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ORCIDSándor Szedmák
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2020 – today
- 2024
[j20]Robert Ebo Armah-Sekum
, Sándor Szedmák, Juho Rousu:
Protein function prediction through multi-view multi-label latent tensor reconstruction. BMC Bioinform. 25(1): 174 (2024)- [j19]Sándor Szedmák, Riikka Huusari, Tat Hong Duong Le, Juho Rousu:
Scalable variable selection for two-view learning tasks with projection operators. Mach. Learn. 113(6): 3525-3544 (2024) - 2023
- [c40]Mubina Kamberovic, Senka Krivic, Amra Delic, Sándor Szedmák, Vedran Ljubovic:
Personalized Learning Systems for Computer Science Students: Analyzing and Predicting Learning Behaviors Using Programming Error Data. UMAP (Adjunct Publication) 2023: 86-91 - [i3]Sándor Szedmák, Riikka Huusari, Tat Hong Duong Le, Juho Rousu:
Scalable variable selection for two-view learning tasks with projection operators. CoRR abs/2307.01558 (2023) - 2022
- [j18]Maryam Sabzevari, Sándor Szedmák, Merja Penttilä, Paula Jouhten, Juho Rousu:
Strain design optimization using reinforcement learning. PLoS Comput. Biol. 18(6) (2022) - 2021
- [j17]Tianduanyi Wang, Sándor Szedmák, Haishan Wang, Tero Aittokallio, Tapio Pahikkala, Anna Cichonska, Juho Rousu:
Modeling drug combination effects via latent tensor reconstruction. Bioinform. 37(Supplement): 93-101 (2021) - 2020
- [j16]Senka Krivic, Michael Cashmore, Daniele Magazzeni, Sándor Szedmák, Justus H. Piater:
Using Machine Learning for Decreasing State Uncertainty in Planning. J. Artif. Intell. Res. 69: 765-806 (2020) - [i2]Sándor Szedmák, Anna Cichonska, Heli Julkunen, Tapio Pahikkala, Juho Rousu:
A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction. CoRR abs/2005.01538 (2020)
2010 – 2019
- 2018
- [j15]Anna Cichonska, Tapio Pahikkala, Sándor Szedmák, Heli Julkunen, Antti Airola, Markus Heinonen, Tero Aittokallio, Juho Rousu:
Learning with multiple pairwise kernels for drug bioactivity prediction. Bioinform. 34(13): i509-i518 (2018) - [j14]Eric Bach, Sándor Szedmák, Céline Brouard, Sebastian Böcker, Juho Rousu:
Liquid-chromatography retention order prediction for metabolite identification. Bioinform. 34(17): i875-i883 (2018) - [j13]Mirko Wächter, Ekaterina Ovchinnikova, Valerij Wittenbeck, Peter Kaiser, Sándor Szedmák, Wail Mustafa, Dirk Kraft, Norbert Krüger, Justus H. Piater, Tamim Asfour:
Integrating multi-purpose natural language understanding, robot's memory, and symbolic planning for task execution in humanoid robots. Robotics Auton. Syst. 99: 148-165 (2018) - 2017
- [j12]Kitsuchart Pasupa, Sándor Szedmák:
Utilising Kronecker Decomposition and Tensor-based Multi-view Learning to predict where people are looking in images. Neurocomputing 248: 80-93 (2017) - [c39]Senka Krivic, Michael Cashmore, Bram Ridder, Daniele Magazzeni, Sándor Szedmák, Justus H. Piater:
Initial State Prediction in Planning. AAAI Workshops 2017 - [c38]Senka Krivic, Michael Cashmore, Daniele Magazzeni, Bram Ridder, Sándor Szedmák, Justus H. Piater:
Decreasing Uncertainty in Planning with State Prediction. IJCAI 2017: 2032-2038 - 2016
- [j11]Hanchen Xiong, Sándor Szedmák, Justus H. Piater:
Learning undirected graphical models using persistent sequential Monte Carlo. Mach. Learn. 103(2): 239-260 (2016) - [c37]Sabrina Fontanella, Antonio Jose Rodríguez-Sánchez, Justus H. Piater, Sándor Szedmák:
Kronecker Decomposition for Image Classification. CLEF 2016: 137-149 - [c36]Huibin Shen, Sándor Szedmák, Céline Brouard, Juho Rousu:
Soft Kernel Target Alignment for Two-Stage Multiple Kernel Learning. DS 2016: 427-441 - [c35]Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater:
Robotic playing for hierarchical complex skill learning. IROS 2016: 2799-2804 - [i1]Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater:
