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387 results on '"Mitiche, Amar"'

1. Parametric Information Maximization for Generalized Category Discovery

Author

Chiaroni, Florent, Dolz, Jose, Masud, Ziko Imtiaz, Mitiche, Amar, and Ayed, Ismail Ben

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We introduce a Parametric Information Maximization (PIM) model for the Generalized Category Discovery (GCD) problem. Specifically, we propose a bi-level optimization formulation, which explores a parameterized family of objective functions, each evaluating a weighted mutual information between the features and the latent labels, subject to supervision constraints from the labeled samples. Our formulation mitigates the class-balance bias encoded in standard information maximization approaches, thereby handling effectively both short-tailed and long-tailed data sets. We report extensive experiments and comparisons demonstrating that our PIM model consistently sets new state-of-the-art performances in GCD across six different datasets, more so when dealing with challenging fine-grained problems.

Published
2022

2. Simplex Clustering via sBeta with Applications to Online Adjustment of Black-Box Predictions

Author

Chiaroni, Florent, Boudiaf, Malik, Mitiche, Amar, and Ayed, Ismail Ben

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We explore clustering the softmax predictions of deep neural networks and introduce a novel probabilistic clustering method, referred to as k-sBetas. In the general context of clustering discrete distributions, the existing methods focused on exploring distortion measures tailored to simplex data, such as the KL divergence, as alternatives to the standard Euclidean distance. We provide a general maximum a posteriori (MAP) perspective of clustering distributions, emphasizing that the statistical models underlying the existing distortion-based methods may not be descriptive enough. Instead, we optimize a mixed-variable objective measuring data conformity within each cluster to the introduced sBeta density function, whose parameters are constrained and estimated jointly with binary assignment variables. Our versatile formulation approximates various parametric densities for modeling simplex data and enables the control of the cluster-balance bias. This yields highly competitive performances for the unsupervised adjustment of black-box model predictions in various scenarios. Our code and comparisons with the existing simplex-clustering approaches and our introduced softmax-prediction benchmarks are publicly available: https://github.com/fchiaroni/Clustering_Softmax_Predictions.

Published
2022

3. Bilinear Models for Machine Learning

Author

Doghri, Tayssir, Szczecinski, Leszek, Benesty, Jacob, and Mitiche, Amar

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this work we define and analyze the bilinear models which replace the conventional linear operation used in many building blocks of machine learning (ML). The main idea is to devise the ML algorithms which are adapted to the objects they treat. In the case of monochromatic images, we show that the bilinear operation exploits better the structure of the image than the conventional linear operation which ignores the spatial relationship between the pixels. This translates into significantly smaller number of parameters required to yield the same performance. We show numerical examples of classification in the MNIST data set.

Published
2019

5. Deep clustering: On the link between discriminative models and K-means

Author

Jabi, Mohammed, Pedersoli, Marco, Mitiche, Amar, and Ayed, Ismail Ben

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

In the context of recent deep clustering studies, discriminative models dominate the literature and report the most competitive performances. These models learn a deep discriminative neural network classifier in which the labels are latent. Typically, they use multinomial logistic regression posteriors and parameter regularization, as is very common in supervised learning. It is generally acknowledged that discriminative objective functions (e.g., those based on the mutual information or the KL divergence) are more flexible than generative approaches (e.g., K-means) in the sense that they make fewer assumptions about the data distributions and, typically, yield much better unsupervised deep learning results. On the surface, several recent discriminative models may seem unrelated to K-means. This study shows that these models are, in fact, equivalent to K-means under mild conditions and common posterior models and parameter regularization. We prove that, for the commonly used logistic regression posteriors, maximizing the $L_2$ regularized mutual information via an approximate alternating direction method (ADM) is equivalent to a soft and regularized K-means loss. Our theoretical analysis not only connects directly several recent state-of-the-art discriminative models to K-means, but also leads to a new soft and regularized deep K-means algorithm, which yields competitive performance on several image clustering benchmarks.

