For pre-UCLA publications, please click here.

2016

  • Chunyu Wang, J. Flynn, Yizhou Wang, and A.L. Yuille.  Recognizing Actions in 3D using Action-Snippets and Activated Simplices. AAAI-16. 2016.
  • Fangting Xia, Jun Zhu, Peng Wang, and Alan Yuille. Non Pose-Guided Human Parsing with Deep Learned Features. AAAI-16. 2016.

2015

  • G. Papandreou, L-C Chen, K. Murphy and A. L. Yuille. Non  Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation. International Conference on Computer
    Vision (ICCV). 2015. pdf
  • P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price, and A.L. Yuille,  Joint Object and Part Segmentation using Deeo Learned Potentials.  International Conference on Computer Vision (ICCV). 2015. pdf
  • Zhou Ren, C. Wamg, and A.L. Yuille.  Scene-Domain Active Part Models for Object Representation. International Conference on Computer Vision (ICCV). 2015. pdf
  • A. Wong and A.L. Yuille. One Shot Learning via Compositions of Meaningful Patches.
    International Conference on Computer Vision (ICCV). 2015. pdf
  • J. Mao, W. Xu, Y. Yang, J. Wang, Z. Huang and A. L. Yuille.  Fast Novel Visual Concept Learning from Sentence Descriptions of Images. International Conference on Computer Vision
    (ICCV). 2015. pdf
  • J. Ma, Z. Zhao, and A.L. Yuille.  Non-Rigid Point Set Registration by Preserving Global and Local Structures. IEEE Transactions on Image Processing. In press. 2015. pdf
  • R. Mottaghi, S. Fidler, A.L. Yuille, R. Urtasun, D. Parikh. Human-Machine CRFs for Identifying Bottlenecks in Scene Understanding. TPAMI. In press. 2015. pdf
  • L-H Chen, A. Schwing, A. Yuille, and R. Urtasun.  Learning Deep Structured Models. International Conference on Machine Learning. 2015. pdf
  • B. Bonev and A.L. Yuille.  Bottom-Up Processing in Complex Scenes: a unifying perspective on segmentation, fixation saliency, object and region proposals, background-detail decomposition, image enhancement..  In "Recent Progress in Brain and Cognitive Engineering". Ed. S-W Lee. Springer. 2015. pdf
  • A.L. Yuille and D.K. Kersten. Early Vision. To appear in From Neuron to Cognition via Computational Neuroscience. Ed. M. Arbib. MIT Press. 2015. pdf
  • H.J. Lu, R. Rojas, T. Beckers, A.L. Yuille. A Bayesian Theory of Sequential Causal Learning and Abstract Transfer. Cognitive Science. In press. 2015. pdf
  • Jiayi Ma, Weichao Qiu, Ji Zhao, Yong Ma, Alan L. Yuille, and Zhuowen Tu. Robust L2E Estimation of Transformation for Non-Rigid registration.  IEEE Transactions on Signal Processing. Accepted for Publication. 2015. pdf
  • X. Dong, B. Bonev, Zhu Yu, and Alan. L. Yuille. Temporally consistent region-based video exposure correction. Proceedings of ICME. International Conference on Multimedia and Expo. 2015. pdf
  • Zhu Yu, Y. Zhang, B. Bonev, and A.L. Yuille. Modeling Deformable Gradient Compositions for Single Image Super-Resolution. CVPR. 2015. pdf
  • P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price, A.L.  Yuille. Towards Unified Depth and Semantic Prediction from a Single Image. . CVPR. 2015. pdf
  • J. Wang and A.L. Yuille. Semantic Part Segmentation using Compositional Model combing Shape and Appearance. CVPR. 2015. pdf
  • X. Chen and A.L. Yuille. Parsing Occluded People by Flexible Compositions. CVPR. 2015. pdf
  • X. Dong, B. Bonev, Y. Zhu, and A.L. Yuille. Region-based Temporally Consistent Video Post-processing. CVPR. 2015. [pdf]
  • L-C Chen, G. Papandreou, I. Kokkions, K. Murphy, and A.L. Yuille. Semantic Image Segmentation with Deep Convolutional Neural Networks. International Conference on Learning Representations. 2015. pdf
  • J. Mao, W. Xu, Y. Yang, J. Wang, and A.L. Yuille. Deep Captioning with Multimodal Recurrent Neural Networks (M-RNN).  International Conference on Learning Representations. 2015. [pdf]
  • P. Wang and A.L. Yuille.  Errror Factor Analysis for Wild Scene Image-Labelling. Winter Conference on Applications of Computer Vision (WACV). 2015. [pdf]


