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Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures.

M. Seddik, C. Louart, M. Tamaazousti, and R. Couillet. ICML, volume 119 of Proceedings of Machine Learning Research, page 8573-8582. PMLR, (2020)

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Mohamed Mohamed

Mohamed Mohamed Halawa

Mohamed Mohamed Gibry

Mohamed Ali Mohamed

Mohamed Suliman Mohamed

Other publications of authors with the same name

A Kernel Random Matrix-Based Approach for Sparse PCA.M. Seddik, M. Tamaazousti, and R. Couillet. ICLR (Poster), OpenReview.net, (2019)Field Theoretical Approach for Signal Detection in Nearly Continuous Positive Spectra I: Matricial Data.V. Lahoche, D. Samary, and M. Tamaazousti. Entropy, 23 (9): 1132 (2021)Generative collaborative networks for single image super-resolution.M. Seddik, M. Tamaazousti, and J. Lin. Neurocomputing, (2020)Neural Networks Classify through the Class-Wise Means of Their Representations.M. Seddik, and M. Tamaazousti. AAAI, page 8204-8211. AAAI Press, (2022)Random Tensor Theory for Tensor Decomposition.M. Ouerfelli, M. Tamaazousti, and V. Rivasseau. AAAI, page 7913-7921. AAAI Press, (2022)Learning to Segment Dynamic Objects using SLAM Outliers.A. Bojko, R. Dupont, M. Tamaazousti, and H. Borgne. ICPR, page 9780-9787. IEEE, (2020)Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution.Z. Chaouai, and M. Tamaazousti. CoRR, (2024)Can we Invert a Local Reflectance Model From a Single Specular Highlight with Known Scene Geometry and Camera Pose?S. Said, M. Tamaazousti, and A. Bartoli. Eurographics (Posters), page 15-16. Eurographics Association, (2019)Color consistency of specular highlights in consumer cameras.J. Lin, M. Tamaazousti, S. Said, and A. Morgand. VRST, page 52:1-52:2. ACM, (2017)Lightweight Neural Networks From PCA & LDA Based Distilled Dense Neural Networks.M. Seddik, H. Essafi, A. Benzine, and M. Tamaazousti. ICIP, page 3060-3064. IEEE, (2020)

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Disambiguation of "Tamaazousti, Mohamed"

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Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures.

Please choose a person to relate this publication to

Mohamed Mohamed

Mohamed Mohamed Halawa

Mohamed Mohamed Gibry

Mohamed Ali Mohamed

Mohamed Suliman Mohamed

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Tamaazousti, Mohamed"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures.

Please choose a person to relate this publication to

Mohamed Mohamed

Mohamed Mohamed Halawa

Mohamed Mohamed Gibry

Mohamed Ali Mohamed

Mohamed Suliman Mohamed

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures.