Matthew B. Blaschko

According to our database, Matthew B. Blaschko authored at least 131 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Mitigating Bias in Bayesian Optimized Data While Designing MacPherson Suspension Architecture.

Sinnu Susan Thomas, Guillaume Lamine, Jacopo Palandri, Mohsen Lakehal-Ayat, Punarjay Chakravarty, Friedrich Wolf-Monheim, Matthew B. Blaschko

IEEE Trans. Artif. Intell., February, 2024

Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data.

Huy Hoang Nguyen, Matthew B. Blaschko, Simo Saarakkala, Aleksei Tiulpin

IEEE Trans. Medical Imaging, January, 2024

The Common Stability Mechanism behind most Self-Supervised Learning Approaches.

Abhishek Jha, Matthew B. Blaschko, Yuki M. Asano, Tinne Tuytelaars

CoRR, 2024

Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis.

Mingshi Li, Dusan Grujicic, Steven De Saeger, Stien Heremans, Ben Somers, Matthew B. Blaschko

CoRR, 2024

Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection.

Teodora Popordanoska, Aleksei Tiulpin, Matthew B. Blaschko

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Beyond Document Page Classification: Design, Datasets, and Challenges.

Jordy Van Landeghem, Sanket Biswas, Matthew B. Blaschko, Marie-Francine Moens

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

2023

Kinematics Design of a MacPherson Suspension Architecture Based on Bayesian Optimization.

Sinnu Susan Thomas, Jacopo Palandri, Mohsen Lakehal-Ayat, Punarjay Chakravarty, Friedrich Wolf-Monheim, Matthew B. Blaschko

IEEE Trans. Cybern., April, 2023

A generalizable deep learning regression model for automated glaucoma screening from fundus images.

Ruben Hemelings, Bart Elen, Alexander K. Schuster, Matthew B. Blaschko, João Barbosa Breda, Pekko Hujanen, Annika Junglas, Stefan Nickels, Andrew White, Norbert Pfeiffer, Paul Mitchell, Patrick De Boever, Anja Tuulonen, Ingeborg Stalmans

npj Digit. Medicine, 2023

Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors.

Teodora Popordanoska, Sebastian G. Gruber, Aleksei Tiulpin, Florian Büttner, Matthew B. Blaschko

CoRR, 2023

Estimating calibration error under label shift without labels.

Teodora Popordanoska, Gorjan Radevski, Tinne Tuytelaars, Matthew B. Blaschko

CoRR, 2023

A Corrected Expected Improvement Acquisition Function Under Noisy Observations.

Han Zhou, Xingchen Ma, Matthew B. Blaschko

CoRR, 2023

Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels.

Zifu Wang, Matthew B. Blaschko

CoRR, 2023

Understanding metric-related pitfalls in image analysis validation.

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

CoRR, 2023

Improved Imagery Throughput via Cascaded Uncertainty Pruning on U-Net++.

Mingshi Li, Zifu Wang, Matthew B. Blaschko

Proceedings of the 2023 Northern Lights Deep Learning Workshop, 2023

Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels.

Zifu Wang, Xuefei Ning, Matthew B. Blaschko

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union.

Zifu Wang, Maxim Berman, Amal Rannen-Triki, Philip H. S. Torr, Devis Tuia, Tinne Tuytelaars, Luc Van Gool, Jiaqian Yu, Matthew B. Blaschko

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction.

Wangduo Xie, Matthew B. Blaschko

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels.

Zifu Wang, Teodora Popordanoska, Jeroen Bertels, Robin Lemmens, Matthew B. Blaschko

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning.

Junyi Zhu, Ruicong Yao, Matthew B. Blaschko

Proceedings of the International Conference on Machine Learning, 2023

Multimodal Distillation for Egocentric Action Recognition.

Gorjan Radevski, Dusan Grujicic, Matthew B. Blaschko, Marie-Francine Moens, Tinne Tuytelaars

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Document Understanding Dataset and Evaluation (DUDE).

Jordy Van Landeghem, Rafal Powalski, Rubèn Tito, Dawid Jurkiewicz, Matthew B. Blaschko, Lukasz Borchmann, Mickaël Coustaty, Sien Moens, Michal Pietruszka, Bertrand Anckaert, Tomasz Stanislawek, Pawel Józiak, Ernest Valveny

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization.

Junyi Zhu, Xingchen Ma, Matthew B. Blaschko

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Greedy Bayesian Posterior Approximation with Deep Ensembles.

Aleksei Tiulpin, Matthew B. Blaschko

Trans. Mach. Learn. Res., 2022

Students taught by multimodal teachers are superior action recognizers.

Gorjan Radevski, Dusan Grujicic, Matthew B. Blaschko, Marie-Francine Moens, Tinne Tuytelaars

CoRR, 2022

On confidence intervals for precision matrices and the eigendecomposition of covariance matrices.

Teodora Popordanoska, Aleksei Tiulpin, Wacha Bounliphone, Matthew B. Blaschko

CoRR, 2022

Benchmarking Scalable Predictive Uncertainty in Text Classification.

Jordy Van Landeghem, Matthew B. Blaschko, Bertrand Anckaert, Marie-Francine Moens

IEEE Access, 2022

Talk2Car: Predicting Physical Trajectories for Natural Language Commands.

Thierry Deruyttere, Dusan Grujicic, Matthew B. Blaschko, Marie-Francine Moens

IEEE Access, 2022

MRF-UNets: Searching UNet with Markov Random Fields.

Zifu Wang, Matthew B. Blaschko

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Optimizing Slimmable Networks for Multiple Target Platforms.

Zifu Wang, Matthew B. Blaschko

Proceedings of the 2022 Northern Lights Deep Learning Workshop, 2022

A Consistent and Differentiable Lp Canonical Calibration Error Estimator.

Teodora Popordanoska, Raphael Sayer, Matthew B. Blaschko

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting.

Huy Hoang Nguyen, Simo Saarakkala, Matthew B. Blaschko, Aleksei Tiulpin

Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022

Combinatorial optimization for low bit-width neural networks.

Han Zhou, Aida Ashrafi, Matthew B. Blaschko

Proceedings of the 26th International Conference on Pattern Recognition, 2022

2-D latent space models: Layer-wise perceptual training and spatial grounding.

Dusan Grujicic, Matthew B. Blaschko

Proceedings of the 26th International Conference on Pattern Recognition, 2022

KULeuven at LeQua 2022: Model Calibration in Quantification Learning.

Teodora Popordanoska, Matthew B. Blaschko

Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, Bologna, Italy, September 5th - to, 2022

Predicting Physical World Destinations for Commands Given to Self-Driving Cars.

Dusan Grujicic, Thierry Deruyttere, Marie-Francine Moens, Matthew B. Blaschko

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation.

Xingchen Ma, Matthew B. Blaschko

IEEE Trans. Pattern Anal. Mach. Intell., 2021

Differentially Private SGD with Sparse Gradients.

Junyi Zhu, Matthew B. Blaschko

CoRR, 2021

Pointwise visual field estimation from optical coherence tomography in glaucoma: a structure-function analysis using deep learning.

Ruben Hemelings, Bart Elen, João Barbosa Breda, Erwin Bellon, Matthew B. Blaschko, Patrick De Boever, Ingeborg Stalmans

CoRR, 2021

DeepProg: A Transformer-based Framework for Predicting Disease Prognosis.

Huy Hoang Nguyen, Simo Saarakkala, Matthew B. Blaschko, Aleksei Tiulpin

CoRR, 2021

Glaucoma detection beyond the optic disc: The importance of the peripapillary region using explainable deep learning.

Ruben Hemelings, Bart Elen, João Barbosa Breda, Matthew B. Blaschko, Patrick De Boever, Ingeborg Stalmans

CoRR, 2021

Pathological myopia classification with simultaneous lesion segmentation using deep learning.

Ruben Hemelings, Bart Elen, Matthew B. Blaschko, Julie Jacob, Ingeborg Stalmans, Patrick De Boever

Comput. Methods Programs Biomed., 2021

On the Relationship Between Calibrated Predictors and Unbiased Volume Estimation.

Teodora Popordanoska, Jeroen Bertels, Dirk Vandermeulen, Frederik Maes, Matthew B. Blaschko

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

Post Training Uncertainty Calibration Of Deep Networks For Medical Image Segmentation.

Axel-Jan Rousseau, Thijs Becker, Jeroen Bertels, Matthew B. Blaschko, Dirk Valkenborg

Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

Remote Sensing and Deep Learning for Environmental Policy Support: From Theory to Practice.

Stien Heremans, Francis Turkelboom, Margot Verhulst, Matthew B. Blaschko, Ben Somers

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021

Meta-Cal: Well-controlled Post-hoc Calibration by Ranking.

Xingchen Ma, Matthew B. Blaschko

Proceedings of the 38th International Conference on Machine Learning, 2021

R-GAP: Recursive Gradient Attack on Privacy.

Junyi Zhu, Matthew B. Blaschko

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading From Plain Radiographs.

Huy Hoang Nguyen, Simo Saarakkala, Matthew B. Blaschko, Aleksei Tiulpin

IEEE Trans. Medical Imaging, 2020

Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index.

Tom Eelbode, Jeroen Bertels, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops, Matthew B. Blaschko

IEEE Trans. Medical Imaging, 2020

The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses.

Jiaqian Yu, Matthew B. Blaschko

IEEE Trans. Pattern Anal. Mach. Intell., 2020

Discriminative Training of Conditional Random Fields with Probably Submodular Constraints.

Maxim Berman, Matthew B. Blaschko

Int. J. Comput. Vis., 2020

Commands 4 Autonomous Vehicles (C4AV) Workshop Summary.

Thierry Deruyttere, Simon Vandenhende, Dusan Grujicic, Yu Liu, Luc Van Gool, Matthew B. Blaschko, Tinne Tuytelaars, Marie-Francine Moens

Proceedings of the Computer Vision - ECCV 2020 Workshops, 2020

AOWS: Adaptive and Optimal Network Width Search With Latency Constraints.

Maxim Berman, Leonid Pishchulin, Ning Xu, Matthew B. Blaschko, Gérard G. Medioni

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Learning to ground medical text in a 3D human atlas.

Dusan Grujicic, Gorjan Radevski, Tinne Tuytelaars, Matthew B. Blaschko

Proceedings of the 24th Conference on Computational Natural Language Learning, 2020

Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization.

Xingchen Ma, Matthew B. Blaschko

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Scattering Networks for Hybrid Representation Learning.

Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko, Eugene Belilovsky

IEEE Trans. Pattern Anal. Mach. Intell., 2019

Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice.

Jeroen Bertels, Tom Eelbode, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops, Matthew B. Blaschko

CoRR, 2019

Generating superpixels using deep image representations.

Thomas Verelst, Matthew B. Blaschko, Maxim Berman

CoRR, 2019

Artery-vein segmentation in fundus images using a fully convolutional network.

Ruben Hemelings, Bart Elen, Ingeborg Stalmans, Karel van Keer, Patrick De Boever, Matthew B. Blaschko

Comput. Medical Imaging Graph., 2019

Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice.

Jeroen Bertels, Tom Eelbode, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops, Matthew B. Blaschko

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

Function Norms for Neural Networks.

Amal Rannen-Triki, Maxim Berman, Vladimir Kolmogorov, Matthew B. Blaschko

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

A Bayesian Optimization Framework for Neural Network Compression.

Xingchen Ma, Amal Rannen Triki, Maxim Berman, Christos Sagonas, Jacques Calì, Matthew B. Blaschko

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Designing MacPherson Suspension Architectures Using Bayesian Optimization.

Sinnu Susan Thomas, Jacopo Palandri, Mohsen Lakehal-Ayat, Punarjay Chakravarty, Friedrich Wolf-Monheim, Matthew B. Blaschko

Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

Adaptive Compression-based Lifelong Learning.

Shivangi Srivastava, Maxim Berman, Matthew B. Blaschko, Devis Tuia

Proceedings of the 30th British Machine Vision Conference 2019, 2019

2018

Yes, IoU loss is submodular - as a function of the mispredictions.

Maxim Berman, Matthew B. Blaschko, Amal Rannen Triki, Jiaqian Yu

CoRR, 2018

Supermodular Locality Sensitive Hashes.

Maxim Berman, Matthew B. Blaschko

CoRR, 2018

Efficient semantic image segmentation with superpixel pooling.

Mathijs Schuurmans, Maxim Berman, Matthew B. Blaschko

CoRR, 2018

An ensemble deep learning based approach for red lesion detection in fundus images.

José Ignacio Orlando, Elena Prokofyeva, Mariana del Fresno, Matthew B. Blaschko

Comput. Methods Programs Biomed., 2018

Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks.

Amal Rannen Triki, Matthew B. Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin Joo

Comput. Medical Imaging Graph., 2018

Towards a Glaucoma Risk Index Based on Simulated Hemodynamics from Fundus Images.

José Ignacio Orlando, João Barbosa Breda, Karel van Keer, Matthew B. Blaschko, Pablo J. Blanco, Carlos A. Bulant

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

Shallow and Deep Models for Domain Adaptation problems.

Siamak Mehrkanoon, Matthew B. Blaschko, Johan A. K. Suykens

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

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

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images.

José Ignacio Orlando, Elena Prokofyeva, Matthew B. Blaschko

IEEE Trans. Biomed. Eng., 2017

Stochastic Weighted Function Norm Regularization.

Amal Rannen Triki, Maxim Berman, Matthew B. Blaschko

CoRR, 2017

An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses.

Jiaqian Yu, Matthew B. Blaschko

CoRR, 2017

Learning to Detect Red Lesions in Fundus Photographs: An Ensemble Approach based on Deep Learning.

José Ignacio Orlando, Elena Prokofyeva, Mariana del Fresno, Matthew B. Blaschko

CoRR, 2017

Optimization of the Jaccard index for image segmentation with the Lovász hinge.

Maxim Berman, Matthew B. Blaschko

CoRR, 2017

Learning to Discover Sparse Graphical Models.

Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew B. Blaschko

Proceedings of the 5th International Conference on Learning Representations, 2017

Joint Embeddings of Scene Graphs and Images.

Eugene Belilovsky, Matthew B. Blaschko, Jamie Ryan Kiros, Raquel Urtasun, Richard S. Zemel

Proceedings of the 5th International Conference on Learning Representations, 2017

Encoder Based Lifelong Learning.

Amal Rannen Triki, Rahaf Aljundi, Matthew B. Blaschko, Tinne Tuytelaars

Proceedings of the IEEE International Conference on Computer Vision, 2017

Slack and Margin Rescaling as Convex Extensions of Supermodular Functions.

Matthew B. Blaschko

Proceedings of the Energy Minimization Methods in Computer Vision and Pattern Recognition, 2017

2016

Discovering predictors of mental health service utilization with k-support regularized logistic regression.

Hakim Sidahmed, Elena Prokofyeva, Matthew B. Blaschko

Inf. Sci., 2016

The pyramid quantized Weisfeiler-Lehman graph representation.

Katerina Gkirtzou, Matthew B. Blaschko

Neurocomputing, 2016

Stochastic Function Norm Regularization of Deep Networks.

Amal Rannen Triki, Matthew B. Blaschko

CoRR, 2016

Fast Non-Parametric Tests of Relative Dependency and Similarity.

Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus, Ioannis Antonoglou, Arthur Gretton, Matthew B. Blaschko

CoRR, 2016

A Test of Relative Similarity For Model Selection in Generative Models.

Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko, Ioannis Antonoglou, Arthur Gretton

Proceedings of the 4th International Conference on Learning Representations, 2016

Discriminative training of CRF models with probably submodular constraints.

Wojciech Zaremba, Matthew B. Blaschko

Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision, 2016

Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity.