2014

  • J. Mao, W. Xu, Y. Yang, J.Wang and A.L. Yuille. Explain Images with Multimodel Recurrent
    Neural Networks
    . Deep Learning and Representation Learning Workshop: NIPS 2014. [pdf]
  •  J. Zhu, J. Mao and A.L. Yuille. Learning from Weakly Supervised Data by the Expectation Loss SVM (e-SVM) algorithm. NIPS 2014. [pdf]
  • X. Chen and A.L. Yuille. Articulated Pose Estimation with Image-Dependent Preference on Pairwise Relations. NIPS 2014. [pdf]
  • Wenhao Liu, Xioachen Lian, A.L. Yuille. Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency.  British Machine Vision Conference (BMVC). 2014. [pdf]
  • Jian Dong, Qiang Chen, Shuicheng Yan, and A.L. Yuille  Towards Unified Object Detection and Segmentation.  European Conference on Computer Vision (ECCV). 2014. [pdf]
  • B. Bonev and A.L. Yuille. A Fast and Simple Algorithm for Producing Candidate Regions.  European Conference on Computer Vision (ECCV). 2014. [pdf]
  • Junhua Mao, Jun Zhu, and A.L. Yuille. An Active Patch Model for Real World Texture and Appearance Classification.  European Conference on Computer Vision (ECCV). 2014. [pdf]
  • G. Papandreou and A.L. Yuille. Perturb-and-MAP Random Fields: Reducing Random Sampling to Optimization, with Application in Computer Vision. MIT press volume on "Advanced Structured
    Prediction". Ed.  S. Nowozin, P. V. Gehler, J. Jancsary, and C. H. Lampert. To appear. 2014.
  • D. Kersten and A.L. Yuille. Inferential Models of the Visual Cortical Hierarchy.
    Brown The New Cognitive Neurosciences, 5th Edition. Gazzaniga (Ed.) 2014.
  • A. Anderson, P.K. Douglas, W.T. Kerr, V..S. Haynes, A.L. Yuille, J. Xie, Y.N. Wu, J.A. Brown, and M.S. Cohen. Non-negative Matrix Factorization of Multimodal MRO, fMRI, and Phenotypic Data reveals Differenital Changes in Default Mode Subnetworks in ADHD. Neuroimage. 2014 [pdf]
  • G. Guo, Y. Wang, T. Jiang, A.L. Yuille, F. Fang, and W. Gao. A Shape Reconstructability Measure of Object Part Importance with Applications to Object Detection and Localization.  Int'J. of Computer Vision (IJCV). 2014 [pdf]
  • J. Ma, J. Zhai, J. Tian, A.L. Yuille, and Z. Tu. Robust Point Matching via Vector Field Consensus. Transactions in Image Processing. 2014 [pdf]
  • A.L. Yuille and J. Luo.  Guest Editorial: Geometry, Lighting, Motion, and Learning.
    Int'J. of Computer Vision (IJCV). 2014 [pdf]
  • G. Papandreou, L-C Chen, and A.L. Yuille. Modeling Image Patches with a Generic Dictionary of Mini-Epitomes. CVPR. 2014. [pdf]
  • Y. Li, X. Hou, C. Koch, J.M. Rehg, and A.L. Yuille. The Secrets of Salient Object Segmentation. CVPR. 2014. pdf
  • R. Mottaghi, X. Chen, X. Liu, N-G Cho, S-W Lee, S. Fidler, R. Urtasun, and A.L. Yuille. The Role of Context for Object Detection and Semantic Segmentation in the Wild. CVPR. 2014.   pdf
  • X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A.L. Yuille. Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts. CVPR. 2014. pdf
  • Y. Zhu, Y. Zhang, and A.L. Yuille. Single Image Super-resolution using Deformable Patches. CVPR. 2104. pdf
  • L-C Chen, S. Fidler, A.L. Yuille, and R. Urtasun. Beat the M'Turkers: Automatic Image Labeling from Weak 3D Supervision. CVPR. 2014. pdf
  • C. Wang, Y. Wang, Z. Lin,  A.L. Yuille, and W. Gao .Robust Estimation of 3D Human Poses from Single Images . CVPR. 2014. pdf
  • W. Qiu, X. Wang, X. Bai, A.L. Yuille, and Z. Tu. Scale-Space SIFT Flow. Winter Conference on Applications of Computer Vision (WACV). 2014. pdf

2013

  • D. Kersten and A.L. Yuille. Bayesian Inference and Beyond. The New Visual Neurosciences. John S. Werner and Leo M. Chalupa (Editors) MIT Press. Cambridge MA. 2013. [pdf]
  • L-C Chen, G. Papandreou, and A.L. Yuille. Learning a Dictionary of Shape Epitomes with Applications to Image Labeling. International Conference on Computer Vision (ICCV). 2013. [pdf]
  • A. L. Yuille and R. Mottaghi. Complexity of Representation and Inference in Compositional Models with Part Sharing. International Conference on Learning Representations (ICML). 2013. [pdf]
  • C. Wang, Y. Wang, and A.L. Yuille. An Approach to Pose Based Action Recognition. CVPR. 2013 [pdf]
  • J. Ma, Z. Zhao, J. Tian, Z. Tu, and A.L. Yuille. Robust Nonrigid Point Set Registration Using the L2-Minimizung Estimate. CVPR 2013, [pdf]
  • X. Hou, A.L. Yuille, and C. Koch. Boundary detection benchmarking: beyond F-measures. CVPR 2013. [pdf]
  • X. Liu, L. Liu, A.L. Yuille. MsLRR: Segment Images via Internal Replication Prior. CVPR 2013. [pdf]
  • S. Fidler, R. Mottaghi, A.L. Yuille, and R. Urtasun. Bottom-Up Segmentation for Top-Down Detection. CVPR 2013. [pdf]

2012

  • A.L. Yuille and H.H. Buelthoff. Where's the Action? Action as an innate bias for visual learning. Commentary. proceedings of the National Academy of Sciences. (PNAS). October. 2012. [pdf]
  • A.L. Yuille. Computer Vision needs a Core and Foundation. Opinion Paper. Image and Vision Computing. Accepted . June. 2012. [pdf]
  • N.-G. Cho,  A.L. Yuille, and S. -W. Lee. Adaptive Self-Occlusion Reasoning for 3D Human Pose Tracking from Accepted. Monocular Image Sequences.  Pattern Recognition. June. 2012. [pdf]
  • A.L. Yuille and X. He. Probabilistic Models of Vision and Max-Margin Methods. Frontiers of Electrical and Electornic Engineering. Vol. 7, Number 1. March. 2012. [pdf]
  • Y. Nishihara, X. Ye, and A.L. Yuille. A family of CCCP Algorithms with minimize the TRW Free Energy. New Generation Computing. 30: 3-16. January. 2012. [pdf]
  • L. Zhu, Y. Chen, Y. Lin, C. Lin, and A.L. Yuille. Recursive Segmentation and Recognition Templates for Image Parsing. IEEE Trans. Pattern Anal. Mach. Intell. 34(2): 359-371. January. 2012. [pdf]

