Sándor Szedmák

发表

Efficient, General Point Cloud Registration with Kernel Feature Maps

Justus H. Piater, Hanchen Xiong, Sándor Szedmák, 2013, 2013 International Conference on Computer and Robot Vision.

The application of structured learning in natural language processing

Mahesan Niranjan, Sándor Szedmák, Craig Saunders, 2010, Machine Translation.

A Study of Point Cloud Registration with Probability Product Kernel Functions

Justus H. Piater, Hanchen Xiong, Sándor Szedmák, 2013, 2013 International Conference on 3D Vision.

Kernel Regression Based Machine Translation

John Shawe-Taylor, Zhuoran Wang, Sándor Szedmák, 2007, NAACL.

Handling phrase reorderings for machine translation

Mahesan Niranjan, Sándor Szedmák, Craig Saunders, 2009, ACL/IJCNLP.

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

John Shawe-Taylor, David R. Hardoon, Sándor Szedmák, 2005, NIPS.

Initial State Prediction in Planning

Justus H. Piater, Senka Krivic, Daniele Magazzeni, 2017, AAAI Workshops.

Using Machine Learning for Decreasing State Uncertainty in Planning

Justus H. Piater, Senka Krivic, Daniele Magazzeni, 2020, J. Artif. Intell. Res..

Joint SVM for Accurate and Fast Image Tagging

Justus H. Piater, Hanchen Xiong, Sándor Szedmák, 2014, ESANN.

Complex affordance learning based on basic affordances

Justus H. Piater, Emre Ugur, Sándor Szedmák, 2014, 2014 22nd Signal Processing and Communications Applications Conference (SIU).

Hierarchical Haptic Manipulation for Complex Skill Learning pdf

Justus H. Piater, Emre Ugur, Sándor Szedmák, 2016, ArXiv.

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

Justus H. Piater, Dirk Kraft, Norbert Krüger, 2018, Robotics Auton. Syst..

Homogeneity analysis for object-action relation reasoning in kitchen scenarios

Justus H. Piater, Hanchen Xiong, Sándor Szedmák, 2013, MLIS '13.

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

Justus H. Piater, Hanchen Xiong, Sándor Szedmák, 2014, ACML.

A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning

Tijl De Bie, David R. Hardoon, Sándor Szedmák, 2007, ESANN.

Diversity priors for learning early visual features

Justus H. Piater, Hanchen Xiong, Antonio Jose Rodríguez-Sánchez, 2015, Front. Comput. Neurosci..

IIS at ImageCLEF 2015: Multi-label Classification Task

Justus H. Piater, Antonio Jose Rodríguez-Sánchez, Sándor Szedmák, 2015, CLEF.

Learning missing edges via kernels in partially-known graphs

Justus H. Piater, Hanchen Xiong, Senka Krivic, 2015, ESANN.

Kronecker Decomposition for Image Classification

Justus H. Piater, Antonio Jose Rodríguez-Sánchez, Sándor Szedmák, 2016, CLEF.

Learning undirected graphical models using persistent sequential Monte Carlo

Justus H. Piater, Hanchen Xiong, Sándor Szedmák, 2016, Machine Learning.

Liquid‐chromatography retention order prediction for metabolite identification

Juho Rousu, Eric Bach, Sándor Szedmák, 2018, Bioinform..

The 2005 PASCAL Visual Object Classes Challenge

Cordelia Schmid, Jorma Laaksonen, Luc Van Gool, 2005, MLCW.

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

Peter L. Hammer, Yanpei Liu, Sándor Szedmák, 2004, Discret. Appl. Math..

Scalable, accurate image annotation with joint SVMs and output kernels

Justus H. Piater, Hanchen Xiong, Sándor Szedmák, 2015, Neurocomputing.

Reactive, task-specific object manipulation by metric reinforcement learning

Ales Ude, Justus H. Piater, Emre Ugur, 2015, 2015 International Conference on Advanced Robotics (ICAR).

Can Computer Vision Problems Benefit from Structured Hierarchical Classification?

Justus H. Piater, Antonio Jose Rodríguez-Sánchez, Thomas Hoyoux, 2015, CAIP.

