Alexander Schwing

Assistant Professor
Department of Electrical and Computer Engineering
Coordinated Science Laboratory
University of Illinois at Urbana-Champaign

Office: CSL 103
eMail:

Publications

  • U. Jain*, Z. Zhang* and A.G. Schwing; Creativity: Generating Diverse Questions using Variational Autoencoders; IEEE Conf. on Computer Vision and Pattern Recognition (CVPR); 2017
(*equal contribution)
  • T. Käser, S. Klingler, A.G. Schwing and M. Gross; Dynamic Bayesian Networks for Student Modeling; Trans. on Learning Technologies; 2017
  • F.S. He, Y. Liu, A.G. Schwing and J. Peng; Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening; Int.'l Conf. on Learning Representations (ICLR); 2017
  • B. London* and A.G. Schwing* Generative Adversarial Structured Networks; Neural Information Processing Systems (NIPS); 2016; Workshop on Adversarial Training
(*equal contribution)
  • Y. Tenzer, A.G. Schwing, K. Gimpel and T. Hazan; Constraints Based Convex Belief Propagation; Neural Information Processing Systems (NIPS); 2016
  • R. Liao, A.G. Schwing, R. Zemel and R. Urtasun; Learning Deep Parsimonious Representations; Neural Information Processing Systems (NIPS); 2016
  • B. Franke, J.-F. Plante, R. Roscher, E.A. Lee, C. Smyth, A. Hatefi, F. Chen, E. Gil, A.G. Schwing, A. Selvitella, M.M. Hoffman, R. Grosse, D. Hendricks and N. Reid; Statistical Inference, Learning and Models in Big Data; Int.'l Statistical Review; 2016
  • Y. Song, A.G. Schwing, R. Zemel and R. Urtasun; Training Deep Neural Networks via Direct Loss Minimization; Int.'l Conf. on Machine Learning (ICML); 2016
  • W. Luo, A.G. Schwing and R. Urtasun; Efficient Deep Learning for Stereo Matching; IEEE Conf. on Computer Vision and Pattern Recognition (CVPR); 2016
  • A.G. Schwing, T. Hazan, M. Pollefeys and R. Urtasun; Distributed Algorithms for Large Scale Learning and Inference in Graphical Models; Trans. on Pattern Analysis and Machine Intelligence (PAMI); accepted for publication
  • O. Meshi, M. Mahdavi and A.G. Schwing; Smooth and Strong: MAP Inference with Linear Convergence; Neural Information Processing Systems (NIPS); 2015
  • Z. Zhang*, A.G. Schwing*, S. Fidler and R. Urtasun; Monocular Object Instance Segmentation and Depth Ordering with CNNs; Int.'l Conf. on Computer Vision (ICCV); 2015
(*equal contribution)
  • L.-C. Chen*, A.G. Schwing*, A.L. Yuille and R. Urtasun; Learning Deep Structured Models; Int.'l Conf. on Machine Learning (ICML); 2015
(*equal contribution)
  • C. Liu*, A.G. Schwing*, K. Kundu, R. Urtasun and S. Fidler; Rent3D: Floor-Plan Priors for Monocular Layout Estimation; IEEE Conf. on Computer Vision and Pattern Recognition (CVPR); 2015
(*equal contribution)
  • J. Xu, A.G. Schwing and R. Urtasun; Learning to Segment under Various Forms of Weak Supervision; IEEE Conf. on Computer Vision and Pattern Recognition (CVPR); 2015
  • S. Wang, A.G. Schwing and R. Urtasun; Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials; Neural Information Processing Systems (NIPS); 2014
  • J. Zhang, A.G. Schwing and R. Urtasun; Message Passing Inference for Large Scale Graphical Models with High Order Potentials; Neural Information Processing Systems (NIPS); 2014
  • F. Srajer, A.G. Schwing, M. Pollefeys and T. Pajdla; MatchBox: Indoor Image Matching via Box-like Scene Estimation; Int.'l Conf. on 3D Vision (3DV); 2014
  • A.G. Schwing, T. Hazan, M. Pollefeys and R. Urtasun; Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm; Int.'l Conf. on Machine Learning (ICML); 2014
  • A. Cohen, A.G. Schwing and M. Pollefeys; Efficient Structured Parsing of Facades Using Dynamic Programming; IEEE Conf. on Computer Vision and Pattern Recognition (CVPR); 2014
