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ORCIDMatthew B. Blaschko
Matthew Brian Blaschko
Person information
- affiliation: KU Leuven, Belgium
- affiliation (former): École Centrale Paris, Center for Visual Computing, France
- affiliation (former): University of Oxford, Department of Engineering Science, UK
- affiliation (former): Max Planck Institute for Biological Cybernetics, Germany
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2020 – today
- 2023
[j24]Sinnu Susan Thomas
, Jacopo Palandri, Mohsen Lakehal-Ayat, Punarjay Chakravarty, Friedrich Wolf-Monheim, Matthew B. Blaschko:
Kinematics Design of a MacPherson Suspension Architecture Based on Bayesian Optimization. IEEE Trans. Cybern. 53(4): 2261-2274 (2023)- [c68]Junyi Zhu, Xingchen Ma, Matthew B. Blaschko:
Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization. CVPR 2023: 24542-24551 - [c67]Junyi Zhu, Ruicong Yao, Matthew B. Blaschko:
Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. ICML 2023: 43228-43257 - [c66]Mingshi Li, Zifu Wang, Matthew B. Blaschko:
Improved Imagery Throughput via Cascaded Uncertainty Pruning on U-Net++. NLDL 2023 - [i55]Annika Reinke, Minu Tizabi, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Carole H. Sudre, Laura Ación, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew B. Blaschko, Florian Büttner, M. Jorge Cardoso, Veronika Cheplygina, Jianxu Chen, Evangelia Christodoulou, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Jens Kleesiek, Florian Kofler, Thijs Kooi, Annette Kopp-Schneider, Michal Kozubek, Anna Kreshuk, Tahsin M. Kurç, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus H. Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern H. Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Susanne M. Rafelski, Nasir M. Rajpoot, Mauricio Reyes, Michael A. Riegler, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben Van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul F. Jäger, Lena Maier-Hein:
Understanding metric-related pitfalls in image analysis validation. CoRR abs/2302.01790 (2023) - [i54]Zifu Wang, Matthew B. Blaschko:
Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels. CoRR abs/2302.05666 (2023) - [i53]Zifu Wang, Teodora Popordanoska, Jeroen Bertels, Robin Lemmens, Matthew B. Blaschko:
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels. CoRR abs/2303.16296 (2023) - [i52]Jordy Van Landeghem, Rubèn Tito, Lukasz Borchmann, Michal Pietruszka, Pawel Józiak, Rafal Powalski, Dawid Jurkiewicz, Mickaël Coustaty, Bertrand Anckaert, Ernest Valveny, Matthew B. Blaschko, Sien Moens, Tomasz Stanislawek:
Document Understanding Dataset and Evaluation (DUDE). CoRR abs/2305.08455 (2023) - [i51]Junyi Zhu, Xingchen Ma, Matthew B. Blaschko:
Confidence-aware Personalized Federated Learning via Variational Expectation Maximization. CoRR abs/2305.12557 (2023) - [i50]Junyi Zhu, Ruicong Yao, Matthew B. Blaschko:
Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. CoRR abs/2306.00127 (2023) - [i49]Gorjan Radevski, Dusan Grujicic, Marie-Francine Moens, Matthew B. Blaschko, Tinne Tuytelaars:
Multimodal Distillation for Egocentric Action Recognition. CoRR abs/2307.07483 (2023) - [i48]Wangduo Xie, Matthew B. Blaschko:
Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction. CoRR abs/2307.12717 (2023) - [i47]Jordy Van Landeghem, Sanket Biswas, Matthew B. Blaschko, Marie-Francine Moens:
Beyond Document Page Classification: Design, Datasets, and Challenges. CoRR abs/2308.12896 (2023) - 2022
- [j23]Jordy Van Landeghem, Matthew B. Blaschko, Bertrand Anckaert, Marie-Francine Moens:
Benchmarking Scalable Predictive Uncertainty in Text Classification. IEEE Access 10: 43703-43737 (2022) - [j22]Thierry Deruyttere, Dusan Grujicic, Matthew B. Blaschko, Marie-Francine Moens:
Talk2Car: Predicting Physical Trajectories for Natural Language Commands. IEEE Access 10: 123809-123834 (2022) - [j21]Aleksei Tiulpin, Matthew B. Blaschko:
Greedy Bayesian Posterior Approximation with Deep Ensembles. Trans. Mach. Learn. Res. 2022 (2022) - [c65]Dusan Grujicic, Thierry Deruyttere, Marie-Francine Moens, Matthew B. Blaschko:
Predicting Physical World Destinations for Commands Given to Self-Driving Cars. AAAI 2022: 715-725 - [c64]Teodora Popordanoska, Matthew B. Blaschko:
KULeuven at LeQua 2022: Model Calibration in Quantification Learning. CLEF (Working Notes) 2022: 1905-1910 - [c63]Han Zhou, Aida Ashrafi, Matthew B. Blaschko:
Combinatorial optimization for low bit-width neural networks. ICPR 2022: 2246-2252 - [c62]Dusan Grujicic, Matthew B. Blaschko:
2-D latent space models: Layer-wise perceptual training and spatial grounding. ICPR 2022: 2437-2443 - [c61]Huy Hoang Nguyen, Simo Saarakkala, Matthew B. Blaschko, Aleksei Tiulpin:
CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting. ISBI 2022: 1-5 - [c60]Teodora Popordanoska, Raphael Sayer, Matthew B. Blaschko:
A Consistent and Differentiable Lp Canonical Calibration Error Estimator. NeurIPS 2022 - [c59]Zifu Wang, Matthew B. Blaschko:
Optimizing Slimmable Networks for Multiple Target Platforms. NLDL 2022 - [c58]Zifu Wang, Matthew B. Blaschko:
MRF-UNets: Searching UNet with Markov Random Fields. ECML/PKDD (3) 2022: 599-614 - [i46]Han Zhou, Aida Ashrafi, Matthew B. Blaschko:
Combinatorial optimization for low bit-width neural networks. CoRR abs/2206.02006 (2022) - [i45]Sinnu Susan Thomas, Jacopo Palandri, Mohsen Lakehal-Ayat, Punarjay Chakravarty, Friedrich Wolf-Monheim, Matthew B. Blaschko:
Designing MacPherson Suspension Architectures using Bayesian Optimization. CoRR abs/2206.09022 (2022) - [i44]Zifu Wang, Matthew B. Blaschko:
MRF-UNets: Searching UNet with Markov Random Fields. CoRR abs/2207.06168 (2022) - [i43]Teodora Popordanoska, Aleksei Tiulpin, Wacha Bounliphone, Matthew B. Blaschko:
On confidence intervals for precision matrices and the eigendecomposition of covariance matrices. CoRR abs/2208.11977 (2022) - [i42]Gorjan Radevski, Dusan Grujicic, Matthew B. Blaschko, Marie-Francine Moens, Tinne Tuytelaars:
Students taught by multimodal teachers are superior action recognizers. CoRR abs/2210.04331 (2022) - [i41]Teodora Popordanoska, Raphael Sayer, Matthew B. Blaschko:
A Consistent and Differentiable Lp Canonical Calibration Error Estimator. CoRR abs/2210.07810 (2022) - [i40]Huy Hoang Nguyen, Matthew B. Blaschko, Simo Saarakkala, Aleksei Tiulpin:
Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data. CoRR abs/2210.13889 (2022) - 2021
- [j20]Ruben Hemelings, Bart Elen, Matthew B. Blaschko, Julie Jacob, Ingeborg Stalmans, Patrick De Boever:
Pathological myopia classification with simultaneous lesion segmentation using deep learning. Comput. Methods Programs Biomed. 199: 105920 (2021) - [j19]Xingchen Ma, Matthew B. Blaschko:
Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 3024-3036 (2021) - [c57]Junyi Zhu, Matthew B. Blaschko:
R-GAP: Recursive Gradient Attack on Privacy. ICLR 2021 - [c56]Xingchen Ma, Matthew B. Blaschko:
Meta-Cal: Well-controlled Post-hoc Calibration by Ranking. ICML 2021: 7235-7245 - [c55]Stien Heremans, Francis Turkelboom, Margot Verhulst, Matthew B. Blaschko, Ben Somers:
