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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
- 2024
[j29]Margot Verhulst, Stien Heremans
, Matthew B. Blaschko, Ben Somers:
Temporal Transferability of Tree Species Classification in Temperate Forests with Sentinel-2 Time Series. Remote. Sens. 16(14): 2653 (2024)- [j28]Sinnu Susan Thomas, Guillaume Lamine, Jacopo Palandri, Mohsen Lakehal-Ayat, Punarjay Chakravarty, Friedrich Wolf-Monheim, Matthew B. Blaschko:
Mitigating Bias in Bayesian Optimized Data While Designing MacPherson Suspension Architecture. IEEE Trans. Artif. Intell. 5(2): 904-915 (2024) - [j27]Huy Hoang Nguyen, Matthew B. Blaschko, Simo Saarakkala, Aleksei Tiulpin:
Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data. IEEE Trans. Medical Imaging 43(1): 529-541 (2024) - [c85]Teodora Popordanoska, Sebastian Gregor Gruber, Aleksei Tiulpin, Florian Büttner, Matthew B. Blaschko:
Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors. AISTATS 2024: 3466-3474 - [c84]Chang Tian, Matthew B. Blaschko, Wenpeng Yin, Mingzhe Xing, Yinliang Yue, Marie-Francine Moens:
A Generic Method for Fine-grained Category Discovery in Natural Language Texts. EMNLP 2024: 3548-3566 - [c83]Junyi Zhu, Shuochen Liu, Yu Yu, Bo Tang, Yibo Yan, Zhiyu Li, Feiyu Xiong, Tong Xu, Matthew B. Blaschko:
FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models. EMNLP (Findings) 2024: 11740-11758 - [c82]Jordy Van Landeghem, Subhajit Maity, Ayan Banerjee, Matthew B. Blaschko, Marie-Francine Moens, Josep Lladós, Sanket Biswas:
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications. ICDAR (4) 2024: 195-217 - [c81]Omar Hamed, Souhail Bakkali, Matthew B. Blaschko, Sien Moens, Jordy Van Landeghem:
Multimodal Adaptive Inference for Document Image Classification with Anytime Early Exiting. ICDAR (4) 2024: 270-286 - [c80]Junyi Zhu, Zinan Lin, Enshu Liu, Xuefei Ning, Matthew B. Blaschko:
Rescaling Intermediate Features Makes Trained Consistency Models Perform Better. Tiny Papers @ ICLR 2024 - [c79]Mingshi Li, Dusan Grujicic, Steven De Saeger, Stien Heremans, Ben Somers, Matthew B. Blaschko:
Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis. IGARSS 2024: 9539-9543 - [c78]Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang, Fabian Isensee, Zifu Wang, Jieneng Chen, Yu-Cheng Chou, Yannick Kirchhoff, Maximilian R. Rokuss, Ziyan Huang, Jin Ye, Junjun He, Tassilo Wald, Constantin Ulrich, Michael Baumgartner, Saikat Roy, Klaus H. Maier-Hein, Paul F. Jaeger, Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen, Yong Xia, Zhaohu Xing, Lei Zhu, Yousef Sadegheih, Afshin Bozorgpour, Pratibha Kumari, Reza Azad, Dorit Merhof, Pengcheng Shi, Ting Ma, Yuxin Du, Fan Bai, Tiejun Huang, Bo Zhao, Haonan Wang, Xiaomeng Li, Hanxue Gu, Haoyu Dong, Jichen Yang, Maciej A. Mazurowski, Saumya Gupta, Linshan Wu, Jia-Xin Zhuang, Hao Chen, Holger Roth, Daguang Xu, Matthew B. Blaschko, Sergio Decherchi, Andrea Cavalli, Alan L. Yuille, Zongwei Zhou:
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation? NeurIPS 2024 - [c77]Teodora Popordanoska, Aleksei Tiulpin, Matthew B. Blaschko:
Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection. WACV 2024: 574-583 - [c76]Jordy Van Landeghem, Sanket Biswas, Matthew B. Blaschko, Marie-Francine Moens:
