Machine Learning & Computer Vision
- I am currently in Seattle, doing an internship for Amazon until October 4th.
Publications, preprints & participation to conferences
- Function Norms for Neural Networks, Amal Rannen Triki, Maxim Berman, Vladimir Kolmogorov, Matthew B. Blaschko. To appear in ICCV 2019 workshop on Statistical Deep Learning for Computer Vision
- A Bayesian Optimization Framework for Neural Network Compression, Xingchen Ma, Amal Rannen Triki, Maxim Berman, Christos Sagonas, Jacques Cali, Matthew B. Blaschko. To appear in ICCV 2019
- Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice, Jeroen Bertels, Tom Elbode, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops, Matthew B. Blaschko. To appear in MICCAI 2019
- Adaptive Compression-based Lifelong Learning, Shivangi Srivastava, Maxim Berman, Matthew B. Blaschko, Devis Tuia, BMVC 2019 spotlight
- MultiGrain: a unified image embedding for classes and instances [code], Maxim Berman, Hervé Jégou, Andrea Vedaldi, Iasonas Kokkinos, Matthijs Douze. arXiv preprint. Work done during an internship at Facebook AI Research in Paris.
- The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks, Maxim Berman, Amal Rannen Triki, Matthew B. Blaschko. Published in CVPR 2018.
- Masters thesis I supervised at KU Leuven (2018)
- Stochastic Weighted Function Norm Regularization, Amal Rannen Triki, Maxim Berman, Matthew B. Blaschko, arxiv preprint, Oct. 2017
- Efficient optimization for probably submodular constraints in CRFs, Maxim Berman, Matthew B. Blaschko. Presented at the NIPS workshop on Constructive Machine Learning 2016.
- Monocular Surface Reconstruction using 3D Deformable Part Models, Stefan Kinauer, Maxim Berman, Iasonas Kokkinos, presented at "Geometry Meets Deep Learning" in ECCV 2016.
My Master works
I based the Pelican theme for this website on pelican-bootstrap3; modifications can be made available.