Remote Sensing and Deep Learning for Environmental Policy Support: From Theory to Practice. IGARSS 2021: 5728-5731 - [c54]Axel-Jan Rousseau, Thijs Becker, Jeroen Bertels, Matthew B. Blaschko, Dirk Valkenborg:
Post Training Uncertainty Calibration Of Deep Networks For Medical Image Segmentation. ISBI 2021: 1052-1056 - [c53]Teodora Popordanoska, Jeroen Bertels, Dirk Vandermeulen, Frederik Maes, Matthew B. Blaschko:
On the Relationship Between Calibrated Predictors and Unbiased Volume Estimation. MICCAI (1) 2021: 678-688 - [i39]Ruben Hemelings, Bart Elen, João Barbosa Breda, Matthew B. Blaschko, Patrick De Boever, Ingeborg Stalmans:
Glaucoma detection beyond the optic disc: The importance of the peripapillary region using explainable deep learning. CoRR abs/2103.11895 (2021) - [i38]Huy Hoang Nguyen, Simo Saarakkala, Matthew B. Blaschko, Aleksei Tiulpin:
DeepProg: A Transformer-based Framework for Predicting Disease Prognosis. CoRR abs/2104.03642 (2021) - [i37]Xingchen Ma, Matthew B. Blaschko:
Meta-Cal: Well-controlled Post-hoc Calibration by Ranking. CoRR abs/2105.04290 (2021) - [i36]Aleksei Tiulpin, Matthew B. Blaschko:
Greedy Bayesian Posterior Approximation with Deep Ensembles. CoRR abs/2105.14275 (2021) - [i35]Ruben Hemelings, Bart Elen, João Barbosa Breda, Erwin Bellon, Matthew B. Blaschko, Patrick De Boever, Ingeborg Stalmans:
Pointwise visual field estimation from optical coherence tomography in glaucoma: a structure-function analysis using deep learning. CoRR abs/2106.03793 (2021) - [i34]Junyi Zhu, Matthew B. Blaschko:
Differentially Private SGD with Sparse Gradients. CoRR abs/2112.00845 (2021) - [i33]Dusan Grujicic, Thierry Deruyttere, Marie-Francine Moens, Matthew B. Blaschko:
Predicting Physical World Destinations for Commands Given to Self-Driving Cars. CoRR abs/2112.05419 (2021) - [i32]Teodora Popordanoska, Jeroen Bertels, Dirk Vandermeulen, Frederik Maes, Matthew B. Blaschko:
On the relationship between calibrated predictors and unbiased volume estimation. CoRR abs/2112.12560 (2021) - 2020
- [j18]Maxim Berman, Matthew B. Blaschko:
Discriminative Training of Conditional Random Fields with Probably Submodular Constraints. Int. J. Comput. Vis. 128(6): 1722-1735 (2020) - [j17]Jiaqian Yu, Matthew B. Blaschko:
The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses. IEEE Trans. Pattern Anal. Mach. Intell. 42(3): 735-748 (2020) - [j16]Tom Eelbode, Jeroen Bertels, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops, Matthew B. Blaschko:
Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index. IEEE Trans. Medical Imaging 39(11): 3679-3690 (2020) - [j15]Huy Hoang Nguyen, Simo Saarakkala, Matthew B. Blaschko, Aleksei Tiulpin:
Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading From Plain Radiographs. IEEE Trans. Medical Imaging 39(12): 4346-4356 (2020) - [c52]Xingchen Ma, Matthew B. Blaschko:
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization. AISTATS 2020: 1015-1025 - [c51]Dusan Grujicic, Gorjan Radevski, Tinne Tuytelaars, Matthew B. Blaschko:
Learning to ground medical text in a 3D human atlas. CoNLL 2020: 302-312 - [c50]Maxim Berman, Leonid Pishchulin, Ning Xu, Matthew B. Blaschko, Gérard G. Medioni:
AOWS: Adaptive and Optimal Network Width Search With Latency Constraints. CVPR 2020: 11214-11223 - [c49]Thierry Deruyttere, Simon Vandenhende, Dusan Grujicic, Yu Liu, Luc Van Gool, Matthew B. Blaschko, Tinne Tuytelaars, Marie-Francine Moens:
Commands 4 Autonomous Vehicles (C4AV) Workshop Summary. ECCV Workshops (2) 2020: 3-26 - [i31]Huy Hoang Nguyen, Simo Saarakkala, Matthew B. Blaschko, Aleksei Tiulpin:
Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading from Plain Radiographs. CoRR abs/2003.01944 (2020) - [i30]Maxim Berman, Leonid Pishchulin, Ning Xu, Matthew B. Blaschko, Gérard G. Medioni:
AOWS: Adaptive and optimal network width search with latency constraints. CoRR abs/2005.10481 (2020) - [i29]Ruben Hemelings, Bart Elen, Matthew B. Blaschko, Julie Jacob, Ingeborg Stalmans, Patrick De Boever:
Pathological myopia classification with simultaneous lesion segmentation using deep learning. CoRR abs/2006.02813 (2020) - [i28]Xingchen Ma, Matthew B. Blaschko:
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization. CoRR abs/2006.11771 (2020) - [i27]Thierry Deruyttere, Simon Vandenhende, Dusan Grujicic, Yu Liu, Luc Van Gool, Matthew B. Blaschko, Tinne Tuytelaars, Marie-Francine Moens:
Commands 4 Autonomous Vehicles (C4AV) Workshop Summary. CoRR abs/2009.08792 (2020) - [i26]Xingchen Ma, Matthew B. Blaschko:
Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation. CoRR abs/2010.03171 (2020) - [i25]Junyi Zhu, Matthew B. Blaschko:
R-GAP: Recursive Gradient Attack on Privacy. CoRR abs/2010.07733 (2020) - [i24]Tom Eelbode, Jeroen Bertels, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops, Matthew B. Blaschko:
Optimization for Medical Image Segmentation: Theory and Practice when evaluating with Dice Score or Jaccard Index. CoRR abs/2010.13499 (2020)
2010 – 2019
- 2019
- [j14]Ruben Hemelings, Bart Elen, Ingeborg Stalmans, Karel van Keer, Patrick De Boever, Matthew B. Blaschko:
Artery-vein segmentation in fundus images using a fully convolutional network. Comput. Medical Imaging Graph. 76 (2019) - [j13]Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko, Eugene Belilovsky:
Scattering Networks for Hybrid Representation Learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(9): 2208-2221 (2019) - [c48]Shivangi Srivastava, Maxim Berman, Matthew B. Blaschko, Devis Tuia:
Adaptive Compression-based Lifelong Learning. BMVC 2019: 153 - [c47]Sinnu Susan Thomas, Jacopo Palandri, Mohsen Lakehal-Ayat, Punarjay Chakravarty, Friedrich Wolf-Monheim, Matthew B. Blaschko:
Designing MacPherson Suspension Architectures Using Bayesian Optimization. BNAIC/BENELEARN 2019 - [c46]Xingchen Ma, Amal Rannen Triki, Maxim Berman, Christos Sagonas, Jacques Calì, Matthew B. Blaschko:
A Bayesian Optimization Framework for Neural Network Compression. ICCV 2019: 10273-10282 - [c45]Amal Rannen-Triki, Maxim Berman, Vladimir Kolmogorov, Matthew B. Blaschko:
Function Norms for Neural Networks. ICCV Workshops 2019: 748-752 - [c44]Jeroen Bertels, Tom Eelbode, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops, Matthew B. Blaschko:
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice. MICCAI (2) 2019: 92-100 - [i23]Thomas Verelst, Matthew B. Blaschko, Maxim Berman:
Generating superpixels using deep image representations. CoRR abs/1903.04586 (2019) - [i22]Shivangi Srivastava, Maxim Berman, Matthew B. Blaschko, Devis Tuia:
Adaptive Compression-based Lifelong Learning. CoRR abs/1907.09695 (2019) - [i21]Jeroen Bertels, Tom Eelbode, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops, Matthew B. Blaschko:
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice. CoRR abs/1911.01685 (2019) - [i20]Maxim Berman, Matthew B. Blaschko:
Discriminative training of conditional random fields with probably submodular constraints. CoRR abs/1911.10819 (2019) - 2018
