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 (CVPR Workshops, 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 (WACV/MOTION, 2005)

Correlational spectral clustering. - Matthew B. Blaschko; Christoph H. Lampert (CVPR, 2008)

Beyond sliding windows: Object localization by efficient subwindow search. - Christoph H. Lampert; Matthew B. Blaschko; Thomas Hofmann 001 (CVPR, 2008)

A Multiple Kernel Learning Approach to Joint Multi-class Object Detection. - Christoph H. Lampert; Matthew B. Blaschko (DAGM-Symposium, 2008)

Learning to Localize Objects with Structured Output Regression. - Matthew B. Blaschko; Christoph H. Lampert (ECCV (1), 2008)

A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery. - Matthew B. Blaschko; Arthur Gretton (MLG@ICML, 2008)

Learning Taxonomies by Dependence Maximization. - Matthew B. Blaschko; Arthur Gretton (NIPS, 2008)

Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis. - Matthew B. Blaschko; Christoph H. Lampert; Arthur Gretton (ECML/PKDD (1), 2008)

Object Localization with Global and Local Context Kernels. - Matthew B. Blaschko; Christoph H. Lampert (BMVC, 2009)

Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity. - Matthew B. Blaschko; Jacquelyn A. Shelton; Andreas M. Bartels (NIPS, 2009)

Structured prediction by joint kernel support estimation. - Christoph H. Lampert; Matthew B. Blaschko (Mach. Learn., 2009)

Efficient Subwindow Search: A Branch and Bound Framework for Object Localization. - Christoph H. Lampert; Matthew B. Blaschko; Thomas Hofmann 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Kernel methods in computer vision: object localization, clustering, and taxonomy discovery. - Matthew Brian Blaschko (2009)

Simultaneous Object Detection and Ranking with Weak Supervision. - Matthew B. Blaschko; Andrea Vedaldi; Andrew Zisserman (NIPS, 2010)

Unsupervised Object Discovery: A Comparison. - Tinne Tuytelaars; Christoph H. Lampert; Matthew B. Blaschko; Wray L. Buntine (Int. J. Comput. Vis., 2010)

Branch and Bound Strategies for Non-maximal Suppression in Object Detection. - Matthew B. Blaschko (EMMCVPR, 2011)

Learning a category independent object detection cascade. - Esa Rahtu; Juho Kannala; Matthew B. Blaschko (ICCV, 2011)

Learning equivariant structured output SVM regressors. - Andrea Vedaldi; Matthew B. Blaschko; Andrew Zisserman (ICCV, 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)

Taxonomic Multi-class Prediction and Person Layout Using Efficient Structured Ranking. - Arpit Mittal; Matthew B. Blaschko; Andrew Zisserman; Philip H. S. Torr (ECCV (2), 2012)

Perceptron Learning of SAT. - Alex Flint; Matthew B. Blaschko (NIPS, 2012)

Guest Editorial: Special Issue on Structured Prediction and Inference. - Matthew B. Blaschko; Christoph H. Lampert (Int. J. Comput. Vis., 2012)

Learning from M/EEG Data with Variable Brain Activation Delays. - Wojciech Zaremba; M. Pawan Kumar; Alexandre Gramfort; Matthew B. Blaschko (IPMI, 2013)

FMRI analysis of cocaine addiction using k-support sparsity. - Katerina Gkirtzou; Jean Honorio; Dimitris Samaras; Rita Z. Goldstein; Matthew B. Blaschko (ISBI, 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 (MLMI, 2013)

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics. - Katerina Gkirtzou; Jean Honorio; Dimitris Samaras; Rita Z. Goldstein; Matthew B. Blaschko (MLMI, 2013)

B-test: A Non-parametric, Low Variance Kernel Two-sample Test. - Wojciech Zaremba; Arthur Gretton; Matthew B. Blaschko (NIPS, 2013)

Taxonomic Prediction with Tree-Structured Covariances. - Matthew B. Blaschko; Wojciech Zaremba; Arthur Gretton (ECML/PKDD (2), 2013)

Non Maximal Suppression in Cascaded Ranking Models. - Matthew B. Blaschko; Juho Kannala; Esa Rahtu (SCIA, 2013)

Fine-Grained Visual Classification of Aircraft. - Subhransu Maji; Esa Rahtu; Juho Kannala; Matthew B. Blaschko; Andrea Vedaldi (CoRR, 2013)