2011

  • G. Papandreou and A.L. Yuille. Peturb and Map: Using Discrete Optimization to Learn and Sample from Energy Models. Proceedings of International Conference on Computer Vision. November. 2011. [pdf]
  • A. Yuille. Towards a Theory of Compositional Learning and Encoding of Objects. 1st IEEE Workshop in Information Theory in Computer Vision and Pattern Recognition. ICCV. November. 2011. [pdf]
  • G. Papandreou and A.L. Yuille. Efficient Variational Inference in Large-scale Bayesian Compressed Sensing.1st IEEE Workshop in Information Theory in Computer Vision and Pattern Recognition. ICCV. November. 2011. [pdf]
  • C. Guo, Y. Wang, Y. Jiang, A.L. Yuille, and W. Gao. Computing Importance of 2D Contour parts by Reconstructability. 1st IEEE Workshop in Information theory in Computer Vision and Pattern Recognition. ICCV. Novermber. 2011. [pdf]
  • R. Mottaghi and A.L. Yuille. A compositional approach to learning part-based models for single and multi-view object detection. #dRR-11 workshop. ICCV. November. 2011. [pdf]
  • X. Ye and A.L. Yuille. Learning a Dictionary of Deformable patches using GPUs. Workshop on GPU's in Computer Vision Applications. ICCV. November.  2011. [pdf]
  • N-M Cho, A.L. Yuille, and S-W Lee. Nonflat Observation Model and Adpative Depth Order Estimation for 3D Human Pose Tracking. First Asian Conference on pattern Recognition. Beijing. November. 2011. [pdf]
  • L. Zhu, Y. Chen, and A.L. Yuille. Recursive Compositional Models for Vision: Description and Review of Previous work. Journal of Mathematical Imaging and Vision. 41(1-2): 122-146. Spetember 2011. [pdf]
  • P-H. Lee, J.J. Lee, S-W Lee, A.L. Yuille and C. Koch. Adaboost for tect Detection in Natural Scences. Proceeding of International Conference on Document Analysis and Recognition. PP 429-434. Sepetember. 2011. {pdf]
  • A. Yuille. Belief Propagation, Mean Field, and Bethe Approximations. In Advances in Markov Random Fields for  Vision and Image Processing. Ed.s A. Blake, P. Kohli, and C. Rother. MIT Press September. 2011. [pdf]
  • A, Anderson, J. Bramen, P. Douglas, A. Lenartowicz, A. Cho, C. Culbertson, A.L. Brody, A.L. Yuille, and M.S. Cohen. Large Sample Group Independent Component Analysis of Functional Magemtic Resonance Imaging using Anatomical atlas-based reduction and bootstrapped clustering. International Journal of Imaging Systems and Technology. Special Issue on Brain Mapping and Neuroimaging. 21(2). June 2011. [pdf]
  • I. Kokkinos and A.L. Yuille. Inference and Learning with Hierarchical Compositional Models. International Journal of Computer Vision. Vol. 93(2):201-225. June. 2011. [pdf]
  • L. Zhu, Y. Chen, and A.L. Yuille. Max-Margin AND/OR graph learning for parsing the human body. International Journal of Computer Vision. 93: 1-21. May. 2011. [pdf]

2010

  • P.K. Douglas, S. Harris, A.L. Yuille, and M.S. Cohen. Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief versus disbelief. Neuroimage. Nov. 2010. [pdf]
  • S. Zheng, A.L. Yuille, and Z. Tu. Detecting Object Boundaries Using Low-, Mid-, and High-Level Information, Journal of Computer Vision and Image Understanding. Vol 114. No. 10, pp 1055-1067. Oct. 2010. [pdf]
  • A.L. Yuille. An Information Theoretic Perspective on Computer Vision. Recent Advances on Information Theoretical Methods. Frontiers of Electrical and Electronic Engineering. Eds. Lei Xu. 6(1). August 2010. [pdf]
  • H. Lu, T. Lin, A. Lee, L. Vese, A.L. Yuille. Functional form of Motion Priors in Human Motion Perception. To appear. NIPS. December. 2010. [pdf]
  • S. Wu, X. He, H. Lu, A.L. Yuille. A Unified model of short-range and long-range motion perception. To appear in NIPS. December. 2010. [pdf]
  • G. Papandreou, A.L. Yuille. Gaussian Sampling by Local Perturbation. To appear in NIPS. December. 2010. [pdf]
  • X. He, A.L. Yuille. Occlusion Boundary Detection using Pseudo-Depth. In ECCV. September. 2010. [pdf]
  • Y. Chen, L. Zhu, A.L. Yuille. Active Mask Hierarchies for Object Detection.  In ECCV. September. 2010. [pdf]
  • L. Zhu, Y. Chen, A. Torrable, W. Freeman, A.L. Yuille. Part and Appearance Sharing: Recursive Compositional Models for Multi-View Multi-Object Detection. In CVPR. June. 2010. [pdf]
  • L. Zhu, Y. Chen, A.L. Yuille, W. Freeman. Latent Hierarchical Structure Learning for Object Detection. In CVPR. June 2010. [pdf]