Hierarchical Haptic Manipulation for Complex Skill Learning. CoRR abs/1603.00794 (2016) - 2015
- [j10]Hanchen Xiong, Antonio Jose Rodríguez-Sánchez, Sándor Szedmák, Justus H. Piater:
Diversity priors for learning early visual features. Frontiers Comput. Neurosci. 9: 104 (2015) - [j9]Hanchen Xiong, Sándor Szedmák, Justus H. Piater:
Scalable, accurate image annotation with joint SVMs and output kernels. Neurocomputing 169: 205-214 (2015) - [c34]Wail Mustafa, Hanchen Xiong, Dirk Kraft, Sándor Szedmák, Justus H. Piater, Norbert Krüger:
Multi-label Object Categorization Using Histograms of Global Relations. 3DV 2015: 309-317 - [c33]Thomas Hoyoux, Antonio Jose Rodríguez-Sánchez, Justus H. Piater, Sándor Szedmák:
Can Computer Vision Problems Benefit from Structured Hierarchical Classification? CAIP (2) 2015: 403-414 - [c32]Antonio Jose Rodríguez-Sánchez, Sabrina Fontanella, Justus H. Piater, Sándor Szedmák:
IIS at ImageCLEF 2015: Multi-label Classification Task. CLEF (Working Notes) 2015 - [c31]Senka Krivic, Sándor Szedmák, Hanchen Xiong, Justus H. Piater:
Learning missing edges via kernels in partially-known graphs. ESANN 2015 - [c30]Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater, Ales Ude:
Reactive, task-specific object manipulation by metric reinforcement learning. ICAR 2015: 557-564 - [c29]Kitsuchart Pasupa, Sándor Szedmák:
Learning to Predict Where People Look with Tensor-Based Multi-view Learning. ICONIP (1) 2015: 432-441 - [c28]Antonio Jose Rodríguez-Sánchez, Sándor Szedmák, Justus H. Piater:
SCurV: A 3D descriptor for object classification. IROS 2015: 1320-1327 - [c27]Alejandro Agostini, Mohamad Javad Aein, Sándor Szedmák, Eren Erdal Aksoy, Justus H. Piater, Florentin Wörgötter:
Using structural bootstrapping for object substitution in robotic executions of human-like manipulation tasks. IROS 2015: 6479-6486 - 2014
- [c26]Hanchen Xiong, Sándor Szedmák, Justus H. Piater:
Towards Maximum Likelihood: Learning Undirected Graphical Models using Persistent Sequential Monte Carlo. ACML 2014 - [c25]Hanchen Xiong, Sándor Szedmák, Justus H. Piater:
Joint SVM for Accurate and Fast Image Tagging. ESANN 2014 - [c24]Hanchen Xiong, Sándor Szedmák, Antonio Jose Rodríguez-Sánchez, Justus H. Piater:
Towards Sparsity and Selectivity: Bayesian Learning of Restricted Boltzmann Machine for Early Visual Features. ICANN 2014: 419-426 - [c23]Emre Ugur, Sándor Szedmák, Justus H. Piater:
Bootstrapping paired-object affordance learning with learned single-affordance features. ICDL-EPIROB 2014: 476-481 - [c22]Sándor Szedmák, Emre Ugur, Justus H. Piater:
Knowledge propagation and relation learning for predicting action effects. IROS 2014: 623-629 - [c21]Emre Ugur, Sándor Szedmák, Justus H. Piater:
Complex affordance learning based on basic affordances. SIU 2014: 698-701 - 2013
- [c20]Hanchen Xiong, Sándor Szedmák, Justus H. Piater:
A Study of Point Cloud Registration with Probability Product Kernel Functions. 3DV 2013: 207-214 - [c19]Hanchen Xiong, Sándor Szedmák, Justus H. Piater:
Efficient, General Point Cloud Registration with Kernel Feature Maps. CRV 2013: 83-90 - [c18]Hanchen Xiong, Sándor Szedmák, Justus H. Piater:
3D Object Class Geometry Modeling with Spatial Latent Dirichlet Markov Random Fields. GCPR 2013: 51-60 - [c17]Hanchen Xiong, Sándor Szedmák, Justus H. Piater:
Homogeneity analysis for object-action relation reasoning in kitchen scenarios. MLIS@IJCAI 2013: 37-44 - 2012
- [j8]Mustansar Ali Ghazanfar, Adam Prügel-Bennett, Sándor Szedmák:
Kernel-Mapping Recommender system algorithms. Inf. Sci. 208: 81-104 (2012) - 2011
- [j7]Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan:
Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation. J. Mach. Learn. Res. 12: 1-30 (2011) - [c16]Mustansar Ali Ghazanfar, Sándor Szedmák, Adam Prügel-Bennett:
Incremental Kernel Mapping Algorithms for Scalable Recommender Systems. ICTAI 2011: 1077-1084 - 2010
- [j6]Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan:
The application of structured learning in natural language processing. Mach. Transl. 24(2): 71-85 (2010) - [c15]Katja Astikainen, Esa Pitkänen, Juho Rousu, Liisa Holm, Sándor Szedmák:
Reaction Kernels - Structured Output Prediction Approaches for Novel Enzyme Function. BIOINFORMATICS 2010: 48-55 - [c14]Katja Astikainen, Liisa Holm, Esa Pitkänen, Sándor Szedmák, Juho Rousu:
Structured Output Prediction of Novel Enzyme Function with Reaction Kernels. BIOSTEC (Selected Papers) 2010: 367-379 - [c13]Sándor Szedmák, Yizhao Ni, Steve R. Gunn:
Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables. WAPA 2010: 96-102
2000 – 2009
- 2009
- [c12]Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan:
Handling phrase reorderings for machine translation. ACL/IJCNLP (2) 2009: 241-244 - [c11]Sándor Szedmák, Esther Galbrun, Craig Saunders, Yizhao Ni:
Large scale maximum margin regression based, structural learning approach to phrase translations. SMART@EAMT 2009 - [c10]Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák:
Large-margin structural prediction via linear programming. SMART@EAMT 2009 - [c9]Kitsuchart Pasupa, Craig Saunders, Sándor Szedmák, Arto Klami, Samuel Kaski, Steve R. Gunn:
Learning to rank images from eye movements. ICCV Workshops 2009: 2009-2016 - 2007
- [j5]Sándor Szedmák, John Shawe-Taylor:
Synthesis of maximum margin and multiview learning using unlabeled data. Neurocomputing 70(7-9): 1254-1264 (2007) - [c8]Sándor Szedmák, Tijl De Bie, David R. Hardoon:
A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning. ESANN 2007: 211-216 - [c7]Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák:
Kernel Regression Based Machine Translation. HLT-NAACL (Short Papers) 2007: 185-188 - 2006
- [j4]Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor:
Kernel-Based Learning of Hierarchical Multilabel Classification Models. J. Mach. Learn. Res. 7: 1601-1626 (2006) - [c6]David R. Hardoon, Craig Saunders, Sándor Szedmák, John Shawe-Taylor:
A Correlation Approach for Automatic Image Annotation. ADMA 2006: 681-692 - [c5]Sándor Szedmák, John Shawe-Taylor:
Synthesis of maximum margin and multiview learning using unlabeled data. ESANN 2006: 479-484 - 2005
- [c4]Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor:
Learning hierarchical multi-category text classification models. ICML 2005: 744-751 - [c3]Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-Taylor, Amos J. Storkey, Sándor Szedmák, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang:
The 2005 PASCAL Visual Object Classes Challenge. MLCW 2005: 117-176 - [c2]Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák:
Two view learning: SVM-2K, Theory and Practice. NIPS 2005: 355-362 - 2004
- [j3]Peter L. Hammer, Alexander Kogan, Bruno Simeone, Sándor Szedmák:
Pareto-optimal patterns in logical analysis of data. Discret. Appl. Math. 144(1-2): 79-102 (2004) - [j2]Peter L. Hammer, Yanpei Liu, Bruno Simeone, Sándor Szedmák:
Saturated systems of homogeneous boxes and the logical analysis of numerical data. Discret. Appl. Math. 144(1-2): 103-109 (2004) - [j1]David R. Hardoon, Sándor Szedmák, John Shawe-Taylor:
Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Comput. 16(12): 2639-2664 (2004) - [c1]Hongying Meng, John Shawe-Taylor, Sándor Szedmák, Jason D. R. Farquhar:
Support Vector Machine to Synthesise Kernels. Deterministic and Statistical Methods in Machine Learning 2004: 242-255
Coauthor Index
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