Eugene Belilovsky, Gaël Varoquaux, Matthew B. Blaschko

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Efficient, dense, object-based segmentation from RGBD video.

Mahsa Ghafarianzadeh, Matthew B. Blaschko, Gabe Sibley

Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2016

Efficient Learning for Discriminative Segmentation with Supermodular Losses.

Jiaqian Yu, Matthew B. Blaschko

Proceedings of the British Machine Vision Conference 2016, 2016

A Convex Surrogate Operator for General Non-Modular Loss Functions.

Jiaqian Yu, Matthew B. Blaschko

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Convex relaxations of penalties for sparse correlated variables with bounded total variation.

Eugene Belilovsky, Andreas Argyriou, Gaël Varoquaux, Matthew B. Blaschko

Mach. Learn., 2015

The Lovász Hinge: A Convex Surrogate for Submodular Losses.

Jiaqian Yu, Matthew B. Blaschko

CoRR, 2015

Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm.

Eugene Belilovsky, Katerina Gkirtzou, Michail Misyrlis, Anna B. Konova, Jean Honorio, Nelly Alia-Klein, Rita Z. Goldstein, Dimitris Samaras, Matthew B. Blaschko

Comput. Medical Imaging Graph., 2015

Learning Submodular Losses with the Lovasz Hinge.

Jiaqian Yu, Matthew B. Blaschko

Proceedings of the 32nd International Conference on Machine Learning, 2015

A low variance consistent test of relative dependency.

Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew B. Blaschko

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

A low variance consistent test of relative dependency.

Wacha Bounliphone, Arthur Gretton, Matthew B. Blaschko

CoRR, 2014

Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images.

José Ignacio Orlando, Matthew B. Blaschko

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, 2014

An <i>O</i>(n \log n) Cutting Plane Algorithm for Structured Output Ranking.

Matthew B. Blaschko, Arpit Mittal, Esa Rahtu

Proceedings of the Pattern Recognition - 36th German Conference, 2014

Understanding Objects in Detail with Fine-Grained Attributes.

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

Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

Unsupervised Spatio-Temporal Segmentation with Sparse Spectral-Clustering.

Mahsa Ghafarianzadeh, Matthew B. Blaschko, Gabe Sibley

Proceedings of the British Machine Vision Conference, 2014

2013

A Note on k-support Norm Regularized Risk Minimization

Matthew B. Blaschko

CoRR, 2013

Fine-Grained Visual Classification of Aircraft.

Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew B. Blaschko, Andrea Vedaldi

CoRR, 2013

Non Maximal Suppression in Cascaded Ranking Models.

Matthew B. Blaschko, Juho Kannala, Esa Rahtu

Proceedings of the Image Analysis, 18th Scandinavian Conference, 2013

Taxonomic Prediction with Tree-Structured Covariances.

Matthew B. Blaschko, Wojciech Zaremba, Arthur Gretton

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

B-test: A Non-parametric, Low Variance Kernel Two-sample Test.

Wojciech Zaremba, Arthur Gretton, Matthew B. Blaschko

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics.

Katerina Gkirtzou, Jean Honorio, Dimitris Samaras, Rita Z. Goldstein, Matthew B. Blaschko

Proceedings of the Machine Learning in Medical Imaging - 4th International Workshop, 2013

Sparse Classification with MRI Based Markers for Neuromuscular Disease Categorization.

Katerina Gkirtzou, Jean-François Deux, Guillaume Bassez, Aristeidis Sotiras, Alain Rahmouni, Thibault Varacca, Nikos Paragios, Matthew B. Blaschko

Proceedings of the Machine Learning in Medical Imaging - 4th International Workshop, 2013

FMRI analysis of cocaine addiction using k-support sparsity.

Katerina Gkirtzou, Jean Honorio, Dimitris Samaras, Rita Z. Goldstein, Matthew B. Blaschko

Proceedings of the 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2013

Learning from M/EEG Data with Variable Brain Activation Delays.

