. Keygraphs for Sign Detection in Indoor Environments by Mobile Phones

Author

Morimitsu, Henrique, Hashimoto, Marcelo, Pimentel, Rodrigo B., Cesar, Roberto M., Jr., Hirata, Roberto, Jr., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Jiang, Xiaoyi, editor, Ferrer, Miquel, editor, and Torsello, Andrea, editor

Published
2011
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26. Parameter Estimation for Ridge Detection in Images with Thin Structures

Author

Perciano, Talita, Hirata, Roberto, Jr., de Castro Jorge, Lúcio André, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bloch, Isabelle, editor, and Cesar, Roberto M., Jr., editor

Published
2010
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27. Ridge Linking Using an Adaptive Oriented Mask Applied to Plant Root Images with Thin Structures

Author

Perciano, Talita, Hirata, Roberto, Jr., de Castro Jorge, Lúcio André, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bloch, Isabelle, editor, and Cesar, Roberto M., Jr., editor

Published
2010
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31. SDBM: Supervised Decision Boundary Maps for Machine Learning Classifiers

Author

Oliveira, Artur, Espadoto, Mateus, Hirata, Roberto, Telea, Alex, Oliveira, Artur, Espadoto, Mateus, Hirata, Roberto, and Telea, Alex

Abstract

Understanding the decision boundaries of a machine learning classifier is key to gain insight on how classifiers work. Recently, a technique called Decision Boundary Map (DBM) was developed to enable the visualization of such boundaries by leveraging direct and inverse projections. However, DBM have scalability issues for creating fine-grained maps, and can generate results that are hard to interpret when the classification problem has many classes. In this paper we propose a new technique called Supervised Decision Boundary Maps (SDBM), which uses a supervised, GPU-accelerated projection technique that solves the original DBM shortcomings. We show through several experiments that SDBM generates results that are much easier to interpret when compared to DBM, is faster and easier to use, while still being generic enough to be used with any type of single-output classifier

Published
2022

42. Greenery Segmentation In Urban Images By Deep Learning

Author

Oliveira, Artur A. M., Hirata, Nina S. T., and Hirata, Roberto

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, I.4.6, I.5.4, Computer Vision and Pattern Recognition (cs.CV), I.5.4, I.4.6, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Computer Science - Computer Vision and Pattern Recognition, Machine Learning (cs.LG)
Abstract

Vegetation is a relevant feature in the urban scenery and its awareness can be measured in an image by the Green View Index (GVI). Previous approaches to estimate the GVI were based upon heuristics image processing approaches and recently by deep learning networks (DLN). By leveraging some recent DLN architectures tuned to the image segmentation problem and exploiting a weighting strategy in the loss function (LF) we improved previously reported results in similar datasets., Supplemental material can be found at http://greenery_data.arturao.org/

Published
2019

43. Kernels on fuzzy sets: an overview

Author

Guevara, Jorge, Hirata, Roberto, and Canu, St��phane

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Statistics - Machine Learning, Machine Learning (stat.ML), Machine Learning (cs.LG)
Abstract

This paper introduces the concept of kernels on fuzzy sets as a similarity measure for $[0,1]$-valued functions, a.k.a. \emph{membership functions of fuzzy sets}. We defined the following classes of kernels: the cross product, the intersection, the non-singleton and the distance-based kernels on fuzzy sets. Applicability of those kernels are on machine learning and data science tasks where uncertainty in data has an ontic or epistemistic interpretation., Learning on Distributions, Functions, Graphs and Groups @ NIPS-2017, 8th Dec

Published
2019

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