Sándor Szedmák

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 19

Top Topics

Sequential Monte Carlo

Undirected Graphical Models

Reinforcement Learning

Top Venues

Publications

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) (2024)
Robert Ebo Armah-Sekum, Sándor Szedmák, Juho Rousu
Protein function prediction through multi-view multi-label latent tensor reconstruction. BMC Bioinform. 25 (1) (2024)
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)
Sándor Szedmák, Riikka Huusari, Tat Hong Duong Le, Juho Rousu
Scalable variable selection for two-view learning tasks with projection operators. CoRR (2023)
Maryam Sabzevari, Sándor Szedmák, Merja Penttilä, Paula Jouhten, Juho Rousu
Strain design optimization using reinforcement learning. PLoS Comput. Biol. 18 (6) (2022)
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) (2021)
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 (2020)
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 (2020)
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) (2018)
Eric Bach, Sándor Szedmák, Céline Brouard, Sebastian Böcker, Juho Rousu
Liquid-chromatography retention order prediction for metabolite identification. Bioinform. 34 (17) (2018)
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 (2018)
Kitsuchart Pasupa, Sándor Szedmák
Utilising Kronecker Decomposition and Tensor-based Multi-view Learning to predict where people are looking in images. Neurocomputing 248 (2017)
Senka Krivic, Michael Cashmore, Bram Ridder, Daniele Magazzeni, Sándor Szedmák, Justus H. Piater
Initial State Prediction in Planning. AAAI Workshops (2017)
Senka Krivic, Michael Cashmore, Daniele Magazzeni, Bram Ridder, Sándor Szedmák, Justus H. Piater
Decreasing Uncertainty in Planning with State Prediction. IJCAI (2017)
Hanchen Xiong, Sándor Szedmák, Justus H. Piater
Learning undirected graphical models using persistent sequential Monte Carlo. Mach. Learn. 103 (2) (2016)
Sabrina Fontanella, Antonio Jose Rodríguez-Sánchez, Justus H. Piater, Sándor Szedmák
Kronecker Decomposition for Image Classification. CLEF (2016)
Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater
Hierarchical Haptic Manipulation for Complex Skill Learning. CoRR (2016)
Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater
Robotic playing for hierarchical complex skill learning. IROS (2016)
Huibin Shen, Sándor Szedmák, Céline Brouard, Juho Rousu
Soft Kernel Target Alignment for Two-Stage Multiple Kernel Learning. DS (2016)
Thomas Hoyoux, Antonio Jose Rodríguez-Sánchez, Justus H. Piater, Sándor Szedmák
Can Computer Vision Problems Benefit from Structured Hierarchical Classification? CAIP (2) (2015)
Wail Mustafa, Hanchen Xiong, Dirk Kraft, Sándor Szedmák, Justus H. Piater, Norbert Krüger
Multi-label Object Categorization Using Histograms of Global Relations. 3DV (2015)
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)
Antonio Jose Rodríguez-Sánchez, Sándor Szedmák, Justus H. Piater
SCurV: A 3D descriptor for object classification. IROS (2015)
Senka Krivic, Sándor Szedmák, Hanchen Xiong, Justus H. Piater
Learning missing edges via kernels in partially-known graphs. ESANN (2015)
Antonio Jose Rodríguez-Sánchez, Sabrina Fontanella, Justus H. Piater, Sándor Szedmák
IIS at ImageCLEF 2015: Multi-label Classification Task. CLEF (Working Notes) (2015)
Hanchen Xiong, Antonio Jose Rodríguez-Sánchez, Sándor Szedmák, Justus H. Piater
Diversity priors for learning early visual features. Frontiers Comput. Neurosci. 9 (2015)
Hanchen Xiong, Sándor Szedmák, Justus H. Piater
Scalable, accurate image annotation with joint SVMs and output kernels. Neurocomputing 169 (2015)
Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater, Ales Ude
Reactive, task-specific object manipulation by metric reinforcement learning. ICAR (2015)
Kitsuchart Pasupa, Sándor Szedmák
Learning to Predict Where People Look with Tensor-Based Multi-view Learning. ICONIP (1) (2015)
Emre Ugur, Sándor Szedmák, Justus H. Piater
Bootstrapping paired-object affordance learning with learned single-affordance features. ICDL-EPIROB (2014)
Hanchen Xiong, Sándor Szedmák, Justus H. Piater
Joint SVM for Accurate and Fast Image Tagging. ESANN (2014)
