Michael C. Mozer

Anup Doshi, Cuong Tran, Matthew H. Wilder, Michael C. Mozer, Mohan M. Trivedi: Sequential Dependencies in Driving. Cognitive Science 36(5): 948-963 (2012)

Michael C. Mozer, Benjamin Link, Harold Pashler: An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments. NIPS 2011: 1791-1799

Michael Mozer, Harold Pashler, Matthew H. Wilder, Robert A. Lindsey, Matt Jones, Michael Jones: Improving Human Judgments by Decontaminating Sequential Dependencies. NIPS 2010: 1705-1713

Dan Knights, Todd Mytkowicz, Peter F. Sweeney, Michael C. Mozer, Amer Diwan: Blind Optimization for Exploiting Hardware Features. CC 2009: 251-265

Dan Knights, Michael C. Mozer, Nicolas Nicolov: Detecting Topic Drift with Compound Topic Models. ICWSM 2009

Michael Mozer, Harold Pashler, Nicholas Cepeda, Robert A. Lindsey, Ed Vul: Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory. NIPS 2009: 1321-1329

Matthew H. Wilder, Matt Jones, Michael Mozer: Sequential effects reflect parallel learning of multiple environmental regularities. NIPS 2009: 2053-2061

Michael C. Mozer, Harold Pashler, Hadjar Homaei: Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds? Cognitive Science 32(7): 1133-1147 (2008)

Michael C. Mozer, Adrian Fan: Top-Down modulation of neural responses in visual perception: a computational exploration. Natural Computing 7(1): 45-55 (2008)

Matt Jones, Michael C. Mozer, Sachiko Kinoshita: Optimal Response Initiation: Why Recent Experience Matters. NIPS 2008: 785-792

Jeremy Reynolds, Michael C. Mozer: Temporal Dynamics of Cognitive Control. NIPS 2008: 1353-1360

Sander M. Bohte, Michael C. Mozer: Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity. Neural Computation 19(2): 371-403 (2007)

Michael Mozer, David Baldwin: Experience-Guided Search: A Theory of Attentional Control. NIPS 2007

Michael C. Mozer, Michael Jones, Michael Shettel: Context Effects in Category Learning: An Investigation of Four Probabilistic Models. NIPS 2006: 993-1000

Michael C. Mozer: Rational Models of Cognitive Control. UC 2006: 20-25

Sander M. Bohte, Michael C. Mozer: Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity. BNAIC 2005: 319-320

Michael Mozer, Michael Shettel, Shaun Vecera: Top-Down Control of Visual Attention: A Rational Account. NIPS 2005

Matthias Hauswirth, Amer Diwan, Peter F. Sweeney, Michael C. Mozer: Automating vertical profiling. OOPSLA 2005: 281-296

Michael Mozer: How Practice Makes Perfect. ICCM 2004: 13-13

Sander M. Bohte, Michael C. Mozer: Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity. NIPS 2004

Michael D. Colagrosso, Michael C. Mozer: Theories of Access Consciousness. NIPS 2004

Lian Yan, Robert H. Dodier, Michael Mozer, Richard H. Wolniewicz: Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic. ICML 2003: 848-855

Sepp Hochreiter, Michael Mozer, Klaus Obermayer: Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems. NIPS 2002: 545-552

Richard S. Zemel, Michael Mozer: Localist Attractor Networks. Neural Computation 13(5): 1045-1064 (2001)

Sepp Hochreiter, Michael Mozer: A Discrete Probabilistic Memory Model for Discovering Dependencies in Time. ICANN 2001: 661-668

Michael C. Mozer, Michael D. Colagrosso, David E. Huber: A Rational Analysis of Cognitive Control in a Speeded Discrimination Task. NIPS 2001: 51-57

Michael C. Mozer, Robert H. Dodier, Michael D. Colagrosso, Cesar Guerra-Salcedo, Richard H. Wolniewicz: Prodding the ROC Curve: Constrained Optimization of Classifier Performance. NIPS 2001: 1409-1415

Michael C. Mozer, Richard H. Wolniewicz, David B. Grimes, Eric Johnson, Howard Kaushansky: Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Trans. Neural Netw. Learning Syst. 11(3): 690-696 (2000)

David B. Grimes, Michael Mozer: The Interplay of Symbolic and Subsymbolic Processes in Anagram Problem Solving. NIPS 2000: 17-23

Sepp Hochreiter, Michael Mozer: Beyond Maximum Likelihood and Density Estimation: A Sample-Based Criterion for Unsupervised Learning of Complex Models. NIPS 2000: 535-541

Jay A. Alexander, Michael Mozer: Template-based procedures for neural network interpretation. Neural Networks 12(3): 479-498 (1999)

Soo-Young Lee, Michael Mozer: Robust Recognition of Noisy and Superimposed Patterns via Selective Attention. NIPS 1999: 31-37