  • J. Xu, A.G. Schwing and R. Urtasun; Tell Me What You See and I will Show You Where It Is; IEEE Conf. on Computer Vision and Pattern Recognition (CVPR); 2014
  • T. Käser, S. Klingler, A.G. Schwing and M. Gross; Beyond Knowledge Tracing: Modeling Skill Topologies with Bayesian Networks; Int.'l Conf. on Intelligent Tutoring Systems (ITS); 2014
(Best paper award)
  • T. Käser, A.G. Schwing, T. Hazan and M. Gross; Computational Education using Latent Structured Prediction; Int.'l Conf. on Artificial Intelligence and Statistics (AISTATS); 2014
  • A.G. Schwing and Y. Zheng; Reliable Extraction of the Mid-Sagittal Plane in 3D Brain MRI via Hierarchical Landmark Detection; IEEE Int.'l Symposium on Biomedical Imaging (ISBI); 2014
  • W. Luo, A.G. Schwing and R. Urtasun; Latent Structured Active Learning; Neural Information Processing Systems (NIPS); 2013
  • J. Zhang, C. Kan, A.G. Schwing and R. Urtasun; Estimating the 3D Layout of Indoor Scenes and its Clutter from Depth Sensors; Int.'l Conf. on Computer Vision (ICCV); 2013
  • A.G. Schwing, S. Fidler, M. Pollefeys and R. Urtasun; Box In the Box: Joint 3D Layout and Object Reasoning from Single Images; Int.'l Conf. on Computer Vision (ICCV); 2013
  • A.G. Schwing, T. Hazan, M. Pollefeys and R. Urtasun; Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins; Neural Information Processing Systems (NIPS); 2012
  • A.G. Schwing and R. Urtasun; Efficient Exact Inference for 3D Indoor Scene Understanding; European Conference on Computer Vision (ECCV); 2012
  • A.G. Schwing, T. Hazan, M. Pollefeys and R. Urtasun; Distributed Structured Prediction for Big Data; NIPS Workshop on Big Learning; 2012
  • A.G. Schwing, T. Hazan, M. Pollefeys and R. Urtasun; Efficient Structured Prediction with Latent Variables for General Graphical Models; Int.'l Conf. on Machine Learning (ICML); 2012
  • A.G. Schwing, T. Hazan, M. Pollefeys and R. Urtasun; Large Scale Structured Prediction with Hidden Variables; Snowbird Workshop; 2011
  • A.G. Schwing, T. Hazan, M. Pollefeys and R. Urtasun; Efficient Structured Prediction for 3D Indoor Scene Understanding; IEEE Conf. on Computer Vision and Pattern Recognition (CVPR); 2012
  • A.G. Schwing, T. Hazan, M. Pollefeys and R. Urtasun; Distributed Message Passing for Large Scale Graphical Models; IEEE Conf. on Computer Vision and Pattern Recognition (CVPR); 2011
  • A.G. Schwing, C. Zach, Y. Zheng and M. Pollefeys; Adaptive Random Forest - How many ``experts'' to ask before making a decision?; IEEE Conf. on Computer Vision and Pattern Recognition (CVPR); 2011
  • M. Koch, A.G. Schwing, D. Comaniciu and M. Pollefeys; Fully Automatic Segmentation of Wrist Bones for Arthritis Patients; IEEE Int.'l Symposium on Biomedical Imaging (ISBI); 2011
  • M. Sarkis, K. Diepold and A.G. Schwing; Enhancing the Motion Estimate in Bundle Adjustment Using Projective Newton-type Optimization on the Manifold; IS&T/SPIE Electronic Imaging - Image Processing: Machine Vision Applications II; 1/2009
  • R. Hunger, D. Schmidt, M. Joham, A.G. Schwing, and W. Utschick; Design of Single-Group Multicasting-Beamformers; IEEE Int.'l Conf. on Communications (ICC); 2007

Patent Applications

  • A. Tsymbal, M. Kelm, M.J. Costa, S.K. Zhou, D. Comaniciu, Y. Zheng and A.G. Schwing; Image Processing Using Random Forest Classifiers; USPA 20120321174; Assignee: Siemens Corp.
  • A.G. Schwing, Y. Zheng, M. Harder, and D. Comaniciu; Method and System for Anatomic Landmark Detection Using Constrained Marginal Space Learning and Geometric Inference; USPA 20100119137; Assignee: Siemens Corp.