A Note on k-support Norm Regularized Risk Minimization - Matthew B. Blaschko (CoRR, 2013)

Unsupervised Spatio-Temporal Segmentation with Sparse Spectral-Clustering. - Mahsa Ghafarianzadeh; Matthew B. Blaschko; Gabe Sibley (BMVC, 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; et al. (CVPR, 2014)

An O(n log n) Cutting Plane Algorithm for Structured Output Ranking. - Matthew B. Blaschko; Arpit Mittal; Esa Rahtu (GCPR, 2014)

Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images. - José Ignacio Orlando; Matthew B. Blaschko (MICCAI (1), 2014)

A low variance consistent test of relative dependency. - Wacha Bounliphone; Arthur Gretton; Matthew B. Blaschko (CoRR, 2014)

A low variance consistent test of relative dependency. - Wacha Bounliphone; Arthur Gretton; Arthur Tenenhaus; Matthew B. Blaschko (ICML, 2015)

Learning Submodular Losses with the Lovasz Hinge. - Jiaqian Yu; Matthew B. Blaschko (ICML, 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)

The Lovász Hinge: A Convex Surrogate for Submodular Losses. - Jiaqian Yu; Matthew B. Blaschko (CoRR, 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)

A Convex Surrogate Operator for General Non-Modular Loss Functions. - Jiaqian Yu; Matthew B. Blaschko (AISTATS, 2016)

Efficient Learning for Discriminative Segmentation with Supermodular Losses. - Jiaqian Yu; Matthew B. Blaschko (BMVC, 2016)

Efficient, dense, object-based segmentation from RGBD video. - Mahsa Ghafarianzadeh; Matthew B. Blaschko; Gabe Sibley (ICRA, 2016)

Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity. - Eugene Belilovsky; Gaël Varoquaux; Matthew B. Blaschko (NIPS, 2016)

Discriminative training of CRF models with probably submodular constraints. - Wojciech Zaremba; Matthew B. Blaschko (WACV, 2016)

A Test of Relative Similarity For Model Selection in Generative Models. - Wacha Bounliphone; Eugene Belilovsky; Matthew B. Blaschko; Ioannis Antonoglou; Arthur Gretton (ICLR (Poster), 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)

Stochastic Function Norm Regularization of Deep Networks. - Amal Rannen Triki; Matthew B. Blaschko (CoRR, 2016)

The pyramid quantized Weisfeiler-Lehman graph representation. - Katerina Gkirtzou; Matthew B. Blaschko (Neurocomputing, 2016)

Discovering predictors of mental health service utilization with k-support regularized logistic regression. - Hakim Sidahmed; Elena Prokofyeva; Matthew B. Blaschko (Inf. Sci., 2016)

Slack and Margin Rescaling as Convex Extensions of Supermodular Functions. - Matthew B. Blaschko (EMMCVPR, 2017)

Encoder Based Lifelong Learning. - Amal Rannen Triki; Rahaf Aljundi; Matthew B. Blaschko; Tinne Tuytelaars (ICCV, 2017)

Joint Embeddings of Scene Graphs and Images. - Eugene Belilovsky; Matthew B. Blaschko; Jamie Ryan Kiros; Raquel Urtasun; Richard S. Zemel (ICLR (Workshop), 2017)

Learning to Discover Sparse Graphical Models. - Eugene Belilovsky; Kyle Kastner; Gaël Varoquaux; Matthew B. Blaschko (ICML, 2017)

Optimization of the Jaccard index for image segmentation with the Lovász hinge. - Maxim Berman; 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)

An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses. - Jiaqian Yu; Matthew B. Blaschko (CoRR, 2017)

Stochastic Weighted Function Norm Regularization. - Amal Rannen Triki; Maxim Berman; Matthew B. Blaschko (CoRR, 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)

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 (CVPR, 2018)

Shallow and Deep Models for Domain Adaptation problems. - Siamak Mehrkanoon; Matthew B. Blaschko; Johan A. K. Suykens (ESANN, 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 (MICCAI (2), 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)

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)

Efficient semantic image segmentation with superpixel pooling. - Mathijs Schuurmans; Maxim Berman; Matthew B. Blaschko (CoRR, 2018)

Supermodular Locality Sensitive Hashes. - Maxim Berman; Matthew B. Blaschko (CoRR, 2018)

Yes, IoU loss is submodular - as a function of the mispredictions. - Maxim Berman; Matthew B. Blaschko; Amal Rannen Triki; Jiaqian Yu (CoRR, 2018)

Adaptive Compression-based Lifelong Learning. - Shivangi Srivastava; Maxim Berman; Matthew B. Blaschko; Devis Tuia (BMVC, 2019)

Designing MacPherson Suspension Architectures Using Bayesian Optimization. - Sinnu Susan Thomas; Jacopo Palandri; Mohsen Lakehal-Ayat; Punarjay Chakravarty; Friedrich Wolf-Monheim; Matthew B. Blaschko (BNAIC/BENELEARN, 2019)

A Bayesian Optimization Framework for Neural Network Compression. - Xingchen Ma; Amal Rannen Triki; Maxim Berman; Christos Sagonas; Jacques Calì; Matthew B. Blaschko (ICCV, 2019)

Function Norms for Neural Networks. - Amal Rannen-Triki; Maxim Berman; Vladimir Kolmogorov; Matthew B. Blaschko (ICCV Workshops, 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 (MICCAI (2), 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)

Generating superpixels using deep image representations. - Thomas Verelst; Matthew B. Blaschko; Maxim Berman (CoRR, 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)

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)

Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization. - Xingchen Ma; Matthew B. Blaschko (AISTATS, 2020)

Learning to ground medical text in a 3D human atlas. - Dusan Grujicic; Gorjan Radevski; Tinne Tuytelaars; Matthew B. Blaschko (CoNLL, 2020)

AOWS: Adaptive and Optimal Network Width Search With Latency Constraints. - Maxim Berman; Leonid Pishchulin; Ning Xu; Matthew B. Blaschko; Gérard G. Medioni (CVPR, 2020)

Commands 4 Autonomous Vehicles (C4AV) Workshop Summary. - Thierry Deruyttere; Simon Vandenhende; Dusan Grujicic; Yu Liu 012; Luc Van Gool; Matthew B. Blaschko; Tinne Tuytelaars; Marie-Francine Moens (ECCV Workshops (2), 2020)

Discriminative Training of Conditional Random Fields with Probably Submodular Constraints. - Maxim Berman; Matthew B. Blaschko (Int. J. Comput. Vis., 2020)

The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses. - Jiaqian Yu; Matthew B. Blaschko (IEEE Trans. Pattern Anal. Mach. Intell., 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)

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)

R-GAP: Recursive Gradient Attack on Privacy. - Junyi Zhu 002; Matthew B. Blaschko (ICLR, 2021)

Meta-Cal: Well-controlled Post-hoc Calibration by Ranking. - Xingchen Ma; Matthew B. Blaschko (ICML, 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 (IGARSS, 2021)

Post Training Uncertainty Calibration Of Deep Networks For Medical Image Segmentation. - Axel-Jan Rousseau; Thijs Becker; Jeroen Bertels; Matthew B. Blaschko; Dirk Valkenborg (ISBI, 2021)

On the Relationship Between Calibrated Predictors and Unbiased Volume Estimation. - Teodora Popordanoska; Jeroen Bertels; Dirk Vandermeulen; Frederik Maes; Matthew B. Blaschko (MICCAI (1), 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)

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)

DeepProg: A Transformer-based Framework for Predicting Disease Prognosis. - Huy Hoang Nguyen; Simo Saarakkala; Matthew B. Blaschko; Aleksei Tiulpin (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)

Differentially Private SGD with Sparse Gradients. - Junyi Zhu 002; Matthew B. Blaschko (CoRR, 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)

Predicting Physical World Destinations for Commands Given to Self-Driving Cars. - Dusan Grujicic; Thierry Deruyttere; Marie-Francine Moens; Matthew B. Blaschko (AAAI, 2022)

KULeuven at LeQua 2022: Model Calibration in Quantification Learning. - Teodora Popordanoska; Matthew B. Blaschko (CLEF (Working Notes), 2022)

2-D latent space models: Layer-wise perceptual training and spatial grounding. - Dusan Grujicic; Matthew B. Blaschko (ICPR, 2022)

Combinatorial optimization for low bit-width neural networks. - Han Zhou 013; Aida Ashrafi; Matthew B. Blaschko (ICPR, 2022)

CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting. - Huy Hoang Nguyen; Simo Saarakkala; Matthew B. Blaschko; Aleksei Tiulpin (ISBI, 2022)

A Consistent and Differentiable Lp Canonical Calibration Error Estimator. - Teodora Popordanoska; Raphael Sayer; Matthew B. Blaschko (NeurIPS, 2022)

Optimizing Slimmable Networks for Multiple Target Platforms. - Zifu Wang; Matthew B. Blaschko (NLDL, 2022)

MRF-UNets: Searching UNet with Markov Random Fields. - Zifu Wang; Matthew B. Blaschko (ECML/PKDD (3), 2022)

Talk2Car: Predicting Physical Trajectories for Natural Language Commands. - Thierry Deruyttere; Dusan Grujicic; Matthew B. Blaschko; Marie-Francine Moens (IEEE Access, 2022)

Benchmarking Scalable Predictive Uncertainty in Text Classification. - Jordy Van Landeghem; Matthew B. Blaschko; Bertrand Anckaert; Marie-Francine Moens (IEEE Access, 2022)

On confidence intervals for precision matrices and the eigendecomposition of covariance matrices. - Teodora Popordanoska; Aleksei Tiulpin; Wacha Bounliphone; Matthew B. Blaschko (CoRR, 2022)

Students taught by multimodal teachers are superior action recognizers. - Gorjan Radevski; Dusan Grujicic; Matthew B. Blaschko; Marie-Francine Moens; Tinne Tuytelaars (CoRR, 2022)

Greedy Bayesian Posterior Approximation with Deep Ensembles. - Aleksei Tiulpin; Matthew B. Blaschko (Trans. Mach. Learn. Res., 2022)

A Corrected Expected Improvement Acquisition Function Under Noisy Observations. - Han Zhou 013; Xingchen Ma; Matthew B. Blaschko (ACML, 2023)

Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization. - Junyi Zhu 002; Xingchen Ma; Matthew B. Blaschko (CVPR, 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; et al. (ICCV, 2023)

Multimodal Distillation for Egocentric Action Recognition. - Gorjan Radevski; Dusan Grujicic; Matthew B. Blaschko; Marie-Francine Moens; Tinne Tuytelaars (ICCV, 2023)

Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. - Junyi Zhu 002; Ruicong Yao; Matthew B. Blaschko (ICML, 2023)

Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels. - Zifu Wang; Teodora Popordanoska; Jeroen Bertels; Robin Lemmens; Matthew B. Blaschko (MICCAI (3), 2023)

Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction. - Wangduo Xie; Matthew B. Blaschko (MICCAI (1), 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 (NeurIPS, 2023)

Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels. - Zifu Wang; Xuefei Ning; Matthew B. Blaschko (NeurIPS, 2023)

Improved Imagery Throughput via Cascaded Uncertainty Pruning on U-Net++. - Mingshi Li; Zifu Wang; Matthew B. Blaschko (NLDL, 2023)

Understanding metric-related pitfalls in image analysis validation. - Annika Reinke; Minu Tizabi; Michael Baumgartner 001; Matthias Eisenmann; Doreen Heckmann-Nötzel; A. Emre Kavur; Tim Rädsch; Carole H. Sudre; Laura Ación; Michela Antonelli; Tal Arbel; Spyridon Bakas; et al. (CoRR, 2023)

Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels. - Zifu Wang; 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 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; et al. (npj Digit. Medicine, 2023)

Improving Differentially Private SGD via Randomly Sparsified Gradients. - Junyi Zhu 002; Matthew B. Blaschko (Trans. Mach. Learn. Res., 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., 2023)

Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors. - Teodora Popordanoska; Sebastian Gregor Gruber; Aleksei Tiulpin; Florian Büttner 001; Matthew B. Blaschko (AISTATS, 2024)

Improving Multimodal Learning with Multi-Loss Gradient Modulation. - Konstantinos Kontras; Christos Chatzichristos; Matthew B. Blaschko; Maarten De Vos (BMVC, 2024)

FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models. - Junyi Zhu 002; Shuochen Liu; Yu Yu; Bo Tang; Yibo Yan; Zhiyu Li; Feiyu Xiong; Tong Xu; Matthew B. Blaschko (EMNLP (Findings), 2024)