2009

  • H. Lu, M. Weiden, A.L. Yuille. Modeling the spacing effect in sequential category learning. In NIPS. Dec. 2009. [pdf]
  • Y. Chen, L. Zhu, A. L. Yuille, H. Zhang. Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation and Recognition using Knowledge Propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI. October 2009. [pdf]
  • A. Anderson, I. D Dinov, J. E Sherin, J. Quintana, A.L. Yuille, and M. Cohen. Classification of Spatially Unaligned fMRI Scans. NeuroImage. August 2009. [pdf]
  • D. Cremers, B. Rosenhahn, and A.L. Yuille (Eds). Statistical and Geometrical Approaches to Visual Motion Analysis. Spinger-Verlag Lecture Notes in Computer Science 5604. August 2009. [website]
  • S. Wu, H.J. Lu, A. Lee and A.L. Yuille. Motion Integration Using Competitive Priors. Statistical and Geometrical Approaches to Visual Motion Analysis. Spinger-Verlag Lecture Notes in Computer Science 5604. August 2009. [pdf]
  • I. Kokkinos and A.L. Yuille. HOP: Hierarchical Object Parsing. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2009. [pdf]
  • A.L. Yuille and S.F. Zheng. Compositional noisy-logical learning. Proceedings of the 26th Annual International Conference on Machine Learning. ICML. June 2009. [pdf]
  • L. Zhu, Y. Chen, A.L. Yuille. Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI. March 2009. [pdf]
  • L. Zhu, Y. Chen, A.L. Yuille. Unsupervised Learning of Probabilistic Grammar-Markov Models for Object Categories. IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI. January 2009. [pdf]

2008

  • S. Wu, H.J. Lu, A.L. Yuille. Model selection and parameter estimation in motion perception. Advances in Neural Information Processing Systems 21. NIPS. December 2008. [pdf]
  • L. Zhu, Y. Chen, Y. Lin, C. Lin, A.L. Yuille. Recursive Segmentation and Recognition Templates for 2D Parsing. Advances in Neural Information Processing Systems 21. NIPS. December 2008. [pdf]
  • L. Zhu, C. Lin, H. Huang, Y. Chen, A.L. Yuille. Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion. Proceedings of the European Conference on Computer Vision. ECCV. October 2008. [pdf]
  • H.J. Lu, A.L. Yuille, M. Liljeholm, P.W. Cheng, and K.J. Holyoak. Bayesian generic priors for causal learning. Psychological Review, vol. 115, no. 4, pp. 955-984. October 2008. [pdf]
  • H.J. Lu, R. Rojas, T. Beckers, and A.L. Yuille. Sequential causal learning in humans and rats. Proceedings of the 30th Annual Conference of the Cognitive Science Society. July 2008. [pdf]
  • I. Kokkinos and A. Yuille. Scale Invariance without Scale Selection. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2008. [pdf]
  • Y. Chen, L. Zhu, A.L. Yuille, H. Zhang. Unsupervised Learning of Probabilistic Object Models for Object Classification, Segmentation and Recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2008. [pdf]
  • L. Zhu, Y. Chen, X. Ye, A.L. Yuille. Structure-Perceptron Learning of a Hierarchical Log-Linear Model. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2008. [pdf]
  • L. Zhu, Y. Chen, Y. Lu, C. Lin, A.L. Yuille. Max Margin AND/OR Graph Learning for Parsing the Human Body. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2008. [pdf]
  • J. J. Corso, A.L. Yuille, and Z. Tu. Graph-Shifts: Natural Image Labeling by Dynamic Hierarchical Computing. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2008. [pdf]
  • J. J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha, and A.L. Yuille. Efficient Multilevel Brain Tumor Segmentation with Integrated Bayesian Model Classification. IEEE Transactions on Medical Imaging, vol. 27, no. 5, pp. 629-640. May 2008. [pdf]
  • J. J. Corso, Z. Tu, and A.L. Yuille. MRF Labeling with a Graph-Shifts Algorithm. Proceedings of International Workshop on Combinatorial Image Analysis, pp. 172-184. April 2008. [pdf]
  • T.L. Griffiths and A.L. Yuille. A primer on probabilistic inference. In M.Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press. Pages 33-58. March 2008. [pdf]
  • S. Dube, J. J. Corso, A.L. Yuille, T. F. Cloughesy, S. El-Saden, and U. Sinha. Hierarchical Segmentation of Malignant Gliomas Via Integrated Contextual Filter Response. Image Processing. Edited by Reinhardt, Joseph M.; Pluim, Josien P. W. Proceedings of the SPIE, vol. 6914. February 2008. [pdf]
  • Z. Tu, S.F. Zheng, and A.L. Yuille. Shape Matching and Registration by Data-driven EM. Journal of Computer Vision and Image Understanding. CVIU. vol. 109, pp. 290-304. February 2008. [pdf]

