| 010 | P. Baldi (UCI) and L. Wu (UCI) |
| A Scalable Machine Learning Approach to the Game of Go |
| |
| 027 | S. V. N. Vishwanathan, Nicol N. Schraudolph (National ICT Australia); Mark W. Schmidt, Kevin Murphy (U. British Columbia) |
| Accelerated Training of Conditional Random Fields with Stochastic Meta-Descent |
| |
| 032 | P. Wiesing (TU Berlin), O. Beck (TU Berlin), L. Schwabe (TU Berlin), J. Marino (U. A Coruna), J. Schummers (MIT), D. Lyon (Salk Institute), M. Sur (MIT), and Obermayer K. (TU Berlin) |
| The Operating Regime of Cortical Computation |
| |
| 035 | Neil Lawrence (U. Sheffield), Joaquin Quiñonero-Candela (Fraunhofer FIRST.IDA) |
| Local Distance Preservation in the GP-LVM through Back Constraints |
| |
| 041 | D. Huttenlocher (Cornell U.) and D. Crandall (Cornell U.) |
| Learning and Recognizing Objects Without Relying on Feature Detection |
| |
| 042 | H. Daume III (USC/ISI), John Langford (TTI-C) and Daniel Marcu (USC/ISI) |
| Search-based Structured Prediction |
| |
| 043 | D. Cohn (Google, Inc.), and D. Verma (University of Washington) |
| Recursive Attribute Factoring |
| |
| 046 | N. Le Roux (U. de Montreal) and Y. Bengio (U. de Montreal) |
| Continuous Neural Networks |
| |
| 054 | J.A. Bagnell (CMU), N.D. Ratliff (CMU), M. A. Zinkevich (Alberta) |
| Sub-gradient Method Structured Learning and Mobile Robotics |
| |
| 056 | Frank DiMaio (UW-Madison), Jude Shavlik (UW-Madison), and George Phillips (UW-Madison) |
| Tracing Protein Backbones in Electron Density Maps using a Markov Random Field Model |
| |
| 057 | Sang Min Oh (GeorgiaTech) and James M. Rehg (GeorgiaTech) and Frank Dellaert (Georgia Tech) |
| On-line Learning of the Traversability of Unstructured Terrain for Outdoor Robot Navigation |
| |
| 059 | R. Hadsell, P. Sermanet, J. Ben, J. Han, S. Chopra, M. Ranzato, Y. Sulsky, B. Flepp, U. Muller, Yann LeCun (NYU and Net-Scale Technologies) |
| On-Line Learning of Long-Range Obstacle Detection for Off-Road Robots |
| |
| 066 | C. Sminchisescu (TTI-C), Atul Kanaujia (Rutgers University), Dimitris Metaxas (Rutgers University) |
| Bidirectional Model Learning for Visual Inference |
| |
| 067 | P. Haffner (AT&T Labs-Research) |
| AdaBoost is Maximum Entropy over the Error Distribution |
| |
| 068 | Luis Perez-Breva (MIT), Luis E. Ortiz (MIT), Chen-Hsiang Yeang (UC Santa Cruz) and Tommi Jaakkola (MIT) |
| A Game-Theoretic Approach to Protein-DNA Binding |
| |
| 070 | Fei Sha (U. of Pennsylvania), and Lawrence K. Saul (U. of Pennsylvania) |
| Large margin Gaussian mixture modeling |
| |
| 083 | N. de Freitas (UBC) |
| Recent Advances in Particle Methods |
| |
| 088 | M. Ranzato (NYU), and Y. LeCun (NYU) |
| Energy-based model for unsupervised learning of sparse overcomplete representations |
| |
| 094 | L. Torresani (Riya, Inc.), P. Hackney (Integrated Movement Studies), and C. Bregler (New York University) |
| Learning to Synthesize Motion Styles |
| |
| 098 | J. Blitzer, K. Crammer, R. McDonald, F. Pereira (University of Pennsylvania) |
| Structural Correspondences for Transfer Learning |
| |
| 099 | Françoise Soulié Fogelman, KXEN |
| Data mining in the real world : what do we need & what do we have |
| |
| 100 | D. Dueck (U. Toronto) and B.J. Frey (U. Toronto) |
| Is it better to cluster using belief propagation or linear programming? |
| |