Published
2018

6. Focal and Generalized Seizures Distinction by Rebalancing Class Data and Random Forest Classification

Author

Abou-Abbas, Lina, Jemal, Imene, Henni, Khadidja, Mitiche, Amar, Mezghani, Neila, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rojas, Ignacio, editor, Castillo-Secilla, Daniel, editor, Herrera, Luis Javier, editor, and Pomares, Héctor, editor

Published
2021
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7. Bilinear Models for Machine Learning

Author

Doghri, Tayssir, Szczecinski, Leszek, Benesty, Jacob, Mitiche, Amar, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Farkaš, Igor, editor, Masulli, Paolo, editor, and Wermter, Stefan, editor

Published
2020
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9. Domain adaptation for EEG-based, cross-subject epileptic seizure prediction

Author

Jemal, Imene, Abou-Abbas, Lina, Henni, Khadidja, Mitiche, Amar, Mezghani, Neila, Jemal, Imene, Abou-Abbas, Lina, Henni, Khadidja, Mitiche, Amar, and Mezghani, Neila

Abstract

The ability to predict the occurrence of an epileptic seizure is a safeguard against patient injury and health complications. However, a major challenge in seizure prediction arises from the significant variability observed in patient data. Common patient-specific approaches, which apply to each patient independently, often perform poorly for other patients due to the data variability. The aim of this study is to propose deep learning models which can handle this variability and generalize across various patients. This study addresses this challenge by introducing a novel cross-subject and multi-subject prediction models. Multiple-subject modeling broadens the scope of patient-specific modeling to account for the data from a dedicated ensemble of patients, thereby providing some useful, though relatively modest, level of generalization. The basic neural network architecture of this model is then adapted to cross-subject prediction, thereby providing a broader, more realistic, context of application. For accrued performance, and generalization ability, cross-subject modeling is enhanced by domain adaptation. Experimental evaluation using the publicly available CHB-MIT and SIENA data datasets shows that our multiple-subject model achieved better performance compared to existing works. However, the cross-subject faces challenges when applied to different patients. Finally, through investigating three domain adaptation methods, the model accuracy has been notably improved by 10.30% and 7.4% for the CHB-MIT and SIENA datasets, respectively.

Published
2024

10. Probabilistic Logic for Intelligent Systems

Author

Henderson, Thomas C., Simmons, Robert, Serbinowski, Bernard, Fan, Xiuyi, Mitiche, Amar, Cline, Michael, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Strand, Marcus, editor, Dillmann, Rüdiger, editor, Menegatti, Emanuele, editor, and Ghidoni, Stefano, editor

Published
2019
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14. Motion Detection

Author

Mitiche, Amar, Aggarwal, J.K, Benesty, Jacob, Series editor, Kellermann, Walter, Series editor, Mitiche, Amar, and Aggarwal, J.K.

Published
2014
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17. Tracking

Author

Mitiche, Amar, Aggarwal, J.K, Benesty, Jacob, Series editor, Kellermann, Walter, Series editor, Mitiche, Amar, and Aggarwal, J.K.

Published
2014
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18. Background Preliminaries

Author

Mitiche, Amar, Aggarwal, J.K, Benesty, Jacob, Series editor, Kellermann, Walter, Series editor, Mitiche, Amar, and Aggarwal, J.K.

Published
2014
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19. Optical Flow Estimation

Author

Mitiche, Amar, Aggarwal, J.K, Benesty, Jacob, Series editor, Kellermann, Walter, Series editor, Mitiche, Amar, and Aggarwal, J.K.

Published
2014
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20. Boundary Preserving Variational Image Differentiation

Author

Mathlouthi, Yosra, Mitiche, Amar, Ayed, Ismail Ben, 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, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Rosenhahn, Bodo, editor, and Andres, Bjoern, editor

Published
2016
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26. Appendix

Author

Mitiche, Amar, Ayed, Ismail Ben, Mitiche, Amar, and Ben Ayed, Ismail

Published
2011
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32. Direct Estimation of Dense Scene Flow and Depth from a Monocular Sequence

Author

Mathlouthi, Yosra, Mitiche, Amar, Ben Ayed, Ismail, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, 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, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, McMahan, Ryan, editor, Jerald, Jason, editor, Zhang, Hui, editor, Drucker, Steven M., editor, Kambhamettu, Chandra, editor, El Choubassi, Maha, editor, Deng, Zhigang, editor, and Carlson, Mark, editor

Published
2014
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34. A Gibbsian Kohonen Network for Online Arabic Character Recognition

