Maxim Berman
Maxim Berman
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The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
M Berman, A Rannen Triki, MB Blaschko
Optimizing the dice score and jaccard index for medical image segmentation: Theory and practice
J Bertels, T Eelbode, M Berman, D Vandermeulen, F Maes, R Bisschops, ...
International Conference on Medical Image Computing and Computer-Assisted …, 2019
Optimization for medical image segmentation: theory and practice when evaluating with Dice score or Jaccard index
T Eelbode, J Bertels, M Berman, D Vandermeulen, F Maes, R Bisschops, ...
IEEE Transactions on Medical Imaging 39 (11), 3679-3690, 2020
Multigrain: a unified image embedding for classes and instances
M Berman, H Jégou, A Vedaldi, I Kokkinos, M Douze
arXiv preprint arXiv:1902.05509, 2019
Optimization of the jaccard index for image segmentation with the lovász hinge
M Berman, MB Blaschko
CoRR, abs/1705.08790 5, 2017
A Bayesian Optimization Framework for Neural Network Compression
X Ma, A Rannen Ep Triki, M Berman, C Sagonas, J Cali, MB Blaschko
Proceedings of the IEEE International Conference on Computer Vision, 2019
AOWS: Adaptive and optimal network width search with latency constraints
M Berman, L Pishchulin, N Xu, MB Blaschko, G Medioni
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
Efficient semantic image segmentation with superpixel pooling
M Schuurmans, M Berman, MB Blaschko
arXiv preprint arXiv:1806.02705, 2018
Adaptive compression-based lifelong learning
S Srivastava, M Berman, MB Blaschko, D Tuia
arXiv preprint arXiv:1907.09695, 2019
Stochastic function norm regularization of deep networks
AR Triki, MB Blaschko
arXiv preprint arXiv:1605.09085, 2016
Generating superpixels with deep representations
T Verelst, M Berman
CVPR 2018 workshop on DeepVision: Beyond supervised learning, Date: 2018/06 …, 2018
Generating superpixels using deep image representations
T Verelst, M Blaschko, M Berman
arXiv preprint arXiv:1903.04586, 2019
Monocular surface reconstruction using 3d deformable part models
S Kinauer, M Berman, I Kokkinos
European Conference on Computer Vision, 296-308, 2016
Function norms for neural networks
A Rannen-Triki, M Berman, V Kolmogorov, MB Blaschko
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
Supermodular Locality Sensitive Hashes
M Berman, MB Blaschko
arXiv preprint arXiv:1807.06686, 2018
Stochastic weighted function norm regularization
AR Triki, M Berman, MB Blaschko
CoRR, 2017
Discriminative training of conditional random fields with probably submodular constraints
M Berman, MB Blaschko
International Journal of Computer Vision 128 (6), 1722-1735, 2020
Tractable Approximations for Achieving Higher Model Efficiency in Computer Vision
M Berman
Yes, IoU loss is submodular-as a function of the mispredictions
M Berman, MB Blaschko, AR Triki, J Yu
arXiv preprint arXiv:1809.01845, 2018
Function Norms and Regularization in Deep Networks
A Rannen Triki, M Berman, MB Blaschko
arXiv e-prints, arXiv: 1710.06703, 2017
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