2007

  • A.L. Yuille and H.J. Lu. The noisy-logical distribution and its application to causal inference. Advances in Neural Information Processing Systems 20. NIPS. December 2007. [pdf]
  • L. Zhu, Y. Chen, C. Lin, A.L. Yuille. Rapid Inference on a novel AND/OR graph: Detection, Segmentation and Parsing of Articulated Deformable Objects in Cluttered Backgrounds. Advances in Neural Information Processing Systems 20. NIPS. December 2007. [pdf]
  • J. Corso, A.L. Yuille, N. Sicotte, and A. Toga. Detection and Segmentation of Pathological Structures by the Extended Graph-Shifts Algorithm. Proceedings of Medical Image Computing and Computer Aided Intervention. MICCAI. October 2007. [pdf]
  • I. Kokkinos and A. Yuille. Unsupervised Learning of Object Deformation Models. Proceedings of IEEE International Conference on Computer Vision. ICCV. October 2007. [pdf]
  • A. L. Yuille, S. C. Zhu, D. Cremers, Y. Wang (Eds). Proceedings of the 6th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2007. Ezhou, China, August 27-29, 2007. Springer 2007. [website]
  • H.J. Lu, A.L Yuille, M. Liljeholm, P.W. Cheng, and K.J. Holyoak. Bayesian models of judgments of causal strength: A comparison. Proceedings of the 29th Annual Conference of the Cognitive Science Society. pp. 1241-1246. August 2007. [pdf]
  • J. J. Corso, Z. Tu, A. Yuille, and A. W. Toga. Segmentation of Sub-Cortical Structures by the Graph-Shifts Algorithm. Proceedings of Information Processing in Medical Imaging. pp. 183-197. July 2007. [pdf]
  • S.F. Zheng, Z. Tu, A. L. Yuille. Detecting Object Boundaries Using Low-, Mid-, and High-level Information. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2007. [pdf]
  • Z. Tu, S.F. Zheng, A.L. Yuille, A.L. Reiss, R.A. Dutton, A.D. Lee, A.M. Galaburda, I. Dinov, P.M. Thompson and A.W. Toga. Automated Extraction of the Cortical Sulci Based. on a Supervised Learning Approach. IEEE Transactions on Medical Imaging. Vol. 26. No. 4. pp. 541-552. April 2007. [pdf]
  • T.S. Lee and A.L. Yuille. Efficient Coding of Visual Scenes by Grouping and Segmentation: Theoretical Principles and Biological Evidence In the Bayesian Brain: Probabilistic Approaches to Neural Coding. Ed. K. Doya, S. Ishii, A. Pouget, and R.P.N. Rao. MIT Press. pp 145-188. January 2007. [pdf]

