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ORCIDMichael C. Mozer
Michael Curtis Mozer – Michael Mozer
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- affiliation: University of Colorado, USA
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2020 – today
- 2023
[i43]Sumukh K. Aithal, Anirudh Goyal, Alex Lamb, Yoshua Bengio, Michael Mozer:
Leveraging the Third Dimension in Contrastive Learning. CoRR abs/2301.11790 (2023)- [i42]Nan Rosemary Ke, Sara-Jane Dunn, Jörg Bornschein, Silvia Chiappa, Mélanie Rey, Jean-Baptiste Lespiau, Albin Cassirer, Jane X. Wang, Theophane Weber, David G. T. Barrett, Matthew M. Botvinick, Anirudh Goyal, Michael Mozer, Danilo J. Rezende:
DiscoGen: Learning to Discover Gene Regulatory Networks. CoRR abs/2304.05823 (2023) - 2022
- [c87]Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, Michael C. Mozer:
Mitigating Bias in Calibration Error Estimation. AISTATS 2022: 4036-4054 - [c86]Anirudh Goyal, Aniket Rajiv Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Curtis Mozer, Yoshua Bengio:
Coordination Among Neural Modules Through a Shared Global Workspace. ICLR 2022 - [c85]Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C. Mozer:
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. ICML 2022: 6009-6033 - [c84]Gamaleldin F. Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael C. Mozer, Thomas Kipf:
SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos. NeurIPS 2022 - [i41]Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C. Mozer:
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. CoRR abs/2201.03529 (2022) - [i40]Dianbo Liu, Alex Lamb, Xu Ji, Pascal Notsawo, Michael Mozer, Yoshua Bengio, Kenji Kawaguchi:
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization. CoRR abs/2202.01334 (2022) - [i39]Shruthi Sukumar, Adrian F. Ward
, Camden Elliott-Williams, Shabnam Hakimi, Michael C. Mozer:
Overcoming Temptation: Incentive Design For Intertemporal Choice. CoRR abs/2203.05782 (2022) - [i38]Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jörg Bornschein, Theophane Weber, Anirudh Goyal, Matthew M. Botvinick, Michael Mozer, Danilo Jimenez Rezende:
Learning to Induce Causal Structure. CoRR abs/2204.04875 (2022) - [i37]Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio:
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel. CoRR abs/2205.10607 (2022) - [i36]Gamaleldin F. Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael C. Mozer, Thomas Kipf:
SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos. CoRR abs/2206.07764 (2022) - [i35]Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf:
Discrete Key-Value Bottleneck. CoRR abs/2207.11240 (2022) - [i34]Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio:
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2210.03022 (2022) - [i33]Tyler R. Scott, Ting Liu, Michael C. Mozer, Andrew C. Gallagher:
An Empirical Study on Clustering Pretrained Embeddings: Is Deep Strictly Better? CoRR abs/2211.05183 (2022) - [i32]Amr Khalifa, Michael C. Mozer, Hanie Sedghi, Behnam Neyshabur, Ibrahim Alabdulmohsin:
Layer-Stack Temperature Scaling. CoRR abs/2211.10193 (2022) - 2021
- [j26]Brett D. Roads, Michael C. Mozer:
Predicting the Ease of Human Category Learning Using Radial Basis Function Networks. Neural Comput. 33(2): 376-397 (2021) - [c83]David Y. J. Kim, Tyler R. Scott, Debshila Basu Mallick, Michael C. Mozer:
Using Semantics of Textbook Highlights to Predict Student Comprehension and Knowledge Retention. iTextbooks@AIED 2021: 108-120 - [c82]Alex Lamb, Anirudh Goyal, Agnieszka Slowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio:
Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers. AISTATS 2021: 919-927 - [c81]Tyler R. Scott, Andrew C. Gallagher, Michael C. Mozer:
von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning. ICCV 2021: 10592-10602 - [c80]Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer:
Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments. ICLR 2021 - [c79]Mengye Ren, Michael Louis Iuzzolino, Michael Curtis Mozer, Richard S. Zemel:
Wandering within a world: Online contextualized few-shot learning. ICLR 2021 - [c78]Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, Michael C. Mozer:
Characterizing Structural Regularities of Labeled Data in Overparameterized Models. ICML 2021: 5034-5044 - [c77]Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer:
Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers. ICML 2021: 10225-10235 - [c76]Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael C. Mozer, Yoshua Bengio:
Discrete-Valued Neural Communication. NeurIPS 2021: 2109-2121 - [c75]Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio:
Neural Production Systems. NeurIPS 2021: 25673-25687 - [c74]Michael L. Iuzzolino, Michael C. Mozer, Samy Bengio:
Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss. NeurIPS 2021: 27631-27644 - [c73]Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs:
Soft Calibration Objectives for Neural Networks. NeurIPS 2021: 29768-29779 - [c72]Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Jimenez Rezende, Michael Mozer, Yoshua Bengio, Chris Pal:
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. NeurIPS Datasets and Benchmarks 2021 - [c71]Zeqian Li, Michael Mozer, Jacob Whitehill:
Compositional Embeddings for Multi-Label One-Shot Learning. WACV 2021: 296-304 - [i31]Michael L. Iuzzolino, Michael C. Mozer, Samy Bengio:
Training cascaded networks for speeded decisions using a temporal-difference loss. CoRR abs/2102.09808 (2021) - [i30]Anirudh Goyal, Aniket Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Mozer, Yoshua Bengio:
Coordination Among Neural Modules Through a Shared Global Workspace. CoRR abs/2103.01197 (2021) - [i29]Anirudh Goyal, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio:
Neural Production Systems. CoRR abs/2103.01937 (2021) - [i28]Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer:
Understanding invariance via feedforward inversion of discriminatively trained classifiers. CoRR abs/2103.07470 (2021) - [i27]Tyler R. Scott, Andrew C. Gallagher, Michael C. Mozer:
von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning. CoRR abs/2103.15718 (2021) - [i26]Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Christopher J. Pal:
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. CoRR abs/2107.00848 (2021) - [i25]Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio:
Discrete-Valued Neural Communication. CoRR abs/2107.02367 (2021) - [i24]Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs:
Soft Calibration Objectives for Neural Networks. CoRR abs/2108.00106 (2021) - [i23]Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke:
Learning Neural Causal Models with Active Interventions. CoRR abs/2109.02429 (2021) - [i22]Mengye Ren, Tyler R. Scott, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel:
Online Unsupervised Learning of Visual Representations and Categories. CoRR abs/2109.05675 (2021) - 2020
- [j25]Adam Winchell, Andrew S. Lan, Michael Mozer:
Highlights as an Early Predictor of Student Comprehension and Interests. Cogn. Sci. 44(11) (2020) - [j24]Nicole M. Beckage, Michael C. Mozer, Eliana Colunga:
Quantifying the Role of Vocabulary Knowledge in Predicting Future Word Learning. IEEE Trans. Cogn. Dev. Syst. 12(2): 148-159 (2020) - [c70]David Y. J. Kim, Adam Winchell, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk, Michael Mozer:
Inferring Student Comprehension from Highlighting Patterns in Digital Textbooks: An Exploration in an Authentic Learning Platform. iTextbooks@AIED 2020: 67-79 - [c69]Guy Davidson, Michael C. Mozer:
Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to Forget. CVPR 2020: 9279-9290 - [c68]Chiyuan Zhang, Samy Bengio, Moritz Hardt, Michael C. Mozer, Yoram Singer:
Identity Crisis: Memorization and Generalization Under Extreme Overparameterization. ICLR 2020 - [c67]Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio:
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. ICML 2020: 6972-6986 - [i21]Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, Michael C. Mozer:
Exploring the Memorization-Generalization Continuum in Deep Learning. CoRR abs/2002.03206 (2020) - [i20]Zeqian Li, Michael C. Mozer, Jacob Whitehill:
Compositional Embeddings for Multi-Label One-Shot Learning. CoRR abs/2002.04193 (2020) - [i19]Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Sergey Levine, Charles Blundell, Yoshua Bengio, Michael Mozer:
Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems. CoRR abs/2006.16225 (2020) - [i18]Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio:
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. CoRR abs/2006.16981 (2020) - [i17]Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel:
Wandering Within a World: Online Contextualized Few-Shot Learning. CoRR abs/2007.04546 (2020) - [i16]Maria Attarian, Brett D. Roads, Michael C. Mozer:
Transforming Neural Network Visual Representations to Predict Human Judgments of Similarity. CoRR abs/2010.06512 (2020) - [i15]Alex Lamb, Anirudh Goyal, Agnieszka Slowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio:
Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers. CoRR abs/2010.08012 (2020) - [i14]Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, Michael C. Mozer:
Mitigating bias in calibration error estimation. CoRR abs/2012.08668 (2020)
2010 – 2019
- 2019
- [c66]Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer:
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. ICML 2019: 3622-3631 - [i13]Guy Davidson, Michael C. Mozer:
Sequential mastery of multiple tasks: Networks naturally learn to learn. CoRR abs/1905.10837 (2019) - [i12]Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Denis Kazakov, Yoshua Bengio, Michael C. Mozer:
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. CoRR abs/1905.11382 (2019) - [i11]Michael L. Iuzzolino, Yoram Singer, Michael C. Mozer:
Convolutional Bipartite Attractor Networks. CoRR abs/1906.03504 (2019) - [i10]Tyler R. Scott, Karl Ridgeway, Michael C. Mozer:
Stochastic Prototype Embeddings. CoRR abs/1909.11702 (2019) - 2018
- [j23]Robert V. Lindsey, Aaron Daluiski, Sumit Chopra, Alexander Lachapelle, Michael Mozer, Serge Sicular, Douglas Hanel, Michael Gardner, Anurag Gupta, Robert Hotchkiss, Hollis Potter:
Deep neural network improves fracture detection by clinicians. Proc. Natl. Acad. Sci. USA 115(45): 11591-11596 (2018) - [c65]Mohammad M. Khajah, Michael C. Mozer, Sean Kelly, Brent Milne:
Boosting Engagement with Educational Software Using Near Wins. AIED (2) 2018: 171-175 - [c64]Shirly Montero, Akshit Arora, Sean Kelly, Brent Milne, Michael Mozer:
Does Deep Knowledge Tracing Model Interactions Among Skills? EDM 2018 - [c63]Adam Winchell, Michael Mozer, Andrew S. Lan, Phillip Grimaldi, Harold Pashler:
Textbook annotations as an early predictor of student learning. EDM 2018 - [c62]Tyler R. Scott, Karl Ridgeway, Michael C. Mozer:
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning. NeurIPS 2018: 76-85 - [c61]Karl Ridgeway, Michael C. Mozer:
Learning Deep Disentangled Embeddings With the F-Statistic Loss. NeurIPS 2018: 185-194 - [c60]Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio:
Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding. NeurIPS 2018: 7651-7662 - [i9]Karl Ridgeway, Michael C. Mozer:
