The Experts below are selected from a list of 62862 Experts worldwide ranked by ideXlab platform

Uta Dahmen - One of the best experts on this subject based on the ideXlab platform.

  • micrant towards Regression Task oriented annotation tool for microscopic images
    International Workshop on Combinatorial Image Analysis, 2020
    Co-Authors: Miroslav Jirik, Vladimira Moulisova, Claudia Schindler, Lenka Cervenkova, Richard Palek, J Rosendorf, Janine Arlt, Lukas Bolek, Jiri Dejmek, Uta Dahmen
    Abstract:

    Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator’s attention intensive. Our goal was to create a tool that would enable us to create annotations of the dataset with minimal demands on expert’s time. Inspired by applications such as Tinder, we have created an annotation tool for describing microscopic images. A graphical user interface is used to select from a couple of images the one with the higher value of the examined parameter. Two experiments were performed. The first compares the speed of annotation of our application with the commonly used tool for processing microscopic images. In the second experiment, the texture description was compared with the annotations from MicrAnt application and commonly used application. The results showed that the processing time using our application is 3 times lower and the Spearman coefficient increases by 0.05 than using a commonly used application. In an experiment, we have shown that the annotations processed using our application increase the correlation of the studied parameter and texture descriptors compared with manual annotations .

  • IWCIA - MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Images
    Lecture Notes in Computer Science, 2020
    Co-Authors: Miroslav Jirik, Vladimira Moulisova, Claudia Schindler, Richard Palek, J Rosendorf, Janine Arlt, Lukas Bolek, Jiri Dejmek, Lenka Červenková, Uta Dahmen
    Abstract:

    Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator’s attention intensive. Our goal was to create a tool that would enable us to create annotations of the dataset with minimal demands on expert’s time. Inspired by applications such as Tinder, we have created an annotation tool for describing microscopic images. A graphical user interface is used to select from a couple of images the one with the higher value of the examined parameter. Two experiments were performed. The first compares the speed of annotation of our application with the commonly used tool for processing microscopic images. In the second experiment, the texture description was compared with the annotations from MicrAnt application and commonly used application. The results showed that the processing time using our application is 3 times lower and the Spearman coefficient increases by 0.05 than using a commonly used application. In an experiment, we have shown that the annotations processed using our application increase the correlation of the studied parameter and texture descriptors compared with manual annotations .

Miroslav Jirik - One of the best experts on this subject based on the ideXlab platform.

  • micrant towards Regression Task oriented annotation tool for microscopic images
    International Workshop on Combinatorial Image Analysis, 2020
    Co-Authors: Miroslav Jirik, Vladimira Moulisova, Claudia Schindler, Lenka Cervenkova, Richard Palek, J Rosendorf, Janine Arlt, Lukas Bolek, Jiri Dejmek, Uta Dahmen
    Abstract:

    Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator’s attention intensive. Our goal was to create a tool that would enable us to create annotations of the dataset with minimal demands on expert’s time. Inspired by applications such as Tinder, we have created an annotation tool for describing microscopic images. A graphical user interface is used to select from a couple of images the one with the higher value of the examined parameter. Two experiments were performed. The first compares the speed of annotation of our application with the commonly used tool for processing microscopic images. In the second experiment, the texture description was compared with the annotations from MicrAnt application and commonly used application. The results showed that the processing time using our application is 3 times lower and the Spearman coefficient increases by 0.05 than using a commonly used application. In an experiment, we have shown that the annotations processed using our application increase the correlation of the studied parameter and texture descriptors compared with manual annotations .

  • IWCIA - MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Images
    Lecture Notes in Computer Science, 2020
    Co-Authors: Miroslav Jirik, Vladimira Moulisova, Claudia Schindler, Richard Palek, J Rosendorf, Janine Arlt, Lukas Bolek, Jiri Dejmek, Lenka Červenková, Uta Dahmen
    Abstract:

    Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator’s attention intensive. Our goal was to create a tool that would enable us to create annotations of the dataset with minimal demands on expert’s time. Inspired by applications such as Tinder, we have created an annotation tool for describing microscopic images. A graphical user interface is used to select from a couple of images the one with the higher value of the examined parameter. Two experiments were performed. The first compares the speed of annotation of our application with the commonly used tool for processing microscopic images. In the second experiment, the texture description was compared with the annotations from MicrAnt application and commonly used application. The results showed that the processing time using our application is 3 times lower and the Spearman coefficient increases by 0.05 than using a commonly used application. In an experiment, we have shown that the annotations processed using our application increase the correlation of the studied parameter and texture descriptors compared with manual annotations .

Han Liu - One of the best experts on this subject based on the ideXlab platform.

  • Calibrated multivariate Regression with application to neural semantic basis discovery
    Journal of machine learning research : JMLR, 2015
    Co-Authors: Han Liu, Lie Wang, Tuo Zhaoy
    Abstract:

    We propose a calibrated multivariate Regression method named CMR for fitting high dimensional multivariate Regression models. Compared with existing methods, CMR calibrates regularization for each Regression Task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O(1/e), where e is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate Regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.