Learning to rank images from eye movements

Kitsuchart Pasupa, Samuel Kaski, Steve R. Gunn, 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

Reaction Kernels - Structured Output Prediction Approaches for Novel Enzyme Function

Juho Rousu, Liisa Holm, Sándor Szedmák, 2018, BIOINFORMATICS.

Structured Output Prediction of Novel Enzyme Function with Reaction Kernels

Juho Rousu, Liisa Holm, Sándor Szedmák, 2010, BIOSTEC.

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

Mahesan Niranjan, Sándor Szedmák, Craig Saunders, 2011, J. Mach. Learn. Res..

Knowledge propagation and relation learning for predicting action effects

Justus H. Piater, Emre Ugur, Sándor Szedmák, 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

Eren Erdal Aksoy, Florentin Wörgötter, Alejandro Agostini, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Decreasing Uncertainty in Planning with State Prediction

Justus H. Piater, Senka Krivic, Daniele Magazzeni, 2017, IJCAI.

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

Kitsuchart Pasupa, Sándor Szedmák, 2015, ICONIP.

Canonical Correlation Analysis: An Overview with Application to Learning Methods

John Shawe-Taylor, David R. Hardoon, Sándor Szedmák, 2004, Neural Computation.

Kernel-Mapping Recommender system algorithms

Adam Prügel-Bennett, Mustansar Ali Ghazanfar, Sándor Szedmák, 2012, Inf. Sci..

SCurV: A 3D descriptor for object classification

Justus H. Piater, Antonio Jose Rodríguez-Sánchez, Sándor Szedmák, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Learning with multiple pairwise kernels for drug bioactivity prediction

Juho Rousu, Anna Cichonska, Tapio Pahikkala, 2018, Bioinform..

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

Justus H. Piater, Hanchen Xiong, Sándor Szedmák, 2013, GCPR.

Synthesis of maximum margin and multiview learning using unlabeled data

John Shawe-Taylor, Sándor Szedmák, J. Shawe-Taylor, 2007, ESANN.

Incremental Kernel Mapping Algorithms for Scalable Recommender Systems

Adam Prügel-Bennett, Mustansar Ali Ghazanfar, Sándor Szedmák, 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

Multi-label Object Categorization Using Histograms of Global Relations

Justus H. Piater, Hanchen Xiong, Dirk Kraft, 2015, 2015 International Conference on 3D Vision.

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

Justus H. Piater, Hanchen Xiong, Antonio Jose Rodríguez-Sánchez, 2014, ICANN.

Learning hierarchical multi-category text classification models

Juho Rousu, John Shawe-Taylor, Sándor Szedmák, 2005, ICML.

Kernel-Based Learning of Hierarchical Multilabel Classification Models

Juho Rousu, John Shawe-Taylor, Sándor Szedmák, 2006, J. Mach. Learn. Res..

Large-margin structural prediction via linear programming

John Shawe-Taylor, Sándor Szedmák, Zuoran Wang, 2009, SMART@EAMT.

Soft Kernel Target Alignment for Two-Stage Multiple Kernel Learning

Juho Rousu, Sándor Szedmák, Huibin Shen, 2016, DS.

A Correlation Approach for Automatic Image Annotation

John Shawe-Taylor, David R. Hardoon, Sándor Szedmák, 2006, ADMA.

Pareto-optimal patterns in logical analysis of data

Peter L. Hammer, Alexander Kogan, Sándor Szedmák, 2004, Discret. Appl. Math..

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

Kitsuchart Pasupa, Sándor Szedmák, S. Szedmák, 2017, Neurocomputing.

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

Justus H. Piater, Emre Ugur, Sándor Szedmák, 2014, 4th International Conference on Development and Learning and on Epigenetic Robotics.

Robotic playing for hierarchical complex skill learning

Justus H. Piater, Emre Ugur, Sándor Szedmák, 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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

Steve R. Gunn, Sándor Szedmák, Yizhao Ni, 2010, WAPA.

Modeling drug combination effects via latent tensor reconstruction

Anna Cichonska, Tapio Pahikkala, Tero Aittokallio, 2021, Bioinform..