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

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

A Focused Backpropagation Algorithm for Temporal Pattern Recognition. - Michael C. Mozer (Complex Syst., 1989)

Discovering Discrete Distributed Representations. - Michael Mozer (NIPS, 1990)

Connectionist Music Composition Based on Melodic and Stylistic Constraints. - Michael Mozer; Todd Soukup (NIPS, 1990)

Discovering the Structure of a Reactive Environment by Exploration. - Michael C. Mozer; Jonathan Bachrach (Neural Comput., 1990)

Perception of multiple objects - a connectionist approach. - Michael C. Mozer (MIT Press, 1991)

Rule Induction through Integrated Symbolic and Subsymbolic Processing. - Clayton McMillan; Michael Mozer; Paul Smolensky (NIPS, 1991)

Induction of Multiscale Temporal Structure. - Michael Mozer (NIPS, 1991)

Learning to Segment Images Using Dynamic Feature Binding. - Michael Mozer; Richard S. Zemel; Marlene Behrmann (NIPS, 1991)

Neural network music composition and the induction of multiscale temporal structure. - Michael Mozer (Wissensbasierte Systeme, 1991)

SLUG: A Connectionist Architecture for Inferring the Structure of Finite-State Environments. - Michael Mozer; Jonathan Bachrach (Mach. Learn., 1991)

Metamorphosis Networks: An Alternative to Constructive Models. - Brian V. Bonnlander; Michael Mozer (NIPS, 1992)

A Connectionist Symbol Manipulator that Discovers the Structure of Context-Free Languages. - Michael Mozer; Sreerupa Das (NIPS, 1992)

Directional-Unit Boltzmann Machines. - Richard S. Zemel; Christopher K. I. Williams; Michael Mozer (NIPS, 1992)

Learning to Segment Images Using Dynamic Feature Binding. - Michael C. Mozer; Richard S. Zemel; Marlene Behrmann; Christopher K. I. Williams (Neural Comput., 1992)

Dynamic Conflict Resolution in a Connectionist Rule-Based System. - Clayton McMillan; Michael Mozer; Paul Smolensky (IJCAI, 1993)

A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction. - Sreerupa Das; Michael Mozer (NIPS, 1993)

Template-Based Algorithms for Connectionist Rule Extraction. - Jay A. Alexander; Michael Mozer (NIPS, 1994)

On the Computational Utility of Consciousness. - Donald W. Mathis; Michael Mozer (NIPS, 1994)

Neural Network Music Composition by Prediction: Exploring the Benefits of Psychoacoustic Constraints and Multi-scale Processing. - Michael C. Mozer (Connect. Sci., 1994)

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

Lending direction to neural networks. - Richard S. Zemel; Christopher K. I. Williams; Michael Mozer (Neural Networks, 1995)

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

Neural net Architectures in Modeling Compositional Syntax: Prediction and Perception of Continuity in Minimalist Works by Phillip Glass and Louis Andriessen. - Yayoi Uno; Michael C. Mozer (ICMC, 1997)

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

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

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

A Principle for Unsupervised Hierarchical Decomposition of Visual Scenes. - Michael Mozer (NIPS, 1998)

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

Robust Recognition of Noisy and Superimposed Patterns via Selective Attention. - Soo-Young Lee; Michael Mozer (NIPS, 1999)

Churn Reduction in the Wireless Industry. - Michael Mozer; Richard H. Wolniewicz; David B. Grimes; Eric Johnson; Howard Kaushansky (NIPS, 1999)

A Generative Model for Attractor Dynamics. - Richard S. Zemel; Michael Mozer (NIPS, 1999)

Room service, AI-style. - Haym Hirsh; Michael H. Coen; Michael C. Mozer; Richard Hasha; James L. Flanagan (IEEE Intell. Syst., 1999)

An Intelligent Environment Must Be Adaptive. - Michael C. Mozer (IEEE Intell. Syst., 1999)

Template-based procedures for neural network interpretation. - Jay A. Alexander; Michael Mozer (Neural Networks, 1999)

The Interplay of Symbolic and Subsymbolic Processes in Anagram Problem Solving. - David B. Grimes; Michael Mozer (NIPS, 2000)