Hanchen Xiong, Sándor Szedmák, Justus H. Piater
Towards Maximum Likelihood: Learning Undirected Graphical Models using Persistent Sequential Monte Carlo. ACML (2014)
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)
Sándor Szedmák, Emre Ugur, Justus H. Piater
Knowledge propagation and relation learning for predicting action effects. IROS (2014)
Emre Ugur, Sándor Szedmák, Justus H. Piater
Complex affordance learning based on basic affordances. SIU (2014)
Hanchen Xiong, Sándor Szedmák, Justus H. Piater
3D Object Class Geometry Modeling with Spatial Latent Dirichlet Markov Random Fields. GCPR (2013)
Hanchen Xiong, Sándor Szedmák, Justus H. Piater
A Study of Point Cloud Registration with Probability Product Kernel Functions. 3DV (2013)
Hanchen Xiong, Sándor Szedmák, Justus H. Piater
Efficient, General Point Cloud Registration with Kernel Feature Maps. CRV (2013)
Hanchen Xiong, Sándor Szedmák, Justus H. Piater
Homogeneity analysis for object-action relation reasoning in kitchen scenarios. MLIS@IJCAI (2013)
Mustansar Ali Ghazanfar, Adam Prügel-Bennett, Sándor Szedmák
Kernel-Mapping Recommender system algorithms. Inf. Sci. 208 (2012)
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 (2011)
Mustansar Ali Ghazanfar, Sándor Szedmák, Adam Prügel-Bennett
Incremental Kernel Mapping Algorithms for Scalable Recommender Systems. ICTAI (2011)
Katja Astikainen, Esa Pitkänen, Juho Rousu, Liisa Holm, Sándor Szedmák
Reaction Kernels - Structured Output Prediction Approaches for Novel Enzyme Function. BIOINFORMATICS (2010)
Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan
The application of structured learning in natural language processing. Mach. Transl. 24 (2) (2010)
Sándor Szedmák, Yizhao Ni, Steve R. Gunn
Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables. WAPA (2010)
Katja Astikainen, Liisa Holm, Esa Pitkänen, Sándor Szedmák, Juho Rousu
Structured Output Prediction of Novel Enzyme Function with Reaction Kernels. BIOSTEC (Selected Papers) (2010)
Kitsuchart Pasupa, Craig Saunders, Sándor Szedmák, Arto Klami, Samuel Kaski, Steve R. Gunn
Learning to rank images from eye movements. ICCV Workshops (2009)
Sándor Szedmák, Esther Galbrun, Craig Saunders, Yizhao Ni
Large scale maximum margin regression based, structural learning approach to phrase translations. SMART@EAMT (2009)
Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák
Large-margin structural prediction via linear programming. SMART@EAMT (2009)
Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan
Handling phrase reorderings for machine translation. ACL/IJCNLP (2) (2009)
Sándor Szedmák, John Shawe-Taylor
Synthesis of maximum margin and multiview learning using unlabeled data. Neurocomputing 70 (7-9) (2007)
Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák
Kernel Regression Based Machine Translation. HLT-NAACL (Short Papers) (2007)
Sándor Szedmák, Tijl De Bie, David R. Hardoon
A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning. ESANN (2007)
David R. Hardoon, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
A Correlation Approach for Automatic Image Annotation. ADMA (2006)
Sándor Szedmák, John Shawe-Taylor
Synthesis of maximum margin and multiview learning using unlabeled data. ESANN (2006)
Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
Kernel-Based Learning of Hierarchical Multilabel Classification Models. J. Mach. Learn. Res. 7 (2006)
Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
Learning hierarchical multi-category text classification models. ICML (2005)
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, Thomas L. 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)
Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák
Two view learning: SVM-2K, Theory and Practice. NIPS (2005)
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) (2004)
Peter L. Hammer, Alexander Kogan, Bruno Simeone, Sándor Szedmák
Pareto-optimal patterns in logical analysis of data. Discret. Appl. Math. 144 (1-2) (2004)
David R. Hardoon, Sándor Szedmák, John Shawe-Taylor
Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Comput. 16 (12) (2004)
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)