Beyond Document Page Classification: Design, Datasets, and Challenges. WACV 2024: 2950-2960 - [i76]Mingshi Li, Dusan Grujicic, Steven De Saeger, Stien Heremans, Ben Somers, Matthew B. Blaschko:
Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis. CoRR abs/2401.15223 (2024) - [i75]Abhishek Jha, Matthew B. Blaschko, Yuki M. Asano, Tinne Tuytelaars:
The Common Stability Mechanism behind most Self-Supervised Learning Approaches. CoRR abs/2402.14957 (2024) - [i74]Enshu Liu, Junyi Zhu, Zinan Lin, Xuefei Ning, Matthew B. Blaschko, Sergey Yekhanin, Shengen Yan, Guohao Dai, Huazhong Yang, Yu Wang:
Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better. CoRR abs/2404.02241 (2024) - [i73]Jiayang Shi, Junyi Zhu, Daniël Maria Pelt, Kees Joost Batenburg, Matthew B. Blaschko:
Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction. CoRR abs/2405.02509 (2024) - [i72]Konstantinos Kontras, Christos Chatzichristos, Matthew B. Blaschko, Maarten De Vos:
Improving Multimodal Learning with Multi-Loss Gradient Modulation. CoRR abs/2405.07930 (2024) - [i71]Omar Hamed, Souhail Bakkali, Marie-Francine Moens, Matthew B. Blaschko, Jordy Van Landeghem:
Multimodal Adaptive Inference for Document Image Classification with Anytime Early Exiting. CoRR abs/2405.12705 (2024) - [i70]Jordy Van Landeghem, Subhajit Maity, Ayan Banerjee, Matthew B. Blaschko, Marie-Francine Moens, Josep Lladós, Sanket Biswas:
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications. CoRR abs/2406.08226 (2024) - [i69]Chang Tian, Matthew B. Blaschko, Wenpeng Yin, Mingzhe Xing, Yinliang Yue, Marie-Francine Moens:
A Generic Method for Fine-grained Category Discovery in Natural Language Texts. CoRR abs/2406.13103 (2024) - [i68]Xuefei Ning, Zifu Wang, Shiyao Li, Zinan Lin, Peiran Yao, Tianyu Fu, Matthew B. Blaschko, Guohao Dai, Huazhong Yang, Yu Wang:
Can LLMs Learn by Teaching? A Preliminary Study. CoRR abs/2406.14629 (2024) - [i67]Junyi Zhu, Shuochen Liu, Yu Yu, Bo Tang, Yibo Yan, Zhiyu Li, Feiyu Xiong, Tong Xu, Matthew B. Blaschko:
FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models. CoRR abs/2406.16069 (2024) - [i66]Enshu Liu, Junyi Zhu, Zinan Lin, Xuefei Ning, Matthew B. Blaschko, Shengen Yan, Guohao Dai, Huazhong Yang, Yu Wang:
Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs. CoRR abs/2407.00945 (2024) - [i65]Zehao Wang, Han Zhou, Matthew B. Blaschko, Tinne Tuytelaars, Minye Wu:
Redundancy-Aware Camera Selection for Indoor Scene Neural Rendering. CoRR abs/2409.07098 (2024) - [i64]Wangduo Xie, Richard Schoonhoven, Tristan van Leeuwen, Matthew B. Blaschko:
AC-IND: Sparse CT reconstruction based on attenuation coefficient estimation and implicit neural distribution. CoRR abs/2409.07171 (2024) - [i63]Han Zhou, Jordy Van Landeghem, Teodora Popordanoska, Matthew B. Blaschko:
A Novel Characterization of the Population Area Under the Risk Coverage Curve (AURC) and Rates of Finite Sample Estimators. CoRR abs/2410.15361 (2024) - [i62]Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang, Fabian Isensee, Zifu Wang, Jieneng Chen, Yu-Cheng Chou, Yannick Kirchhoff, Maximilian R. Rokuss, Ziyan Huang, Jin Ye, Junjun He, Tassilo Wald, Constantin Ulrich, Michael Baumgartner, Saikat Roy, Klaus H. Maier-Hein, Paul F. Jaeger, Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen, Yong Xia, Zhaohu Xing, Lei Zhu, Yousef Sadegheih, Afshin Bozorgpour, Pratibha Kumari, Reza Azad, Dorit Merhof, Pengcheng Shi, Ting Ma, Yuxin Du, Fan Bai, Tiejun Huang, Bo Zhao, Haonan Wang, Xiaomeng Li, Hanxue Gu, Haoyu Dong, Jichen Yang, Maciej A. Mazurowski, Saumya Gupta, Linshan Wu, Jiaxin Zhuang, Hao Chen, Holger Roth, Daguang Xu, Matthew B. Blaschko, Sergio Decherchi, Andrea Cavalli, Alan L. Yuille, Zongwei Zhou:
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation? CoRR abs/2411.03670 (2024) - [i61]Konstantinos Kontras, Thomas Strypsteen, Christos Chatzichristos, Paul P. Liang, Matthew B. Blaschko, Maarten De Vos:
Multimodal Fusion Balancing Through Game-Theoretic Regularization. CoRR abs/2411.07335 (2024) - 2023
- [j26]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:
A generalizable deep learning regression model for automated glaucoma screening from fundus images. npj Digit. Medicine 6 (2023) - [j25]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) - [j24]Junyi Zhu, Matthew B. Blaschko:
Improving Differentially Private SGD via Randomly Sparsified Gradients. Trans. Mach. Learn. Res. 2023 (2023) - [c75]Han Zhou, Xingchen Ma, Matthew B. Blaschko:
A Corrected Expected Improvement Acquisition Function Under Noisy Observations. ACML 2023: 1747-1762 - [c74]Junyi Zhu, Xingchen Ma, Matthew B. Blaschko:
Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization. CVPR 2023: 24542-24551 - [c73]Gorjan Radevski, Dusan Grujicic, Matthew B. Blaschko, Marie-Francine Moens, Tinne Tuytelaars:
Multimodal Distillation for Egocentric Action Recognition. ICCV 2023: 5190-5201 - [c72]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:
Document Understanding Dataset and Evaluation (DUDE). ICCV 2023: 19471-19483 - [c71]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 - [c70]Wangduo Xie, Matthew B. Blaschko:
Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction. MICCAI (1) 2023: 77-86 - [c69]Zifu Wang, Teodora Popordanoska, Jeroen Bertels, Robin Lemmens, Matthew B. Blaschko:
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels. MICCAI (3) 2023: 475-485 - [c68]Zifu Wang, Maxim Berman, Amal Rannen-Triki, Philip H. S. Torr, Devis Tuia, Tinne Tuytelaars, Luc Van Gool, Jiaqian Yu, Matthew B. Blaschko:
Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union. NeurIPS 2023 - [c67]Zifu Wang, Xuefei Ning, Matthew B. Blaschko:
Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels. NeurIPS 2023 - [c66]Mingshi Li, Zifu Wang, Matthew B. Blaschko:
Improved Imagery Throughput via Cascaded Uncertainty Pruning on U-Net++. NLDL 2023 - [i60]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) - [i59]Zifu Wang, Matthew B. Blaschko:
Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels. CoRR abs/2302.05666 (2023) - [i58]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) - [i57]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) - [i56]Junyi Zhu, Xingchen Ma, Matthew B. Blaschko:
Confidence-aware Personalized Federated Learning via Variational Expectation Maximization. CoRR abs/2305.12557 (2023) - [i55]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) - [i54]Gorjan Radevski, Dusan Grujicic, Marie-Francine Moens, Matthew B. Blaschko, Tinne Tuytelaars:
Multimodal Distillation for Egocentric Action Recognition. CoRR abs/2307.07483 (2023) - [i53]Wangduo Xie, Matthew B. Blaschko:
Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction. CoRR abs/2307.12717 (2023) - [i52]Jordy Van Landeghem, Sanket Biswas, Matthew B. Blaschko, Marie-Francine Moens:
Beyond Document Page Classification: Design, Datasets, and Challenges. CoRR abs/2308.12896 (2023) - [i51]Han Zhou, Xingchen Ma, Matthew B. Blaschko:
A Corrected Expected Improvement Acquisition Function Under Noisy Observations. CoRR abs/2310.05166 (2023) - [i50]Zifu Wang, Maxim Berman, Amal Rannen Triki, Philip H. S. Torr, Devis Tuia, Tinne Tuytelaars, Luc Van Gool, Jiaqian Yu, Matthew B. Blaschko:
Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union. CoRR abs/2310.19252 (2023) - [i49]Teodora Popordanoska, Aleksei Tiulpin, Matthew B. Blaschko:
Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection. CoRR abs/2312.06645 (2023) - [i48]Teodora Popordanoska, Gorjan Radevski, Tinne Tuytelaars, Matthew B. Blaschko:
Estimating calibration error under label shift without labels. CoRR abs/2312.08586 (2023) - [i47]Teodora Popordanoska, Sebastian G. Gruber, Aleksei Tiulpin, Florian Büttner, Matthew B. Blaschko:
Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors. CoRR abs/2312.08589 (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]Wojciech Zaremba, M. Pawan Kumar, Alexandre Gramfort, Matthew B. Blaschko:
Learning from M/EEG Data with Variable Brain Activation Delays. IPMI 2013: 414-425 - [c22]Katerina Gkirtzou, Jean Honorio, Dimitris Samaras, Rita Z. Goldstein, Matthew B. Blaschko:
FMRI analysis of cocaine addiction using k-support sparsity. ISBI 2013: 1078-1081 - [c21]Katerina Gkirtzou, Jean-François Deux, Guillaume Bassez, Aristeidis Sotiras, Alain Rahmouni, Thibault Varacca, Nikos Paragios, Matthew B. Blaschko:
Sparse Classification with MRI Based Markers for Neuromuscular Disease Categorization. MLMI 2013: 33-40 - [c20]Katerina Gkirtzou, Jean Honorio, Dimitris Samaras, Rita Z. Goldstein, Matthew B. Blaschko:
fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics. MLMI 2013: 90-97 - [c19]Wojciech Zaremba, Arthur Gretton, Matthew B. Blaschko:
B-test: A Non-parametric, Low Variance Kernel Two-sample Test. NIPS 2013: 755-763 - [c18]Matthew B. Blaschko, Wojciech Zaremba, Arthur Gretton:
Taxonomic Prediction with Tree-Structured Covariances. ECML/PKDD (2) 2013: 304-319 - [c17]Matthew B. Blaschko, Juho Kannala, Esa Rahtu:
Non Maximal Suppression in Cascaded Ranking Models. SCIA 2013: 408-419 - [i3]Matthew B. Blaschko:
A Note on k-support Norm Regularized Risk Minimization. CoRR abs/1303.6390 (2013) - [i2]Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew B. Blaschko, Andrea Vedaldi:
Fine-Grained Visual Classification of Aircraft. CoRR abs/1306.5151 (2013) - [i1]Wojciech Zaremba, Arthur Gretton, Matthew B. Blaschko:
B-test: A Non-parametric, Low Variance Kernel Two-sample Test. CoRR abs/1307.1954 (2013) - 2012
- [j5]Matthew B. Blaschko, Christoph H. Lampert:
Guest Editorial: Special Issue on Structured Prediction and Inference. Int. J. Comput. Vis. 99(3): 257-258 (2012) - [c16]Arpit Mittal, Matthew B. Blaschko, Andrew Zisserman, Philip H. S. Torr:
Taxonomic Multi-class Prediction and Person Layout Using Efficient Structured Ranking. ECCV (2) 2012: 245-258 - [c15]Alex Flint, Matthew B. Blaschko:
Perceptron Learning of SAT. NIPS 2012: 2780-2788 - 2011
- [j4]Matthew B. Blaschko, Jacquelyn A. Shelton, Andreas M. Bartels, Christoph H. Lampert, Arthur Gretton:
Semi-supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognit. Lett. 32(11): 1572-1583 (2011) - [c14]Matthew B. Blaschko:
Branch and Bound Strategies for Non-maximal Suppression in Object Detection. EMMCVPR 2011: 385-398 - [c13]Andrea Vedaldi, Matthew B. Blaschko, Andrew Zisserman:
Learning equivariant structured output SVM regressors. ICCV 2011: 959-966 - [c12]Esa Rahtu, Juho Kannala, Matthew B. Blaschko:
Learning a category independent object detection cascade. ICCV 2011: 1052-1059 - 2010
- [j3]Tinne Tuytelaars, Christoph H. Lampert, Matthew B. Blaschko, Wray L. Buntine:
Unsupervised Object Discovery: A Comparison. Int. J. Comput. Vis. 88(2): 284-302 (2010) - [c11]Matthew B. Blaschko, Andrea Vedaldi, Andrew Zisserman:
Simultaneous Object Detection and Ranking with Weak Supervision. NIPS 2010: 235-243
2000 – 2009
- 2009
- [b1]Matthew Brian Blaschko:
Kernel methods in computer vision: object localization, clustering, and taxonomy discovery. Berlin Institute of Technology, 2009 - [j2]Christoph H. Lampert, Matthew B. Blaschko:
Structured prediction by joint kernel support estimation. Mach. Learn. 77(2-3): 249-269 (2009) - [j1]Christoph H. Lampert, Matthew B. Blaschko, Thomas Hofmann:
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization. IEEE Trans. Pattern Anal. Mach. Intell. 31(12): 2129-2142 (2009) - [c10]Matthew B. Blaschko, Christoph H. Lampert:
Object Localization with Global and Local Context Kernels. BMVC 2009: 1-11 - [c9]Matthew B. Blaschko, Jacquelyn A. Shelton, Andreas M. Bartels:
Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity. NIPS 2009: 126-134 - 2008
- [c8]Matthew B. Blaschko, Christoph H. Lampert:
Correlational spectral clustering. CVPR 2008 - [c7]Christoph H. Lampert, Matthew B. Blaschko, Thomas Hofmann:
Beyond sliding windows: Object localization by efficient subwindow search. CVPR 2008 - [c6]Christoph H. Lampert, Matthew B. Blaschko:
A Multiple Kernel Learning Approach to Joint Multi-class Object Detection. DAGM-Symposium 2008: 31-40 - [c5]Matthew B. Blaschko, Christoph H. Lampert:
Learning to Localize Objects with Structured Output Regression. ECCV (1) 2008: 2-15 - [c4]Matthew B. Blaschko, Arthur Gretton:
Learning Taxonomies by Dependence Maximization. NIPS 2008: 153-160 - [c3]Matthew B. Blaschko, Christoph H. Lampert, Arthur Gretton:
Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis. ECML/PKDD (1) 2008: 133-145 - 2005
- [c2]Dimitri A. Lisin, Marwan A. Mattar, Matthew B. Blaschko, Erik G. Learned-Miller, Mark C. Benfield:
Combining Local and Global Image Features for Object Class Recognition. CVPR Workshops 2005: 47 - [c1]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:
Automatic In Situ Identification of Plankton. WACV/MOTION 2005: 79-86
Coauthor Index
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