Author

Mezghani, Neila, Mitiche, Amar, 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, Remagnino, Paolo, editor, Porikli, Fatih, editor, Peters, Jörg, editor, Klosowski, James, editor, Arns, Laura, editor, Chun, Yu Ka, editor, Rhyne, Theresa-Marie, editor, and Monroe, Laura, editor

Published
2008
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35. Merging Active Contours

Author

Ben Ayed, Ismail, Mitiche, Amar, 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, Remagnino, Paolo, editor, Porikli, Fatih, editor, Peters, Jörg, editor, Klosowski, James, editor, Arns, Laura, editor, Chun, Yu Ka, editor, Rhyne, Theresa-Marie, editor, and Monroe, Laura, editor

Published
2008
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36. A Continuous Labeling for Multiphase Graph Cut Image Partitioning

Author

Salah, Mohamed Ben, Mitiche, Amar, Ayed, Ismail Ben, 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, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Remagnino, Paolo, editor, Porikli, Fatih, editor, Peters, Jörg, editor, Klosowski, James, editor, Arns, Laura, editor, Chun, Yu Ka, editor, Rhyne, Theresa-Marie, editor, and Monroe, Laura, editor

Published
2008
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37. Embedding a Region Merging Prior in Level Set Vector-Valued Image Segmentation

Author

Ben Ayed, Ismail, Mitiche, Amar, 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, Yagi, Yasushi, editor, Kang, Sing Bing, editor, Kweon, In So, editor, and Zha, Hongbin, editor

Published
2007
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47. An interpretable deep learning classifier for epileptic seizure prediction using EEG data

Author

Jemal, Imene, Mezghani, Neila, Abou-Abbas, Lina, Mitiche, Amar, Jemal, Imene, Mezghani, Neila, Abou-Abbas, Lina, and Mitiche, Amar

Abstract

Deep learning has served pattern classification in many applications, with a performance which often well exceeds that of other machine learning paradigms. Yet, in general, deep learning has used computational architectures built, albeit partially, by ad hoc means, and its classification decisions are not necessarily interpretable in terms of knowledge relevant to the application it serves. This is often referred to as the black box problem, which in certain applications, such as epileptic seizure prediction, can be a serious impediment. The purpose of this study is to investigate an interpretable deep learning classifier for epileptic EEG-driven seizure prediction. This neural network is interpretable because its layers can be visualized and interpreted as a result of a novel architecture where the learned weights follow from signal processing computations such as frequency sub-band and spatial filters. Consequently, the extracted features are no longer abstract as they correspond to the features commonly used for decoding EEG data. In addition, the network uses layer-wise relevance propagation to reveal pertinent features which can further explain the computations leading to the decisions. In seizure prediction experiments using the CHB-MIT data set, the method produced classification results which improved on the state-of-the art, with first network layer filters corresponding to clinically relevant frequency bands, and the input channels in the brain location in which the seizure originates contributing most significantly to the network predictions.

Published
2022

48. EEG oscillatory power and complexity for epileptic seizure detection

Author

Abou-Abbas, Lina, Jemal, Imene, Henni, Khadidja, Ouakrim, Youssef, Mitiche, Amar, Mezghani, Neila, Abou-Abbas, Lina, Jemal, Imene, Henni, Khadidja, Ouakrim, Youssef, Mitiche, Amar, and Mezghani, Neila

Abstract

Monitoring patients at risk of epileptic seizure is critical for optimal treatment and ensuing the reduction of seizure risk and complications. In general, seizure detection is done manually in hospitals and involves time-consuming visual inspection and interpretation by experts of electroencephalography (EEG) recordings. The purpose of this study is to investigate the pertinence of band-limited spectral power and signal complexity in order to discriminate between seizure and seizure-free EEG brain activity. The signal complexity and spectral power are evaluated in five frequency intervals, namely, the delta, theta, alpha, beta, and gamma bands, to be used as EEG signal feature representation. Classification of seizure and seizure-free data was performed by prevalent potent classifiers. Substantial comparative performance evaluation experiments were performed on a large EEG data record of 341 patients in the Temple University Hospital EEG seizure database. Based on statistically validated criteria, results show the efficiency of band-limited spectral power and signal complexity when using random forest and gradient-boosting decision tree classifiers (95% of the area under the curve (AUC) and 91% for both F-measure and accuracy). These results support the use of these automatic classification schemes to assist the practicing neurologist interpret EEG records more accurately and without tedious visual inspection.

Published
2022

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