A Generic Method for Fine-grained Category Discovery in Natural Language Texts. - Chang Tian; Matthew B. Blaschko; Wenpeng Yin 001; Mingzhe Xing; Yinliang Yue; Marie-Francine Moens (EMNLP, 2024)

Multimodal Adaptive Inference for Document Image Classification with Anytime Early Exiting. - Omar Hamed; Souhail Bakkali; Matthew B. Blaschko; Sien Moens; Jordy Van Landeghem (ICDAR (4), 2024)

DistilDoc: Knowledge Distillation for Visually-Rich Document Applications. - Jordy Van Landeghem; Subhajit Maity; Ayan Banerjee 002; Matthew B. Blaschko; Marie-Francine Moens; Josep Lladós 001; Sanket Biswas (ICDAR (4), 2024)

Rescaling Intermediate Features Makes Trained Consistency Models Perform Better. - Junyi Zhu 002; Zinan Lin 001; Enshu Liu; Xuefei Ning; Matthew B. Blaschko (Tiny Papers @ ICLR, 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 (IGARSS, 2024)

Laparoflow-SSL: Image Analysis From a Tiny Dataset Through Self-Supervised Transformers Leveraging Unlabeled Surgical Video. - Karel G. M. Moens; Jonas De Vylder; Matthew B. Blaschko; Tinne Tuytelaars (MIDL, 2024)

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation? - Pedro R. A. S. Bassi; Wenxuan Li; Yucheng Tang; Fabian Isensee; Zifu Wang; Jieneng Chen; Yu-Cheng Chou; Yannick Kirchhoff; Maximilian Rokuss; Ziyan Huang; Jin Ye 002; Junjun He; et al. (NeurIPS, 2024)

Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study. - Xuefei Ning; Zifu Wang; Shiyao Li; Zinan Lin 001; Peiran Yao; Tianyu Fu 004; Matthew B. Blaschko; Guohao Dai; Huazhong Yang; Yu Wang 002 (NeurIPS, 2024)

LaSCal: Label-Shift Calibration without target labels. - Teodora Popordanoska; Gorjan Radevski; Tinne Tuytelaars; Matthew B. Blaschko (NeurIPS, 2024)

Beyond Document Page Classification: Design, Datasets, and Challenges. - Jordy Van Landeghem; Sanket Biswas; Matthew B. Blaschko; Marie-Francine Moens (WACV, 2024)

Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection. - Teodora Popordanoska; Aleksei Tiulpin; Matthew B. Blaschko (WACV, 2024)

The Common Stability Mechanism behind most Self-Supervised Learning Approaches. - Abhishek Jha 001; Matthew B. Blaschko; Yuki M. Asano; Tinne Tuytelaars (CoRR, 2024)

Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better. - Enshu Liu; Junyi Zhu 002; Zinan Lin 001; Xuefei Ning; Matthew B. Blaschko; Sergey Yekhanin; Shengen Yan; Guohao Dai; Huazhong Yang; Yu Wang 002 (CoRR, 2024)

Can LLMs Learn by Teaching? A Preliminary Study. - Xuefei Ning; Zifu Wang; Shiyao Li; Zinan Lin 001; Peiran Yao; Tianyu Fu 004; Matthew B. Blaschko; Guohao Dai; Huazhong Yang; Yu Wang 002 (CoRR, 2024)

Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs. - Enshu Liu; Junyi Zhu 002; Zinan Lin 001; Xuefei Ning; Matthew B. Blaschko; Shengen Yan; Guohao Dai; Huazhong Yang; Yu Wang 002 (CoRR, 2024)

Redundancy-Aware Camera Selection for Indoor Scene Neural Rendering. - Zehao Wang; Han Zhou 013; Matthew B. Blaschko; Tinne Tuytelaars; Minye Wu (CoRR, 2024)

A Novel Characterization of the Population Area Under the Risk Coverage Curve (AURC) and Rates of Finite Sample Estimators. - Han Zhou 013; Jordy Van Landeghem; Teodora Popordanoska; Matthew B. Blaschko (CoRR, 2024)

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation? - Pedro R. A. S. Bassi; Wenxuan Li; Yucheng Tang; Fabian Isensee; Zifu Wang; Jieneng Chen; Yu-Cheng Chou; Yannick Kirchhoff; Maximilian Rokuss; Ziyan Huang; Jin Ye 002; Junjun He; et al. (CoRR, 2024)