Richard S. Zemel, Michael Mozer: A Generative Model for Attractor Dynamics. NIPS 1999: 80-88

Michael Mozer, Richard H. Wolniewicz, David B. Grimes, Eric Johnson, Howard Kaushansky: Churn Reduction in the Wireless Industry. NIPS 1999: 935-941

Sreerupa Das, Michael Mozer: Dynamic On-line Clustering and State Extraction: An Approach to Symbolic Learning. Neural Networks 11(1): 53-64 (1998)

Michael Mozer: A Principle for Unsupervised Hierarchical Decomposition of Visual Scenes. NIPS 1998: 52-58

Brad Calder, Dirk Grunwald, Michael P. Jones, Donald C. Lindsay, James H. Martin, Michael Mozer, Benjamin G. Zorn: Evidence-Based Static Branch Prediction Using Machine Learning. ACM Trans. Program. Lang. Syst. 19(1): 188-222 (1997)

Michael Mozer, Mark Sitton, Martha J. Farah: A Superadditive-Impairment Theory of Optic Aphasia. NIPS 1997

Michael Mozer, Debra Miller: Parsing the Stream of Time: The Value of Event-Based Segmentation in a Complex Real-World Control Problem. Summer School on Neural Networks 1997: 370-388

Michael Mozer, Michael I. Jordan, Thomas Petsche (Eds.): Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA, December 2-5, 1996. MIT Press 1997

Michael Mozer, Lucky Vidmar, Robert H. Dodier: The Neurothermostat: Predictive Optimal Control of Residential Heating Systems. NIPS 1996: 953-959

David S. Touretzky, Michael Mozer, Michael E. Hasselmo (Eds.): Advances in Neural Information Processing Systems 8, NIPS, Denver, CO, November 27-30, 1995. MIT Press 1996, isbn 0-262-20107-0

Richard S. Zemel, Christopher K. I. Williams, Michael Mozer: Lending direction to neural networks. Neural Networks 8(4): 503-512 (1995)

Brad Calder, Dirk Grunwald, Donald C. Lindsay, James H. Martin, Michael Mozer, Benjamin G. Zorn: Corpus-Based Static Branch Prediction. PLDI 1995: 79-92

Donald W. Mathis, Michael Mozer: On the Computational Utility of Consciousness. NIPS 1994: 11-18

Jay A. Alexander, Michael Mozer: Template-Based Algorithms for Connectionist Rule Extraction. NIPS 1994: 609-616

Clayton McMillan, Michael Mozer, Paul Smolensky: Dynamic Conflict Resolution in a Connectionist Rule-Based System. IJCAI 1993: 1366-1373

Sreerupa Das, Michael Mozer: A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction. NIPS 1993: 19-26

Michael C. Mozer, Richard S. Zemel, Marlene Behrmann, Christopher K. I. Williams: Learning to Segment Images Using Dynamic Feature Binding. Neural Computation 4(5): 650-665 (1992)

Brian V. Bonnlander, Michael Mozer: Metamorphosis Networks: An Alternative to Constructive Models. NIPS 1992: 131-138

Richard S. Zemel, Christopher K. I. Williams, Michael Mozer: Directional-Unit Boltzmann Machines. NIPS 1992: 172-179

Michael Mozer, Sreerupa Das: A Connectionist Symbol Manipulator that Discovers the Structure of Context-Free Languages. NIPS 1992: 863-870

Michael C. Mozer: Perception of multiple objects - a connectionist approach. Neural network modeling and connectionism, MIT Press 1991, isbn 978-0-262-13270-1, pp. 1-217

Michael Mozer, Jonathan Bachrach: SLUG: A Connectionist Architecture for Inferring the Structure of Finite-State Environments. Machine Learning 7: 139-160 (1991)

Michael Mozer: Induction of Multiscale Temporal Structure. NIPS 1991: 275-282

Michael Mozer, Richard S. Zemel, Marlene Behrmann: Learning to Segment Images Using Dynamic Feature Binding. NIPS 1991: 436-443

Clayton McMillan, Michael Mozer, Paul Smolensky: Rule Induction through Integrated Symbolic and Subsymbolic Processing. NIPS 1991: 969-976

Michael Mozer: Neural network music composition and the induction of multiscale temporal structure. Wissensbasierte Systeme 1991: 448-458

Michael Mozer: Discovering Discrete Distributed Representations. NIPS 1990: 627-634

Michael Mozer, Todd Soukup: Connectionist Music Composition Based on Melodic and Stylistic Constraints. NIPS 1990: 789-796

Richard S. Zemel, Michael Mozer, Geoffrey E. Hinton: TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations. NIPS 1989: 266-273

Michael Mozer, Jonathan Bachrach: Discovering the Structure of a Reactive Environment by Exploration. NIPS 1989: 439-446

Michael Mozer, Paul Smolensky: Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment. NIPS 1988: 107-115