| 102 | N. Srebro (U. Toronto), and G. Shakhnarovich (Brown), and S. Roweis (U. Toronto) |
| When is clustering hard? |
| |
| 001 | Ciprian Chelba (Microsoft Research) |
| Acoustic Sensitive Language Model Perplexity for Automatic Speech Recognition |
| |
| 007 | Bruce Denby (Université Pierre et Marie Curie) |
| Talking without Speaking : An Overview of the OUISPER Project |
| |
| 008 | S. Kiritchenko, S. Matwin, R. Nock, F. Famili |
| Learning in the Presence of Class Hierarchies |
| |
| 014 | C. Pedersen (U. Queensland), and J. Diederich (U. Queensland, American Univ. of Sharjah) |
| Human and machine learning based accent recognition |
| |
| 017 | M. Gupta (U. Washington) |
| Some recent research in nonparametric near-neighbor learning |
| |
| 018 | Brendt Wohlberg and Kevin Vixie (Los Alamos National Laboratory) |
| Template Matching With Invariance Based on Local Linear Transform Approximations |
| |
| 019 | Peng Zhao, Guilherme Rocha & Bin Yu (UC Berkeley) |
| Grouped and Hierarchical Model Selection through Composite Absolute Penalties (CAP) |
| |
| 020 | C.Alippi (Politecnico di Milano), and M.Roveri (Politecnico di Milano) |
| PROCESS DRIFTS AND NON-STATIONARITY DETECTING INDEXES |
| |
| 021 | L. Coin (Imperial College), Shu-Yi Su (Imperial College), David Balding (Imperial College) |
| Using Sparse Bayesian Learning |
| |
| 023 | E. Chang (UC Santa Barbara/Google), and M. Lyu (Chinese University Hong Kong) |
| A Unified Learning Paradigm for Web-scale Information Management |
| |
| 025 | Frank Dellaert (Georgia Institute of Technology) |
| Variable Elimination and the Bayes Tree Algorithm |
| |
| 026 | A. B. Owen (Stanford U) |
| A robust hybrid of lasso and ridge regression |
| |
| 028 | Christopher R. Wren (MERL) and Yuri A. Ivanov (MERL) |
| Boosted Haar Wavelets for Sensor Networks |
| |
| 029 | J. Diederich (U. Queensland, American Univ. of Sharjah) |
| Rule-Extraction from Support Vector Machines |
| |
| 030 | M. Braun (Fraunhofer Institute FIRST), and J. Buhmann (Swiss Federal Institute of Technology Zurich) |
| On Learning in Infinite Dimensional Kernel Feature Spaces |
| |
| 031 | Aurelie GOULON (ESPCI, Paris), Arthur DUPRAT (ESPCI, Paris), Gerard DREYFUS (ESPCI, Paris) |
| GRAPH MACHINES FOR REGRESSION ON STRUCTURED DATA |
| |
| 033 | Kai Puolamäki, Jarkko Salojärvi, Eerika Savia, and Samuel Kaski (Helsinki U. of Technology) |
| Discriminative MCMC |
| |
| 034 | Ashok Veeraraghavan ( University of Maryland, College Park) and Rama Chellappa (University of Maryland, College Park) |
| Learning the function space of an activity |
| |
| 037 | N. P. Cuntoor (U. Maryland), and R. Chellappa (U. Maryland) |
| Mixed State Models for Human Activities |
| |
| 039 | Ulrike von Luxburg (Fraunhofer IPSI), Shai Ben-David (University of Waterloo) |
| Prior Beliefs in Semi-Supervised Learning |
| |
| 040 | Baback Moghaddam (MERL), Yair Weiss (Hebrew University), Shai Avidan (MERL) |
| Spectral Bounds for Feature Selection & Sparse LDA |
| |
| 044 | T. Joachims (Cornell) |
| Training Linear SVMs in Linear Time |
| |
| 048 | M. Siracusa (MIT), and J. Fisher (MIT) |
| Modeling and Estimating Dynamic Dependency Structure : Applications to Audio-Visual Speaker Labeling |
| |
| 049 | Y. Bengio, D. Popovici, P. Lamblin, R. Garg, B. Cromp, and P.-J. L'Heureux (U. Montreal) |
| Discriminant Mixture of 3D Molecular Surface Models |
| |