Learning Deep Disentangled Embeddings with the F-Statistic Loss. CoRR abs/1802.05312 (2018) - [i8]Michael C. Mozer, Denis Kazakov, Robert V. Lindsey:
State-Denoised Recurrent Neural Networks. CoRR abs/1805.08394 (2018) - [i7]Tyler R. Scott, Karl Ridgeway, Michael C. Mozer:
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning. CoRR abs/1805.08402 (2018) - [i6]Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio:
Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding. CoRR abs/1809.03702 (2018) - [i5]Karl Ridgeway, Michael C. Mozer:
Open-Ended Content-Style Recombination Via Leakage Filtering. CoRR abs/1810.00110 (2018) - 2017
- [j22]Karl Ridgeway, Michael C. Mozer, Anita R. Bowles:
Forgetting of Foreign-Language Skills: A Corpus-Based Analysis of Online Tutoring Software. Cogn. Sci. 41(4): 924-949 (2017) - [j21]Brett D. Roads, Michael C. Mozer:
Improving Human-Machine Cooperative Classification Via Cognitive Theories of Similarity. Cogn. Sci. 41(5): 1394-1411 (2017) - [j20]Ronald T. Kneusel, Michael C. Mozer:
Improving Human-Machine Cooperative Visual Search With Soft Highlighting. ACM Trans. Appl. Percept. 15(1): 3:1-3:21 (2017) - [c59]Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel:
Learning to generate images with perceptual similarity metrics. ICIP 2017: 4277-4281 - [i4]Michael C. Mozer, Denis Kazakov, Robert V. Lindsey:
Discrete Event, Continuous Time RNNs. CoRR abs/1710.04110 (2017) - 2016
- [c58]Mohammad Khajah, Brett D. Roads, Robert V. Lindsey, Yun-En Liu, Michael C. Mozer:
Designing Engaging Games Using Bayesian Optimization. CHI 2016: 5571-5582 - [c57]Mohammad Khajah, Robert V. Lindsey, Michael Mozer:
How Deep is Knowledge Tracing? EDM 2016 - [i3]Mohammad Khajah, Robert V. Lindsey, Michael C. Mozer:
How deep is knowledge tracing? CoRR abs/1604.02416 (2016) - [i2]Ronald T. Kneusel, Michael C. Mozer:
Improving Human-Machine Cooperative Visual Search With Soft Highlighting. CoRR abs/1612.08117 (2016) - 2015
- [c56]Nicole Beckage, Michael Mozer, Eliana Colunga:
Predicting a Child's Trajectory of Lexical Acquisition. CogSci 2015 - [i1]Karl Ridgeway, Jake Snell, Brett Roads, Richard S. Zemel, Michael C. Mozer:
Learning to generate images with perceptual similarity metrics. CoRR abs/1511.06409 (2015) - 2014
- [j19]Mohammad Khajah, Robert V. Lindsey, Michael C. Mozer:
Maximizing Students' Retention via Spaced Review: Practical Guidance From Computational Models of Memory. Top. Cogn. Sci. 6(1): 157-169 (2014) - [c55]Mohammad Khajah, Rowan Wing, Robert V. Lindsey, Michael Mozer:
Integrating latent-factor and knowledge-tracing models to predict individual differences in learning. EDM 2014: 99-106 - [c54]Robert V. Lindsey, Mohammad Khajah, Michael C. Mozer:
Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning. NIPS 2014: 1386-1394 - [c53]Mohammad Khajah, Yun Huang, José P. González-Brenes, Michael Mozer, Peter Brusilovsky:
Integrating Knowledge Tracing and Item Response Theory: A Tale of Two Frameworks. UMAP Workshops 2014 - 2013
- [c52]Mohammad Khajah, Robert V. Lindsey, Michael Mozer:
Maximizing Students' Retention Via Spaced Review: Practical Guidance From Computational Models Of Memory. CogSci 2013 - [c51]Robert V. Lindsey, Michael Mozer, William J. Huggins, Harold Pashler:
Optimizing Instructional Policies. NIPS 2013: 2778-2786 - 2012
- [j18]Anup Doshi, Cuong Tran, Matthew H. Wilder, Michael C. Mozer, Mohan M. Trivedi:
Sequential Dependencies in Driving. Cogn. Sci. 36(5): 948-963 (2012) - 2011
- [c50]Benjamin Link, Brittany Ann Kos, Tor D. Wager, Michael Mozer:
Past Experience Influences Judgment of Pain: Prediction of Sequential Dependencies. CogSci 2011 - [c49]Michael C. Mozer, Benjamin Link, Harold Pashler:
An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments. NIPS 2011: 1791-1799 - 2010
