default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMichael C. Mozer
Michael Curtis Mozer – Michael Mozer
Person information
- affiliation: University of Colorado, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c96]Matt Jones, Tyler R. Scott, Michael C. Mozer:
Human-like Learning in Temporally Structured Environments. AAAI Spring Symposia 2024: 553- [c95]Katherine L. Hermann, Hossein Mobahi, Thomas Fel, Michael Curtis Mozer:
On the Foundations of Shortcut Learning. ICLR 2024 - [i55]Haonan Wang, James Zou, Michael Mozer, Anirudh Goyal, Alex Lamb, Linjun Zhang, Weijie J. Su, Zhun Deng, Michael Qizhe Xie, Hannah Brown, Kenji Kawaguchi:
Can AI Be as Creative as Humans? CoRR abs/2401.01623 (2024) - [i54]Yanlai Yang, Matt Jones, Michael C. Mozer, Mengye Ren:
Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training. CoRR abs/2403.09613 (2024) - [i53]Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Sanjeev Arora:
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. CoRR abs/2405.12205 (2024) - [i52]Anand Gopalakrishnan, Aleksandar Stanic, Jürgen Schmidhuber, Michael Curtis Mozer:
Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery. CoRR abs/2405.17283 (2024) - [i51]Vedant Shah, Dingli Yu, Kaifeng Lyu, Simon Park, Nan Rosemary Ke, Michael Mozer, Yoshua Bengio, Sanjeev Arora, Anirudh Goyal:
AI-Assisted Generation of Difficult Math Questions. CoRR abs/2407.21009 (2024) - [i50]Aniket Didolkar, Andrii Zadaianchuk, Anirudh Goyal, Michael C. Mozer, Yoshua Bengio, Georg Martius, Maximilian Seitzer:
Zero-Shot Object-Centric Representation Learning. CoRR abs/2408.09162 (2024) - [i49]Lukas Muttenthaler, Klaus Greff, Frieda Born, Bernhard Spitzer, Simon Kornblith, Michael C. Mozer, Klaus-Robert Müller, Thomas Unterthiner, Andrew K. Lampinen:
Aligning Machine and Human Visual Representations across Abstraction Levels. CoRR abs/2409.06509 (2024) - [i48]Michael A. Lepori, Michael Mozer, Asma Ghandeharioun:
Racing Thoughts: Explaining Large Language Model Contextualization Errors. CoRR abs/2410.02102 (2024) - 2023
- [j27]Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Bernhard Schölkopf, Michael Curtis Mozer, Christopher Pal, Yoshua Bengio:
Neural Causal Structure Discovery from Interventions. Trans. Mach. Learn. Res. 2023 (2023) - [c94]Dianbo Liu, Alex Lamb, Xu Ji, Pascal Tikeng Notsawo Jr., Michael Mozer, Yoshua Bengio, Kenji Kawaguchi:
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness. AAAI 2023: 8825-8833 - [c93]David Mayo, Tyler R. Scott, Mengye Ren, Gamaledin Elsayed, Katherine L. Hermann, Matt Jones, Michael Mozer:
Multitask Learning Via Interleaving: A Neural Network Investigation. CogSci 2023 - [c92]Matt Jones, Tyler R. Scott, Mengye Ren, Gamaleldin Fathy Elsayed, Katherine L. Hermann, David Mayo, Michael Curtis Mozer:
Learning in temporally structured environments. ICLR 2023 - [c91]Nan Rosemary Ke, Silvia Chiappa, Jane X. Wang, Jörg Bornschein, Anirudh Goyal, Mélanie Rey, Theophane Weber, Matthew M. Botvinick, Michael Curtis Mozer, Danilo Jimenez Rezende:
Learning to Induce Causal Structure. ICLR 2023 - [c90]Dianbo Liu, Vedant Shah, Oussama Boussif
, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Curtis Mozer, Nicolas Heess, Yoshua Bengio:
Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. ICLR 2023 - [c89]Pratyush Maini, Michael Curtis Mozer, Hanie Sedghi, Zachary Chase Lipton, J. Zico Kolter, Chiyuan Zhang:
Can Neural Network Memorization Be Localized? ICML 2023: 23536-23557 - [c88]Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf:
Discrete Key-Value Bottleneck. ICML 2023: 34431-34455 - [i47]Sumukh K. Aithal, Anirudh Goyal, Alex Lamb, Yoshua Bengio, Michael Mozer:
Leveraging the Third Dimension in Contrastive Learning. CoRR abs/2301.11790 (2023) - [i46]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) - [i45]Ayush Chakravarthy, Trang Nguyen, Anirudh Goyal, Yoshua Bengio, Michael C. Mozer:
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior. CoRR abs/2305.19550 (2023) - [i44]Pratyush Maini, Michael C. Mozer, Hanie Sedghi, Zachary C. Lipton, J. Zico Kolter, Chiyuan Zhang:
Can Neural Network Memorization Be Localized? CoRR abs/2307.09542 (2023) - [i43]Katherine L. Hermann, Hossein Mobahi, Thomas Fel, Michael C. Mozer:
On the Foundations of Shortcut Learning. CoRR abs/2310.16228 (2023) - [i42]Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal:
Unlearning via Sparse Representations. CoRR abs/2311.15268 (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]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 - 2000
- [j12]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 Networks Learn. Syst. 11(3): 690-696 (2000) - [c27]David B. Grimes, Michael Mozer:
The Interplay of Symbolic and Subsymbolic Processes in Anagram Problem Solving. NIPS 2000: 17-23 - [c26]Sepp Hochreiter, Michael Mozer:
Beyond Maximum Likelihood and Density Estimation: A Sample-Based Criterion for Unsupervised Learning of Complex Models. NIPS 2000: 535-541
1990 – 1999
- 1999
- [j11]Haym Hirsh, Michael H. Coen, Michael C. Mozer, Richard Hasha, James L. Flanagan:
Room service, AI-style. IEEE Intell. Syst. 14(2): 8-19 (1999) - [j10]Michael C. Mozer:
An Intelligent Environment Must Be Adaptive. IEEE Intell. Syst. 14(2): 11-13 (1999) - [j9]Jay A. Alexander, Michael Mozer:
Template-based procedures for neural network interpretation. Neural Networks 12(3): 479-498 (1999) - [c25]Soo-Young Lee, Michael Mozer:
Robust Recognition of Noisy and Superimposed Patterns via Selective Attention. NIPS 1999: 31-37 - [c24]Richard S. Zemel, Michael Mozer:
A Generative Model for Attractor Dynamics. NIPS 1999: 80-88 - [c23]Michael Mozer, Richard H. Wolniewicz, David B. Grimes, Eric Johnson, Howard Kaushansky:
Churn Reduction in the Wireless Industry. NIPS 1999: 935-941 - 1998
- [j8]Sreerupa Das, Michael Mozer:
Dynamic On-line Clustering and State Extraction: An Approach to Symbolic Learning. Neural Networks 11(1): 53-64 (1998) - [c22]Michael Mozer:
A Principle for Unsupervised Hierarchical Decomposition of Visual Scenes. NIPS 1998: 52-58 - 1997
- [j7]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) - [c21]Yayoi Uno, Michael C. Mozer:
Neural net Architectures in Modeling Compositional Syntax: Prediction and Perception of Continuity in Minimalist Works by Phillip Glass and Louis Andriessen. ICMC 1997 - [c20]Michael Mozer, Mark Sitton, Martha J. Farah:
A Superadditive-Impairment Theory of Optic Aphasia. NIPS 1997: 66-72 - [c19]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 - [e2]Michael Mozer, Michael I. Jordan, Thomas Petsche:
Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA, December 2-5, 1996. MIT Press 1997 [contents] - 1996
- [c18]Michael Mozer, Lucky Vidmar, Robert H. Dodier:
The Neurothermostat: Predictive Optimal Control of Residential Heating Systems. NIPS 1996: 953-959 - [e1]David S. Touretzky, Michael Mozer, Michael E. Hasselmo:
Advances in Neural Information Processing Systems 8, NIPS, Denver, CO, USA, November 27-30, 1995. MIT Press 1996, ISBN 0-262-20107-0 [contents] - 1995
- [j6]Richard S. Zemel, Christopher K. I. Williams, Michael Mozer:
Lending direction to neural networks. Neural Networks 8(4): 503-512 (1995) - [c17]Brad Calder, Dirk Grunwald, Donald C. Lindsay, James H. Martin, Michael Mozer, Benjamin G. Zorn:
Corpus-Based Static Branch Prediction. PLDI 1995: 79-92 - 1994
- [j5]Michael C. Mozer:
Neural Network Music Composition by Prediction: Exploring the Benefits of Psychoacoustic Constraints and Multi-scale Processing. Connect. Sci. 6(2-3): 247-280 (1994) - [c16]Donald W. Mathis, Michael Mozer:
On the Computational Utility of Consciousness. NIPS 1994: 11-18 - [c15]Jay A. Alexander, Michael Mozer:
Template-Based Algorithms for Connectionist Rule Extraction. NIPS 1994: 609-616 - 1993
- [c14]Clayton McMillan, Michael Mozer, Paul Smolensky:
Dynamic Conflict Resolution in a Connectionist Rule-Based System. IJCAI 1993: 1366-1373 - [c13]Sreerupa Das, Michael Mozer:
A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction. NIPS 1993: 19-26 - 1992
- [j4]Michael C. Mozer, Richard S. Zemel, Marlene Behrmann, Christopher K. I. Williams:
Learning to Segment Images Using Dynamic Feature Binding. Neural Comput. 4(5): 650-665 (1992) - [c12]Brian V. Bonnlander, Michael Mozer:
Metamorphosis Networks: An Alternative to Constructive Models. NIPS 1992: 131-138 - [c11]Richard S. Zemel, Christopher K. I. Williams, Michael Mozer:
Directional-Unit Boltzmann Machines. NIPS 1992: 172-179 - [c10]Michael Mozer, Sreerupa Das:
A Connectionist Symbol Manipulator that Discovers the Structure of Context-Free Languages. NIPS 1992: 863-870 - 1991
- [b1]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 - [j3]Michael Mozer, Jonathan Bachrach:
SLUG: A Connectionist Architecture for Inferring the Structure of Finite-State Environments. Mach. Learn. 7: 139-160 (1991) - [c9]Michael Mozer:
Induction of Multiscale Temporal Structure. NIPS 1991: 275-282 - [c8]Michael Mozer, Richard S. Zemel, Marlene Behrmann:
Learning to Segment Images Using Dynamic Feature Binding. NIPS 1991: 436-443 - [c7]Clayton McMillan, Michael Mozer, Paul Smolensky:
Rule Induction through Integrated Symbolic and Subsymbolic Processing. NIPS 1991: 969-976 - [c6]Michael Mozer:
Neural network music composition and the induction of multiscale temporal structure. Wissensbasierte Systeme 1991: 448-458 - 1990
- [j2]Michael C. Mozer, Jonathan Bachrach:
Discovering the Structure of a Reactive Environment by Exploration. Neural Comput. 2(4): 447-457 (1990) - [c5]Michael Mozer:
Discovering Discrete Distributed Representations. NIPS 1990: 627-634 - [c4]Michael Mozer, Todd Soukup:
Connectionist Music Composition Based on Melodic and Stylistic Constraints. NIPS 1990: 789-796
1980 – 1989
- 1989
- [j1]Michael C. Mozer:
A Focused Backpropagation Algorithm for Temporal Pattern Recognition. Complex Syst. 3(4) (1989) - [c3]Richard S. Zemel, Michael Mozer, Geoffrey E. Hinton:
TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations. NIPS 1989: 266-273 - [c2]Michael Mozer, Jonathan Bachrach:
Discovering the Structure of a Reactive Environment by Exploration. NIPS 1989: 439-446 - 1988
- [c1]Michael Mozer, Paul Smolensky:
Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment. NIPS 1988: 107-115
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-11 21:30 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