2006

  • L. Zhu, Y. Chen, and A.L. Yuille. Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing. Advances in Neural Information Processing Systems 19. NIPS. December 2006. [pdf]
  • Z. Tu, X. Chen, A.L. Yuille and S.C. Zhu. Image Parsing: Segmentation, Detection, and Recognition. In Towards Category-Level Object Recognition. Eds. J. Ponce, M. Hebert, C. Schmid, A. Zisserman. Springer LNCS 4170. pp 545-576. October 2006. [pdf]
  • J. J. Corso, E. Sharon, and A. L. Yuille. Multilevel Segmentation and Integrated Bayesian Model Classification with an Application to Brain Tumor Segmentation. Proceedings of Medical Image Computing and Computer Aided Intervention. MICCAI. vol. 2, pp. 790-798. October 2006. [pdf]
  • S.F. Zheng, Z. Tu, A. L. Yuille, A. L. Reiss, R. A. Dutton, A. D. Lee, A. M. Galaburda, P. M. Thompson, I. D. Dinov, A. W. Toga. A Learning Based Algorithm for Automatic Extraction of the Cortical Sulci. Proceedings of Medical Image Computing and Computer Aided Intervention. MICCAI. vol. 1, pp. 695-703. October 2006. [pdf]
  • H.J. Lu, A.L Yuille, M. Liljeholm, P.W. Cheng, and K.J. Holyoak. Modeling causal learning using Bayesian generic priors on generative and preventive powers. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp. 519-524. July 2006. [pdf]
  • T.L. Griffiths and A.L. Yuille. A primer on probabilistic inference. In Trends in Cognitive Sciences. Supplement to special issue on Probabilistic Models of Cognition, vol 10, no. 7. July 2006. [pdf]
  • A.L. Yuille and D. Kersten. Vision as Bayesian Inference: Analysis by Synthesis? In Trends in Cognitive Neuroscience, vol. 10, no. 7, pp. 301-308. July 2006. [pdf]
  • N. Chater, J. Tenenbaum, A.L. Yuille. Probabilistic models of cognition: Where next? In Trends in Cognitive Neuroscience, vol. 10, no. 7, pp. 292-293. July 2006. [pdf]
  • N. Chater, J. Tenenbaum and A.L. Yuille. Probabilistic Models of Cognition: Conceptual Foundations. In Trends in Cognitive Neuroscience, vol. 10, no. 7, pp. 287-291. July 2006. [pdf]
  • B. Rokers, A.L. Yuille and Z. Liu. The perceived motion of a stereokinetic stimulus. Vision Research, vol. 46, no. 15, pp. 2375-87. July 2006. [pdf]
  • I. Kokkinos, P. Maragos, A. L. Yuille. Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2006. [pdf]

2005

  • H.J. Lu and A.L. Yuille. Ideal Observers for Detecting Human Motion: Correspondence Noise. Advances in Neural Information Processing Systems 18. NIPS. December 2005. [pdf]
  • A.L. Yuille. Augmented Rescorla-Wagner and Maximum Likelihood Estimation. Advances in Neural Information Processing Systems 18. NIPS. December 2005. [pdf]
  • L. Zhu, A.L. Yuille. A Hierarchical Compositional System for Rapid Object Detection. Advances in Neural Information Processing Systems 18. NIPS. December 2005. [pdf]
  • A. Rangarajan, B. C. Vemuri, A. L. Yuille (Eds). Proceedings of the 5th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2005. St. Augustine, FL, USA, November 9-11, 2005, Proceedings Springer 2005. 
  • M. Rosen-Zvi, M. I. Jordan, A. L. Yuille. The DLR Hierarchy of Approximate Inference. UAI. pp. 493-500. July 2005. [pdf]
  • Z. Tu, X. Chen, A.L. Yuille, S.C. Zhu. Image Parsing: Unifying Segmentation, Detection, and Recognition. International Journal of Computer Vision. IJCV. vol. 63, no. 2, pp. 113-140. July 2005. [pdf]
  • X. Chen and A.L. Yuille. A Time-Efficient Cascade for Real Time Object Detection. 1st International Workshop on Computer Vision Applications for the Visually Impaired. In association with CVPR 2005. June 2005. [pdf]