- [c48]Michael Mozer, Harold Pashler, Matthew H. Wilder, Robert V. Lindsey, Matt Jones, Michael Jones:
Improving Human Judgments by Decontaminating Sequential Dependencies. NIPS 2010: 1705-1713
2000 – 2009
- 2009
- [c47]Dan Knights, Todd Mytkowicz, Peter F. Sweeney, Michael C. Mozer, Amer Diwan:
Blind Optimization for Exploiting Hardware Features. CC 2009: 251-265 - [c46]Dan Knights, Michael C. Mozer, Nicolas Nicolov:
Detecting Topic Drift with Compound Topic Models. ICWSM 2009 - [c45]Michael Mozer, Harold Pashler, Nicholas Cepeda, Robert V. Lindsey, Ed Vul:
Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory. NIPS 2009: 1321-1329 - [c44]Matthew H. Wilder, Matt Jones, Michael Mozer:
Sequential effects reflect parallel learning of multiple environmental regularities. NIPS 2009: 2053-2061 - 2008
- [j17]Michael C. Mozer, Harold Pashler, Hadjar Homaei:
Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds? Cogn. Sci. 32(7): 1133-1147 (2008) - [j16]Michael C. Mozer, Adrian Fan:
Top-Down modulation of neural responses in visual perception: a computational exploration. Nat. Comput. 7(1): 45-55 (2008) - [c43]Matt Jones, Michael C. Mozer, Sachiko Kinoshita:
Optimal Response Initiation: Why Recent Experience Matters. NIPS 2008: 785-792 - [c42]Jeremy Reynolds, Michael C. Mozer:
Temporal Dynamics of Cognitive Control. NIPS 2008: 1353-1360 - 2007
- [j15]Sander M. Bohté, Michael C. Mozer:
Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity. Neural Comput. 19(2): 371-403 (2007) - [c41]Michael Mozer, David Baldwin:
Experience-Guided Search: A Theory of Attentional Control. NIPS 2007: 1033-1040 - [p2]Michael C. Mozer, Sachiko Kinoshita, Michael Shettel:
Sequential Dependencies in Human Behavior Offer Insights Into Cognitive Control. Integrated Models of Cognitive Systems 2007: 180-193 - [p1]Sepp Hochreiter, Michael C. Mozer:
Monaural Speech Separation by Support Vector Machines: Bridging the Divide Between Supervised and Unsupervised Learning Methods. Blind Speech Separation 2007: 411-428 - 2006
- [c40]Michael C. Mozer, Michael Jones, Michael Shettel:
Context Effects in Category Learning: An Investigation of Four Probabilistic Models. NIPS 2006: 993-1000 - [c39]Michael C. Mozer:
Rational Models of Cognitive Control. UC 2006: 20-25 - 2005
- [c38]Sander M. Bohté, Michael C. Mozer:
Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity. BNAIC 2005: 319-320 - [c37]Michael Mozer, Michael Shettel, Shaun Vecera:
Top-Down Control of Visual Attention: A Rational Account. NIPS 2005: 923-930 - [c36]Matthias Hauswirth, Amer Diwan, Peter F. Sweeney, Michael C. Mozer:
Automating vertical profiling. OOPSLA 2005: 281-296 - 2004
- [c35]Michael Mozer:
How Practice Makes Perfect. ICCM 2004: 13-13 - [c34]Sander M. Bohté, Michael C. Mozer:
Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity. NIPS 2004: 201-208 - [c33]Michael D. Colagrosso, Michael C. Mozer:
Theories of Access Consciousness. NIPS 2004: 289-296 - 2003
- [c32]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 - 2002
- [c31]Sepp Hochreiter, Michael Mozer, Klaus Obermayer:
Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems. NIPS 2002: 545-552 - 2001
- [j14]Harold Pashler, Michael C. Mozer, Christine R. Harris:
Mating Strategies in a Darwinian Microworld: Simulating the Consequences of Female Reproductive Refractoriness. Adapt. Behav. 9(1): 5-15 (2001) - [j13]Richard S. Zemel, Michael Mozer:
Localist Attractor Networks. Neural Comput. 13(5): 1045-1064 (2001) - [c30]Sepp Hochreiter, Michael Mozer:
A Discrete Probabilistic Memory Model for Discovering Dependencies in Time. ICANN 2001: 661-668 - [c29]Michael C. Mozer, Michael D. Colagrosso, David E. Huber:
A Rational Analysis of Cognitive Control in a Speeded Discrimination Task. NIPS 2001: 51-57 - [c28]