M. Szummer

发表

Partially labeled classification with Markov random walks

Tommi S. Jaakkola, Martin Szummer, T. Jaakkola, 2001, NIPS.

Learning query-dependent prefilters for scalable image retrieval

A. Fitzgibbon, M. Szummer, L. Torresani, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Clustering and efficient use of unlabeled examples

T. Jaakkola, M. Szummer, 2001 .

Martin Szummer, Rosalind W. Picard, M. Szummer, 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.

Efficient Object Category Recognition Using Classemes

Andrew W. Fitzgibbon, Lorenzo Torresani, Martin Szummer, 2010, ECCV.

Semi-supervised learning of compact document representations with deep networks

Marc'Aurelio Ranzato, Martin Szummer, Marc'Aurelio Ranzato, 2008, ICML '08.

Learning CRFs Using Graph Cuts

Derek Hoiem, Pushmeet Kohli, Martin Szummer, 2008, ECCV.

Optimizing Binary MRFs via Extended Roof Duality

Vladimir Kolmogorov, Carsten Rother, Martin Szummer, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

Modeling user subjectivity in image libraries

Tom Minka, Martin Szummer, Rosalind W. Picard, 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

Classemes: A Compact Image Descriptor for Efficient Novel-Class Recognition and Search

Andrew W. Fitzgibbon, Lorenzo Torresani, Martin Szummer, 2014, Registration and Recognition in Images and Videos.

Contextual recognition of hand-drawn diagrams with conditional random fields

Yuan Qi, Martin Szummer, M. Szummer, 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

Snitch: interactive decision trees for troubleshooting misconfigurations

Martin Szummer, Dushyanth Narayanan, James Mickens, 2007 .

Information Regularization with Partially Labeled Data

Tommi S. Jaakkola, Martin Szummer, T. Jaakkola, 2002, NIPS.

Diagram structure recognition by Bayesian conditional random fields

Yuan Qi, Tom Minka, Martin Szummer, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

Bayesian Conditional Random Fields

Yuan Qi, Tom Minka, Martin Szummer, 2005, AISTATS.

Generative models and Bayesian model comparison for shape recognition

Balaji Krishnapuram, Christopher M. Bishop, Martin Szummer, 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

Large Scale Image Retrieval

Jiri Matas, Ondrej Chum, Martin Szummer, 2011 .

Cost-Sensitive Machine Learning for Information Retrieval

Filip Radlinski, Martin Szummer, Filip Radlinski, 2011 .

Temporal texture modeling

Martin Szummer, Rosalind W. Picard, M. Szummer, 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

Random walks on the click graph

Nick Craswell, Martin Szummer, Nick Craswell, 2007, SIGIR.

Incorporating Context and User Feedback in Pen-Based Interfaces

Martin Szummer, Philip J. Cowans, M. Szummer, 2004, AAAI Technical Report.

Balance Regularized Neural Network Models for Causal Effect Estimation pdf

Mehrdad Farajtabar, Martin Szummer, Andrew Lee, 2020, ArXiv.

Inferring query intent from reformulations and clicks

Filip Radlinski, Nick Craswell, Martin Szummer, 2010, WWW '10.

Semi-supervised learning to rank with preference regularization

Emine Yilmaz, Martin Szummer, M. Szummer, 2011, CIKM '11.

Information Retrieval System

Nick Craswell, Martin Szummer, John Guiver, 2004 .

Incremental on-line adaptation of POMDP-based dialogue managers to extended domains

Dongho Kim, Matthew Henderson, Milica Gasic, 2014, INTERSPEECH.

Behavioral classification on the click graph

Nick Craswell, Martin Szummer, Nick Craswell, 2008, WWW.

Discriminative Writer Adaptation

Christopher M. Bishop, Martin Szummer, Charles M. Bishop, 2006 .

Kernel Expansions with Unlabeled Examples

Tommi S. Jaakkola, Martin Szummer, T. Jaakkola, 2000, NIPS.

Dialogue context sensitive HMM-based speech synthesis

Dongho Kim, Matthew Henderson, Milica Gasic, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Amortized learning of neural causal representations pdf

Martin Szummer, Danilo Jimenez Rezende, Nan Rosemary Ke, 2020, ArXiv.

Relevance feedback exploiting query-specific document manifolds

Emine Yilmaz, Martin Szummer, Chang Wang, 2011, CIKM '11.

LambdaMerge: merging the results of query reformulations

Milad Shokouhi, Nick Craswell, Martin Szummer, 2011, WSDM '11.

A Decision Theoretic Framework for Ranking using Implicit Feedback

Martin Szummer, Nick Craswell, John Guiver, 2008 .