  • NIPS - Multivariate Regression with Calibration
    Advances in neural information processing systems, 2014
    Co-Authors: Han Liu, Lie Wang, Tuo Zhao
    Abstract:

    We propose a new method named calibrated multivariate Regression (CMR) for fitting high dimensional multivariate Regression models. Compared to existing methods, CMR calibrates the regularization for each Regression Task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm which has a worst-case iteration complexity O(1/ ∈), where ∈ is a pre-specified numerical accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate Regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts.

  • Multivariate Regression with Calibration
    arXiv: Machine Learning, 2013
    Co-Authors: Han Liu, Lie Wang, Tuo Zhao
    Abstract:

    We propose a new method named calibrated multivariate Regression (CMR) for fitting high dimensional multivariate Regression models. Compared to existing methods, CMR calibrates the regularization for each Regression Task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence $O(1/\epsilon)$, where $\epsilon$ is a pre-specified accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms existing multivariate Regression methods. We also apply CMR on a brain activity prediction problem and find that CMR even outperforms the handcrafted models created by human experts.

  • Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery
    arXiv: Machine Learning, 2013
    Co-Authors: Han Liu, Lie Wang, Tuo Zhao
    Abstract:

    We propose a calibrated multivariate Regression method named CMR for fitting high dimensional multivariate Regression models. Compared with existing methods, CMR calibrates regularization for each Regression Task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence $\cO(1/\epsilon)$, where $\epsilon$ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate Regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package \texttt{camel} implementing the proposed method is available on the Comprehensive R Archive Network \url{this http URL}.

Tuo Zhao - One of the best experts on this subject based on the ideXlab platform.

  • NIPS - Multivariate Regression with Calibration
    Advances in neural information processing systems, 2014
    Co-Authors: Han Liu, Lie Wang, Tuo Zhao
    Abstract:

    We propose a new method named calibrated multivariate Regression (CMR) for fitting high dimensional multivariate Regression models. Compared to existing methods, CMR calibrates the regularization for each Regression Task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm which has a worst-case iteration complexity O(1/ ∈), where ∈ is a pre-specified numerical accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate Regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts.

  • Multivariate Regression with Calibration
    arXiv: Machine Learning, 2013
    Co-Authors: Han Liu, Lie Wang, Tuo Zhao
    Abstract:

    We propose a new method named calibrated multivariate Regression (CMR) for fitting high dimensional multivariate Regression models. Compared to existing methods, CMR calibrates the regularization for each Regression Task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence $O(1/\epsilon)$, where $\epsilon$ is a pre-specified accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms existing multivariate Regression methods. We also apply CMR on a brain activity prediction problem and find that CMR even outperforms the handcrafted models created by human experts.

  • Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery
    arXiv: Machine Learning, 2013
    Co-Authors: Han Liu, Lie Wang, Tuo Zhao
    Abstract:

    We propose a calibrated multivariate Regression method named CMR for fitting high dimensional multivariate Regression models. Compared with existing methods, CMR calibrates regularization for each Regression Task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence $\cO(1/\epsilon)$, where $\epsilon$ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate Regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package \texttt{camel} implementing the proposed method is available on the Comprehensive R Archive Network \url{this http URL}.

Jiri Dejmek - One of the best experts on this subject based on the ideXlab platform.

  • micrant towards Regression Task oriented annotation tool for microscopic images
    International Workshop on Combinatorial Image Analysis, 2020
    Co-Authors: Miroslav Jirik, Vladimira Moulisova, Claudia Schindler, Lenka Cervenkova, Richard Palek, J Rosendorf, Janine Arlt, Lukas Bolek, Jiri Dejmek, Uta Dahmen
    Abstract:

    Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator’s attention intensive. Our goal was to create a tool that would enable us to create annotations of the dataset with minimal demands on expert’s time. Inspired by applications such as Tinder, we have created an annotation tool for describing microscopic images. A graphical user interface is used to select from a couple of images the one with the higher value of the examined parameter. Two experiments were performed. The first compares the speed of annotation of our application with the commonly used tool for processing microscopic images. In the second experiment, the texture description was compared with the annotations from MicrAnt application and commonly used application. The results showed that the processing time using our application is 3 times lower and the Spearman coefficient increases by 0.05 than using a commonly used application. In an experiment, we have shown that the annotations processed using our application increase the correlation of the studied parameter and texture descriptors compared with manual annotations .

  • IWCIA - MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Images
    Lecture Notes in Computer Science, 2020
    Co-Authors: Miroslav Jirik, Vladimira Moulisova, Claudia Schindler, Richard Palek, J Rosendorf, Janine Arlt, Lukas Bolek, Jiri Dejmek, Lenka Červenková, Uta Dahmen
    Abstract:

    Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator’s attention intensive. Our goal was to create a tool that would enable us to create annotations of the dataset with minimal demands on expert’s time. Inspired by applications such as Tinder, we have created an annotation tool for describing microscopic images. A graphical user interface is used to select from a couple of images the one with the higher value of the examined parameter. Two experiments were performed. The first compares the speed of annotation of our application with the commonly used tool for processing microscopic images. In the second experiment, the texture description was compared with the annotations from MicrAnt application and commonly used application. The results showed that the processing time using our application is 3 times lower and the Spearman coefficient increases by 0.05 than using a commonly used application. In an experiment, we have shown that the annotations processed using our application increase the correlation of the studied parameter and texture descriptors compared with manual annotations .