Multimodal Fusion Balancing Through Game-Theoretic Regularization. - Konstantinos Kontras; Thomas Strypsteen; Christos Chatzichristos; Paul P. Liang; Matthew B. Blaschko; Maarten De Vos (CoRR, 2024)

Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction. - Jiayang Shi; Junyi Zhu 002; Daniël Maria Pelt; Kees Joost Batenburg; Matthew B. Blaschko (Trans. Mach. Learn. Res., 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, 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., 2024)

Temporal Transferability of Tree Species Classification in Temperate Forests with Sentinel-2 Time Series. - Margot Verhulst; Stien Heremans; Matthew B. Blaschko; Ben Somers (Remote. Sens., 2024)

Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better. - Enshu Liu; Junyi Zhu 002; Zinan Lin 001; Xuefei Ning; Shuaiqi Wang; Matthew B. Blaschko; Sergey Yekhanin; Shengen Yan; Guohao Dai; Huazhong Yang; Yu Wang 002 (ICLR, 2025)

Bayesian Optimization over Bounded Domains with the Beta Product Kernel. - Huy Hoang Nguyen; Han Zhou 013; Matthew B. Blaschko; Aleksei Tiulpin (UAI, 2025)

AC-IND: Sparse CT Reconstruction Based on Attenuation Coefficient Estimation and Implicit Neural Distribution. - Wangduo Xie; Richard Schoonhoven; Tristan van Leeuwen; Matthew B. Blaschko (WACV, 2025)

Diversity-Driven View Subset Selection for Indoor Novel View Synthesis. - Zehao Wang; Han Zhou 013; Matthew B. Blaschko; Tinne Tuytelaars; Minye Wu (Trans. Mach. Learn. Res., 2025)

Using Causality for Enhanced Prediction of Web Traffic Time Series. - Chang Tian; Mingzhe Xing; Zenglin Shi; Matthew B. Blaschko; Yinliang Yue; Marie-Francine Moens (CoRR, 2025)

ChromaFormer: A Scalable and Accurate Transformer Architecture for Land Cover Classification. - Mingshi Li; Dusan Grujicic; Ben Somers; Stien Heremans; Steven De Saeger; Matthew B. Blaschko (CoRR, 2025)

DAVE: Diagnostic benchmark for Audio Visual Evaluation. - Gorjan Radevski; Teodora Popordanoska; Matthew B. Blaschko; Tinne Tuytelaars (CoRR, 2025)

Unlocking the Value of Decentralized Data: A Federated Dual Learning Approach for Model Aggregation. - Junyi Zhu 002; Ruicong Yao; Taha Ceritli; Savas Özkan; Matthew B. Blaschko; Eunchung Noh; Jeongwon Min; Cho Jung Min; Mete Ozay (CoRR, 2025)

Surgeons vs. Computer Vision: A comparative analysis on surgical phase recognition capabilities. - Marco Mezzina; Pieter De Backer; Tom Vercauteren; Matthew B. Blaschko; Alexandre Mottrie; Tinne Tuytelaars (CoRR, 2025)

Beyond Segmentation: Confidence-Aware and Debiased Estimation of Ratio-based Biomarkers. - Jiameng Li; Teodora Popordanoska; Sebastian G. Gruber; Frederik Maes; Matthew B. Blaschko (CoRR, 2025)

Revisiting Reweighted Risk for Calibration: AURC, Focal Loss, and Inverse Focal Loss. - Han Zhou 013; Sebastian G. Gruber; Teodora Popordanoska; Matthew B. Blaschko (CoRR, 2025)

Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles. - Zifu Wang; Junyi Zhu 002; Bo Tang; Zhiyu Li; Feiyu Xiong; Jiaqian Yu; Matthew B. Blaschko (CoRR, 2025)

Surgeons versus computer vision: a comparative analysis on surgical phase recognition capabilities. - Marco Mezzina; Pieter De Backer; Tom Vercauteren; Matthew B. Blaschko; Alexandre Mottrie; Tinne Tuytelaars (Int. J. Comput. Assist. Radiol. Surg., 2025)

Large Language Models Reasoning Abilities Under Non-Ideal Conditions After RL-Fine-Tuning. - Chang Tian; Matthew B. Blaschko; Mingzhe Xing; Xiuxing Li; Yinliang Yue; Marie-Francine Moens (CoRR, 2025)