2004

  • A.L. Yuille. The Rescorla-Wagner Algorithm and Maximum Likelihood Estimation of Causal Parameters. Advances in Neural Information Processing Systems 17. NIPS. December 2004. [pdf]
  • A.L. Yuille. The Convergence of Contrastive Divergences. Advances in Neural Information Processing Systems 17. NIPS. December 2004. [pdf]
  • D. Kersten, P. Mamassian and A.L. Yuille. Object Perception as Bayesian Inference. Annual Review of Psychology, vol. 555, pp 271-304. 2004. [pdf]
  • X. Chen and A.L. Yuille. AdaBoost Learning for Detecting and Reading Text in City Scenes. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2004. [pdf]
  • A. Barbu and A.L. Yuille. Motion Estimation by Swendsen-Wang Cuts. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. CVPR. June 2004. [pdf]
  • Z. Tu and A.L. Yuille. Shape Matching and Recognition: Using Generative Models and Informative Features. Proceedings of the European Conference on Computer Vision. ECCV. vol. 3, pp 195-209, May 2004. [pdf]

2003

  • J.M. Coughlan and A.L. Yuille. A Large Deviation Theory Analysis of Bayesian Tree Search. In Mathematical Methods in Computer Vision, Eds. P. Olver and A. Tannenbaum, IMA Volumes in Mathematics and its Applications, vol. 133, pp 1-17, Spinger, 2003. [pdf]
  • A.L. Yuille, F. Fang, P. Schrater and D. Kersten. Human and Ideal Observers for Detecting Image Curves. Advances in Neural Information Processing Systems 16. NIPS. December 2003. [pdf]
  • A. Rangarajan, J.M. Coughlan and A.L. Yuille. A Bayesian Network for Relational Shape Matching. Proceedings of International Conference on Computer Vision. ICCV. October 2003. [pdf]
  • Z. Tu, X. Chen, A.L. Yuille and S.C. Zhu. Image Parsing: Segmentation, Detection, and Recognition. Proceedings of International Conference on Computer Vision. ICCV. October 2003. [pdf]
  • D. Cremers and A.L. Yuille. A Generative Model Based Approach to Motion Segmentation. In B. Michaelis and G. Krell (Eds.). German Conference on Pattern Recognition (DAGM), Springer LNCS vol. 2781, pp 313-320, September 2003. [pdf]
  • A.L. Yuille, J.M. Coughlan, and S. Konishi. The Generic Viewpoint Assumption and Planar Bias. IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI. Vol. 25, No. 8, August 2003. [pdf]
  • J.M. Coughlan and A.L. Yuille. Manhattan World. Neural Computation, Vol. 15, No. 5, pp 1063-1088. May 2003. [pdf]
  • D. Kersten and A.L. Yuille. Bayesian Models of Object Perception. Current Opinion in Neurobiology, Vol. 13, pp 1-9. April 2003. [pdf]
  • A.L. Yuille and A. Rangarajan. The Concave-Convex Procedure (CCCP). Neural Computation, Vol. 15, No. 4, pp 915-936. April 2003. [pdf]
  • S.M. Konishi, A.L. Yuille, and J.M. Coughlan. A Statistical Approach to Multi-Scale Edge Detection. Image and Vision Computing (IVC), Special issue on Generative-Model Based Vision, Vol. 21, No. 1, pp 37-48, January 2003. [pdf]
  • J.M. Coughlan and A.L. Yuille. Algorithms from Statistical Physics for Generative Models of Images. Image and Vision Computing (IVC), Special issue on Generative-Model Based Vision, Vol. 21, No. 1, pp 29-36, January 2003. [pdf]
  • S. M. Konishi, A.L. Yuille, J.M. Coughlan and S.C. Zhu. Statistical Edge Detection: Learning and Evaluating Edge Cues. IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI. Vol. 25, No. 1, pp 57-74. January 2003. [pdf]
  • A.L. Yuille, J. M. Coughlan, and S. Konishi. The KGBR Viewpoint-Lighting Ambiguity. Journal of the American Optical Society. Vol.20, No. 1, pp 24-31. January 2003. [pdf]