Sándor Szedmák

According to our database, Sándor Szedmák authored at least 60 papers between 2004 and 2023.

Collaborative distances:

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

2023

Scalable variable selection for two-view learning tasks with projection operators.

Sándor Szedmák, Riikka Huusari, Tat Hong Duong Le, Juho Rousu

CoRR, 2023

Personalized Learning Systems for Computer Science Students: Analyzing and Predicting Learning Behaviors Using Programming Error Data.

Mubina Kamberovic, Senka Krivic, Amra Delic, Sándor Szedmák, Vedran Ljubovic

Proceedings of the Adjunct Proceedings of the 31st ACM Conference on User Modeling, 2023

2022

Strain design optimization using reinforcement learning.

Maryam Sabzevari, Sándor Szedmák, Merja Penttilä, Paula Jouhten, Juho Rousu

PLoS Comput. Biol., 2022

2021

Modeling drug combination effects via latent tensor reconstruction.

Tianduanyi Wang, Sándor Szedmák, Haishan Wang, Tero Aittokallio, Tapio Pahikkala, Anna Cichonska, Juho Rousu

Bioinform., 2021

2020

Using Machine Learning for Decreasing State Uncertainty in Planning.

Senka Krivic, Michael Cashmore, Daniele Magazzeni, Sándor Szedmák, Justus H. Piater

J. Artif. Intell. Res., 2020

A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction.

Sándor Szedmák, Anna Cichonska, Heli Julkunen, Tapio Pahikkala, Juho Rousu

CoRR, 2020

2018

Integrating multi-purpose natural language understanding, robot's memory, and symbolic planning for task execution in humanoid robots.

Mirko Wächter, Ekaterina Ovchinnikova, Valerij Wittenbeck, Peter Kaiser, Sándor Szedmák, Wail Mustafa, Dirk Kraft, Norbert Krüger, Justus H. Piater, Tamim Asfour

Robotics Auton. Syst., 2018

Learning with multiple pairwise kernels for drug bioactivity prediction.

Anna Cichonska, Tapio Pahikkala, Sándor Szedmák, Heli Julkunen, Antti Airola, Markus Heinonen, Tero Aittokallio, Juho Rousu

Bioinform., 2018

Liquid-chromatography retention order prediction for metabolite identification.

Eric Bach, Sándor Szedmák, Céline Brouard, Sebastian Böcker, Juho Rousu

Bioinform., 2018

2017

Utilising Kronecker Decomposition and Tensor-based Multi-view Learning to predict where people are looking in images.

Kitsuchart Pasupa, Sándor Szedmák

Neurocomputing, 2017

Decreasing Uncertainty in Planning with State Prediction.

Senka Krivic, Michael Cashmore, Daniele Magazzeni, Bram Ridder, Sándor Szedmák, Justus H. Piater

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Initial State Prediction in Planning.

Senka Krivic, Michael Cashmore, Bram Ridder, Daniele Magazzeni, Sándor Szedmák, Justus H. Piater

Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Learning undirected graphical models using persistent sequential Monte Carlo.

Hanchen Xiong, Sándor Szedmák, Justus H. Piater

Mach. Learn., 2016

Hierarchical Haptic Manipulation for Complex Skill Learning.

Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater

CoRR, 2016

Robotic playing for hierarchical complex skill learning.

Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater

Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016

Soft Kernel Target Alignment for Two-Stage Multiple Kernel Learning.

Huibin Shen, Sándor Szedmák, Céline Brouard, Juho Rousu

Proceedings of the Discovery Science - 19th International Conference, 2016

Kronecker Decomposition for Image Classification.

Sabrina Fontanella, Antonio Jose Rodríguez-Sánchez, Justus H. Piater, Sándor Szedmák

Proceedings of the Experimental IR Meets Multilinguality, Multimodality, and Interaction, 2016

2015

Scalable, accurate image annotation with joint SVMs and output kernels.