Wojciech Zaremba, M. Pawan Kumar, Alexandre Gramfort, Matthew B. Blaschko

Proceedings of the Information Processing in Medical Imaging, 2013

2012

Guest Editorial: Special Issue on Structured Prediction and Inference.

Matthew B. Blaschko, Christoph H. Lampert

Int. J. Comput. Vis., 2012

Perceptron Learning of SAT.

Alex Flint, Matthew B. Blaschko

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Taxonomic Multi-class Prediction and Person Layout Using Efficient Structured Ranking.

Arpit Mittal, Matthew B. Blaschko, Andrew Zisserman, Philip H. S. Torr

Proceedings of the Computer Vision - ECCV 2012, 2012

2011

Semi-supervised kernel canonical correlation analysis with application to human fMRI.

Matthew B. Blaschko, Jacquelyn A. Shelton, Andreas M. Bartels, Christoph H. Lampert, Arthur Gretton

Pattern Recognit. Lett., 2011

Learning equivariant structured output SVM regressors.

Andrea Vedaldi, Matthew B. Blaschko, Andrew Zisserman

Proceedings of the IEEE International Conference on Computer Vision, 2011

Learning a category independent object detection cascade.

Esa Rahtu, Juho Kannala, Matthew B. Blaschko

Proceedings of the IEEE International Conference on Computer Vision, 2011

Branch and Bound Strategies for Non-maximal Suppression in Object Detection.

Matthew B. Blaschko

Proceedings of the Energy Minimazation Methods in Computer Vision and Pattern Recognition, 2011

2010

Unsupervised Object Discovery: A Comparison.

Tinne Tuytelaars, Christoph H. Lampert, Matthew B. Blaschko, Wray L. Buntine

Int. J. Comput. Vis., 2010

Simultaneous Object Detection and Ranking with Weak Supervision.

Matthew B. Blaschko, Andrea Vedaldi, Andrew Zisserman

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009

Kernel methods in computer vision: object localization, clustering, and taxonomy discovery.

Matthew Brian Blaschko

PhD thesis, 2009

Efficient Subwindow Search: A Branch and Bound Framework for Object Localization.

Christoph H. Lampert, Matthew B. Blaschko, Thomas Hofmann

IEEE Trans. Pattern Anal. Mach. Intell., 2009

Structured prediction by joint kernel support estimation.

Christoph H. Lampert, Matthew B. Blaschko

Mach. Learn., 2009

Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity.

Matthew B. Blaschko, Jacquelyn A. Shelton, Andreas M. Bartels

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Object Localization with Global and Local Context Kernels.

Matthew B. Blaschko, Christoph H. Lampert

Proceedings of the British Machine Vision Conference, 2009

2008

Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis.

Matthew B. Blaschko, Christoph H. Lampert, Arthur Gretton

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Learning Taxonomies by Dependence Maximization.

Matthew B. Blaschko, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Learning to Localize Objects with Structured Output Regression.

Matthew B. Blaschko, Christoph H. Lampert

Proceedings of the Computer Vision, 2008

A Multiple Kernel Learning Approach to Joint Multi-class Object Detection.

Christoph H. Lampert, Matthew B. Blaschko

Proceedings of the Pattern Recognition, 2008

Beyond sliding windows: Object localization by efficient subwindow search.

Christoph H. Lampert, Matthew B. Blaschko, Thomas Hofmann

Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

Correlational spectral clustering.

Matthew B. Blaschko, Christoph H. Lampert

Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

2005

Automatic In Situ Identification of Plankton.

Matthew B. Blaschko, Gary Holness, Marwan A. Mattar, Dimitri A. Lisin, Paul E. Utgoff, Allen R. Hanson, Howard J. Schultz, Edward M. Riseman, Michael E. Sieracki, William M. Balch, Ben Tupper

Proceedings of the 7th IEEE Workshop on Applications of Computer Vision / IEEE Workshop on Motion and Video Computing (WACV/MOTION 2005), 2005

Combining Local and Global Image Features for Object Class Recognition.

Dimitri A. Lisin, Marwan A. Mattar, Matthew B. Blaschko, Erik G. Learned-Miller, Mark C. Benfield

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005