| 050 | H. Narayanan (Univ. of Chicago), M. Belkin (Ohio State Univ.), P. Niyogi (Univ. of Cicago) |
| Volumes of Boundaries, Heat Propagation and Spectral Clustering |
| |
| 051 | D. Minnen, T. Starner, I. Essa, and C. Isbell (Georgia Tech) |
| Activity Discovery: Sparse Motifs from Multivariate Time Series |
| |
| 052 | A. Guta (U. Penn), D. Foster (U. Penn), L. Ungar (U. Penn) |
| Unsupervised learning of distance metrics |
| |
| 053 | Minyoung Kim (Rutgers) and Vladimir Pavlovic (Rutgers) |
| Efficient Discriminative Learning of Mixture of Bayesian Networks for Sequence Classification |
| |
| 058 | M. Holmes (Georgia Tech), and C. Isbell (Georgia Tech) |
| Looping Suffix Trees for Inference of Partially Observable Hidden State |
| |
| 060 | O. Berkman (Tel-Aviv University), and N. Intrator (Tel-Aviv University) |
| Robust Inference in Bayesian Networks |
| |
| 061 | N. Srebro (U. Toronto), and S. Ben-David (U. Waterloo) |
| Learning bounds for support vector machines with learned kernels |
| |
| 063 | Alexandru Niculescu-Mizil (Cornell University), and Rich Caruana (Cornell University) |
| Inductive Transfer for Bayesian Network Structure Learning |
| |
| 065 | T. Jebara (Columbia U.), B. Shaw (Columbia U.), and V. Shchogolev (Columbia U.) |
| B-Matching for Embedding |
| |
| 071 | A. Globerson (MIT), S. Roweis (U. Toronto) |
| Visualizing pairwise similarity via semidefinite programming |
| |
| 072 | M. Kim (Rutgers), Y. Jing (Georgia Tech.), V. Pavlovic (Rutgers), and J. M. Rehg (Georgia Tech.) |
| Discriminative Learning of Generative Models for Sequence Labeling Problems |
| |
| 073 | Maria-Florina Balcan (CMU), Alina Beygelzimer (IBM Watson), John Langford (TTI-Chicago) |
| Robust Reductions from Ranking to Classification |
| |
| 074 | R. Caruana (Cornell), C. Bucila (Cornell), A. Niculescu-Mizil (Cornell) |
| Model Compression |
| |
| 075 | G. Grudic and J. Mulligan |
| The Polynomial Mahalanobis Distance |
| |
| 077 | K. Chellapilla and J. Platt (Microsoft Research) |
| Redundant Bit Vectors for Handwriting Retrieval |
| |
| 078 | D. Verma (U. Washington, Seattle) and R.P.N. Rao (U. Washington, Seattle) |
| Graphical Models for Planning and Acting in Uncertain Environments |
| |
| 080 | O. Madani (Yahoo! Research) |
| Recall Systems |
| |
| 081 | Anthony Hoogs (GE Global Research) and Roderic Collins (GE Global Research) |
| Boundary Detection using Semantic, Hierarchical Category Models |
| |
| 084 | Gang Wu, Zhihua Zhang, Edward Y. Chang |
| Speeding up Support Vector Machines |
| |
| 085 | G. Shakhnarovich (Brown University), and J. W. Fisher (MIT) |
| Performance of approximate nearest-neighbor classification |
| |
| 090 | K. Q. Weinberger (UPenn), K. Crammer (UPenn), L. K. Saul (UPenn) |
| Local Distance Metric Learning for Large Margin |
| |
| 092 | Ian Fasel and Javier R. Movellan |
| Unsupervised Learning of Object Categories and Image Segmentations |
| |
| 093 | V. Punyakanok (U. of Illinois), and D. Roth (U. of Illinois) |
| Utility of Constraints in Structured Output Problems |
| |
| 096 | K. Kavukcuoglu (NYU), Y.LeCun (NYU), K. White (Yale), PE Barbano (Yale) |
| End-to-End Learning for Automatic Cell Phenotyping |
| |
| 103 | Matt L. Miller (NEC Labs America) |
| Linear Feature Discovery from Decision Boundary Normals |
| |
| 105 | Alex Vasilescu (MIT) |
| Manifold Decomposition and Low Dimensional Parameterization |
| |