Hanchen Xiong, Sándor Szedmák, Justus H. Piater

Neurocomputing, 2015

Diversity priors for learning early visual features.

Hanchen Xiong, Antonio Jose Rodríguez-Sánchez, Sándor Szedmák, Justus H. Piater

Frontiers Comput. Neurosci., 2015

SCurV: A 3D descriptor for object classification.

Antonio Jose Rodríguez-Sánchez, Sándor Szedmák, Justus H. Piater

Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015

Using structural bootstrapping for object substitution in robotic executions of human-like manipulation tasks.

Alejandro Agostini, Mohamad Javad Aein, Sándor Szedmák, Eren Erdal Aksoy, Justus H. Piater, Florentin Wörgötter

Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015

Learning to Predict Where People Look with Tensor-Based Multi-view Learning.

Kitsuchart Pasupa, Sándor Szedmák

Proceedings of the Neural Information Processing - 22nd International Conference, 2015

Reactive, task-specific object manipulation by metric reinforcement learning.

Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater, Ales Ude

Proceedings of the International Conference on Advanced Robotics, 2015

Learning missing edges via kernels in partially-known graphs.

Senka Krivic, Sándor Szedmák, Hanchen Xiong, Justus H. Piater

Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

IIS at ImageCLEF 2015: Multi-label Classification Task.

Antonio Jose Rodríguez-Sánchez, Sabrina Fontanella, Justus H. Piater, Sándor Szedmák

Proceedings of the Working Notes of CLEF 2015, 2015

Can Computer Vision Problems Benefit from Structured Hierarchical Classification?

Thomas Hoyoux, Antonio Jose Rodríguez-Sánchez, Justus H. Piater, Sándor Szedmák

Proceedings of the Computer Analysis of Images and Patterns, 2015

Multi-label Object Categorization Using Histograms of Global Relations.

Wail Mustafa, Hanchen Xiong, Dirk Kraft, Sándor Szedmák, Justus H. Piater, Norbert Krüger

Proceedings of the 2015 International Conference on 3D Vision, 2015

2014

Complex affordance learning based on basic affordances.

Emre Ugur, Sándor Szedmák, Justus H. Piater

Proceedings of the 2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014

Knowledge propagation and relation learning for predicting action effects.

Sándor Szedmák, Emre Ugur, Justus H. Piater

Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

Bootstrapping paired-object affordance learning with learned single-affordance features.

Emre Ugur, Sándor Szedmák, Justus H. Piater

Proceedings of the 4th International Conference on Development and Learning and on Epigenetic Robotics, 2014

Towards Sparsity and Selectivity: Bayesian Learning of Restricted Boltzmann Machine for Early Visual Features.

Hanchen Xiong, Sándor Szedmák, Antonio Jose Rodríguez-Sánchez, Justus H. Piater

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

Joint SVM for Accurate and Fast Image Tagging.

Hanchen Xiong, Sándor Szedmák, Justus H. Piater

Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

Towards Maximum Likelihood: Learning Undirected Graphical Models using Persistent Sequential Monte Carlo.

Hanchen Xiong, Sándor Szedmák, Justus H. Piater

Proceedings of the Sixth Asian Conference on Machine Learning, 2014

2013

Homogeneity analysis for object-action relation reasoning in kitchen scenarios.

Hanchen Xiong, Sándor Szedmák, Justus H. Piater

Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems, 2013

3D Object Class Geometry Modeling with Spatial Latent Dirichlet Markov Random Fields.

Hanchen Xiong, Sándor Szedmák, Justus H. Piater

Proceedings of the Pattern Recognition - 35th German Conference, 2013

Efficient, General Point Cloud Registration with Kernel Feature Maps.

Hanchen Xiong, Sándor Szedmák, Justus H. Piater

Proceedings of the Tenth Conference on Computer and Robot Vision, 2013

A Study of Point Cloud Registration with Probability Product Kernel Functions.