- [j12]Amal Rannen Triki, Matthew B. Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin Joo:
Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks. Comput. Medical Imaging Graph. 69: 21-32 (2018) - [j11]José Ignacio Orlando, Elena Prokofyeva, Mariana del Fresno, Matthew B. Blaschko:
An ensemble deep learning based approach for red lesion detection in fundus images. Comput. Methods Programs Biomed. 153: 115-127 (2018) - [c43]Maxim Berman, Amal Rannen Triki, Matthew B. Blaschko:
The Lovász-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks. CVPR 2018: 4413-4421 - [c42]Siamak Mehrkanoon, Matthew B. Blaschko, Johan A. K. Suykens:
Shallow and Deep Models for Domain Adaptation problems. ESANN 2018 - [c41]José Ignacio Orlando, João Barbosa Breda, Karel van Keer, Matthew B. Blaschko, Pablo J. Blanco, Carlos A. Bulant:
Towards a Glaucoma Risk Index Based on Simulated Hemodynamics from Fundus Images. MICCAI (2) 2018: 65-73 - [i19]José Ignacio Orlando, João Barbosa Breda, Karel van Keer, Matthew B. Blaschko, Pablo J. Blanco, Carlos A. Bulant:
Towards a glaucoma risk index based on simulated hemodynamics from fundus images. CoRR abs/1805.10273 (2018) - [i18]Mathijs Schuurmans, Maxim Berman, Matthew B. Blaschko:
Efficient semantic image segmentation with superpixel pooling. CoRR abs/1806.02705 (2018) - [i17]Maxim Berman, Matthew B. Blaschko:
Supermodular Locality Sensitive Hashes. CoRR abs/1807.06686 (2018) - [i16]Maxim Berman, Matthew B. Blaschko, Amal Rannen Triki, Jiaqian Yu:
Yes, IoU loss is submodular - as a function of the mispredictions. CoRR abs/1809.01845 (2018) - [i15]Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko, Eugene Belilovsky:
Scattering Networks for Hybrid Representation Learning. CoRR abs/1809.06367 (2018) - 2017
- [j10]José Ignacio Orlando, Elena Prokofyeva, Matthew B. Blaschko:
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images. IEEE Trans. Biomed. Eng. 64(1): 16-27 (2017) - [c40]Matthew B. Blaschko:
Slack and Margin Rescaling as Convex Extensions of Supermodular Functions. EMMCVPR 2017: 439-454 - [c39]Amal Rannen Triki, Rahaf Aljundi, Matthew B. Blaschko, Tinne Tuytelaars:
Encoder Based Lifelong Learning. ICCV 2017: 1329-1337 - [c38]Eugene Belilovsky, Matthew B. Blaschko, Jamie Ryan Kiros, Raquel Urtasun, Richard S. Zemel:
Joint Embeddings of Scene Graphs and Images. ICLR (Workshop) 2017 - [c37]Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew B. Blaschko:
Learning to Discover Sparse Graphical Models. ICLR (Workshop) 2017 - [c36]Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew B. Blaschko:
Learning to Discover Sparse Graphical Models. ICML 2017: 440-448 - [i14]Jiaqian Yu, Matthew B. Blaschko:
An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses. CoRR abs/1702.03690 (2017) - [i13]Amal Rannen Triki, Rahaf Aljundi, Matthew B. Blaschko, Tinne Tuytelaars:
Encoder Based Lifelong Learning. CoRR abs/1704.01920 (2017) - [i12]Maxim Berman, Matthew B. Blaschko:
Optimization of the Jaccard index for image segmentation with the Lovász hinge. CoRR abs/1705.08790 (2017) - [i11]José Ignacio Orlando, Elena Prokofyeva, Mariana del Fresno, Matthew B. Blaschko:
Learning to Detect Red Lesions in Fundus Photographs: An Ensemble Approach based on Deep Learning. CoRR abs/1706.03008 (2017) - [i10]Amal Rannen Triki, Maxim Berman, Matthew B. Blaschko:
Stochastic Weighted Function Norm Regularization. CoRR abs/1710.06703 (2017) - 2016
- [j9]Katerina Gkirtzou, Matthew B. Blaschko:
The pyramid quantized Weisfeiler-Lehman graph representation. Neurocomputing 173: 1495-1507 (2016) - [j8]Hakim Sidahmed, Elena Prokofyeva, Matthew B. Blaschko:
Discovering predictors of mental health service utilization with k-support regularized logistic regression. Inf. Sci. 329: 937-949 (2016) - [c35]Jiaqian Yu, Matthew B. Blaschko:
A Convex Surrogate Operator for General Non-Modular Loss Functions. AISTATS 2016: 1032-1041 - [c34]Jiaqian Yu, Matthew B. Blaschko:
Efficient Learning for Discriminative Segmentation with Supermodular Losses. BMVC 2016 - [c33]Mahsa Ghafarianzadeh, Matthew B. Blaschko, Gabe Sibley:
Efficient, dense, object-based segmentation from RGBD video. ICRA 2016: 2310-2317 - [c32]Eugene Belilovsky, Gaël Varoquaux, Matthew B. Blaschko:
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity. NIPS 2016: 595-603 - [c31]Wojciech Zaremba, Matthew B. Blaschko:
Discriminative training of CRF models with probably submodular constraints. WACV 2016: 1-7 - [c30]Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko, Ioannis Antonoglou, Arthur Gretton:
A Test of Relative Similarity For Model Selection in Generative Models. ICLR (Poster) 2016 - [i9]Jiaqian Yu, Matthew B. Blaschko:
A Convex Surrogate Operator for General Non-Modular Loss Functions. CoRR abs/1604.03373 (2016) - [i8]Amal Rannen Triki, Matthew B. Blaschko:
Stochastic Function Norm Regularization of Deep Networks. CoRR abs/1605.09085 (2016) - [i7]Matthew B. Blaschko:
Slack and Margin Rescaling as Convex Extensions of Supermodular Functions. CoRR abs/1606.05918 (2016) - [i6]Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus, Ioannis Antonoglou, Arthur Gretton, Matthew B. Blaschko:
Fast Non-Parametric Tests of Relative Dependency and Similarity. CoRR abs/1611.05740 (2016) - 2015
- [j7]Eugene Belilovsky, Katerina Gkirtzou, Michail Misyrlis, Anna B. Konova, Jean Honorio, Nelly Alia-Klein, Rita Z. Goldstein, Dimitris Samaras, Matthew B. Blaschko:
Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm. Comput. Medical Imaging Graph. 46: 40-46 (2015) - [j6]Eugene Belilovsky, Andreas Argyriou, Gaël Varoquaux, Matthew B. Blaschko:
Convex relaxations of penalties for sparse correlated variables with bounded total variation. Mach. Learn. 100(2-3): 533-553 (2015) - [c29]Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew B. Blaschko:
A low variance consistent test of relative dependency. ICML 2015: 20-29 - [c28]Jiaqian Yu, Matthew B. Blaschko:
Learning Submodular Losses with the Lovasz Hinge. ICML 2015: 1623-1631 - [i5]Jiaqian Yu, Matthew B. Blaschko:
The Lovász Hinge: A Convex Surrogate for Submodular Losses. CoRR abs/1512.07797 (2015) - 2014
- [c27]Mahsa Ghafarianzadeh, Matthew B. Blaschko, Gabe Sibley:
Unsupervised Spatio-Temporal Segmentation with Sparse Spectral-Clustering. BMVC 2014 - [c26]Andrea Vedaldi, Siddharth Mahendran, Stavros Tsogkas, Subhransu Maji, Ross B. Girshick, Juho Kannala, Esa Rahtu, Iasonas Kokkinos, Matthew B. Blaschko, David J. Weiss, Ben Taskar, Karen Simonyan, Naomi Saphra, Sammy Mohamed:
Understanding Objects in Detail with Fine-Grained Attributes. CVPR 2014: 3622-3629 - [c25]Matthew B. Blaschko, Arpit Mittal, Esa Rahtu:
An O(n \log n) Cutting Plane Algorithm for Structured Output Ranking. GCPR 2014: 132-143 - [c24]José Ignacio Orlando, Matthew B. Blaschko:
Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images. MICCAI (1) 2014: 634-641 - [i4]Wacha Bounliphone, Arthur Gretton, Matthew B. Blaschko:
A low variance consistent test of relative dependency. CoRR abs/1406.3852 (2014) - 2013
- [c23]