Hanchen Xiong, Sándor Szedmák, Justus H. Piater

Proceedings of the 2013 International Conference on 3D Vision, 2013

2012

Kernel-Mapping Recommender system algorithms.

Mustansar Ali Ghazanfar, Adam Prügel-Bennett, Sándor Szedmák

Inf. Sci., 2012

2011

Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation.

Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan

J. Mach. Learn. Res., 2011

Incremental Kernel Mapping Algorithms for Scalable Recommender Systems.

Mustansar Ali Ghazanfar, Sándor Szedmák, Adam Prügel-Bennett

Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011

2010

The application of structured learning in natural language processing.

Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan

Mach. Transl., 2010

Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables.

Sándor Szedmák, Yizhao Ni, Steve R. Gunn

Proceedings of the First Workshop on Applications of Pattern Analysis, 2010

Reaction Kernels - Structured Output Prediction Approaches for Novel Enzyme Function.

Katja Astikainen, Esa Pitkänen, Juho Rousu, Liisa Holm, Sándor Szedmák

Proceedings of the BIOINFORMATICS 2010, 2010

Structured Output Prediction of Novel Enzyme Function with Reaction Kernels.

Katja Astikainen, Liisa Holm, Esa Pitkänen, Sándor Szedmák, Juho Rousu

Proceedings of the Biomedical Engineering Systems and Technologies, 2010

2009

Learning to rank images from eye movements.

Kitsuchart Pasupa, Craig Saunders, Sándor Szedmák, Arto Klami, Samuel Kaski, Steve R. Gunn

Proceedings of the 12th IEEE International Conference on Computer Vision Workshops, 2009

Large-margin structural prediction via linear programming.

Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák

Proceedings of the Workshop on Statistical Multilingual Analysis for Retrieval and Translation, 2009

Large scale maximum margin regression based, structural learning approach to phrase translations.

Sándor Szedmák, Esther Galbrun, Craig Saunders, Yizhao Ni

Proceedings of the Workshop on Statistical Multilingual Analysis for Retrieval and Translation, 2009

Handling phrase reorderings for machine translation.

Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan

Proceedings of the ACL 2009, 2009

2007

Synthesis of maximum margin and multiview learning using unlabeled data.

Sándor Szedmák, John Shawe-Taylor

Neurocomputing, 2007

Kernel Regression Based Machine Translation.

Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák

Proceedings of the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, 2007

A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning.

Sándor Szedmák, Tijl De Bie, David R. Hardoon

Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

2006

Kernel-Based Learning of Hierarchical Multilabel Classification Models.

Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor

J. Mach. Learn. Res., 2006

A Correlation Approach for Automatic Image Annotation.

David R. Hardoon, Craig Saunders, Sándor Szedmák, John Shawe-Taylor

Proceedings of the Advanced Data Mining and Applications, Second International Conference, 2006

2005

Two view learning: SVM-2K, Theory and Practice.

Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

The 2005 PASCAL Visual Object Classes Challenge.

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

Proceedings of the Machine Learning Challenges, 2005

Learning hierarchical multi-category text classification models.

Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor

Proceedings of the Machine Learning, 2005

2004

Canonical Correlation Analysis: An Overview with Application to Learning Methods.

David R. Hardoon, Sándor Szedmák, John Shawe-Taylor

Neural Comput., 2004

Saturated systems of homogeneous boxes and the logical analysis of numerical data.

Peter L. Hammer, Yanpei Liu, Bruno Simeone, Sándor Szedmák

Discret. Appl. Math., 2004

Pareto-optimal patterns in logical analysis of data.

Peter L. Hammer, Alexander Kogan, Bruno Simeone, Sándor Szedmák

Discret. Appl. Math., 2004

Support Vector Machine to Synthesise Kernels.

Hongying Meng, John Shawe-Taylor, Sándor Szedmák, Jason D. R. Farquhar

Proceedings of the Deterministic and Statistical Methods in Machine Learning, 2004