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

Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. - Michael C. Mozer; Richard H. Wolniewicz; David B. Grimes; Eric Johnson; Howard Kaushansky (IEEE Trans. Neural Networks Learn. Syst., 2000)

A Discrete Probabilistic Memory Model for Discovering Dependencies in Time. - Sepp Hochreiter; Michael Mozer (ICANN, 2001)

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

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

Mating Strategies in a Darwinian Microworld: Simulating the Consequences of Female Reproductive Refractoriness. - Harold Pashler; Michael C. Mozer; Christine R. Harris (Adapt. Behav., 2001)

Localist Attractor Networks. - Richard S. Zemel; Michael Mozer (Neural Comput., 2001)

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

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

How Practice Makes Perfect. - Michael Mozer (ICCM, 2004)

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

Theories of Access Consciousness. - Michael D. Colagrosso; Michael C. Mozer (NIPS, 2004)

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

Automating vertical profiling. - Matthias Hauswirth; Amer Diwan; Peter F. Sweeney; Michael C. Mozer (OOPSLA, 2005)

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

Rational Models of Cognitive Control. - Michael C. Mozer (UC, 2006)

Sequential Dependencies in Human Behavior Offer Insights Into Cognitive Control. - Michael C. Mozer; Sachiko Kinoshita; Michael Shettel (Integrated Models of Cognitive Systems, 2007)

Monaural Speech Separation by Support Vector Machines: Bridging the Divide Between Supervised and Unsupervised Learning Methods. - Sepp Hochreiter; Michael C. Mozer (Blind Speech Separation, 2007)

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

Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity. - Sander M. Bohté; Michael C. Mozer (Neural Comput., 2007)

Optimal Response Initiation: Why Recent Experience Matters. - Matt Jones 001; Michael C. Mozer; Sachiko Kinoshita (NIPS, 2008)

Temporal Dynamics of Cognitive Control. - Jeremy Reynolds; Michael C. Mozer (NIPS, 2008)

Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds? - Michael C. Mozer; Harold Pashler; Hadjar Homaei (Cogn. Sci., 2008)

Top-Down modulation of neural responses in visual perception: a computational exploration. - Michael C. Mozer; Adrian Fan (Nat. Comput., 2008)

Blind Optimization for Exploiting Hardware Features. - Dan Knights; Todd Mytkowicz; Peter F. Sweeney; Michael C. Mozer; Amer Diwan (CC, 2009)

Detecting Topic Drift with Compound Topic Models. - Dan Knights; Michael C. Mozer; Nicolas Nicolov (ICWSM, 2009)

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

Sequential effects reflect parallel learning of multiple environmental regularities. - Matthew H. Wilder; Matt Jones 001; Michael Mozer (NIPS, 2009)

Improving Human Judgments by Decontaminating Sequential Dependencies. - Michael Mozer; Harold Pashler; Matthew H. Wilder; Robert V. Lindsey; Matt Jones 001; Michael Jones (NIPS, 2010)

Past Experience Influences Judgment of Pain: Prediction of Sequential Dependencies. - Benjamin Link; Brittany Ann Kos; Tor D. Wager; Michael Mozer (CogSci, 2011)

An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments. - Michael C. Mozer; Benjamin Link; Harold Pashler (NIPS, 2011)

Sequential Dependencies in Driving. - Anup Doshi; Cuong Tran 001; Matthew H. Wilder; Michael C. Mozer; Mohan M. Trivedi (Cogn. Sci., 2012)

Optimizing Instructional Policies. - Robert V. Lindsey; Michael Mozer; William J. Huggins; Harold Pashler (NIPS, 2013)

Integrating latent-factor and knowledge-tracing models to predict individual differences in learning. - Mohammad Khajah; Rowan Wing; Robert V. Lindsey; Michael Mozer (EDM, 2014)

Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning. - Robert V. Lindsey; Mohammad Khajah; Michael C. Mozer (NIPS, 2014)

Integrating Knowledge Tracing and Item Response Theory: A Tale of Two Frameworks. - Mohammad Khajah; Yun Huang 002; José P. González-Brenes; Michael Mozer; Peter Brusilovsky (UMAP Workshops, 2014)

Maximizing Students' Retention via Spaced Review: Practical Guidance From Computational Models of Memory. - Mohammad Khajah; Robert V. Lindsey; Michael C. Mozer (Top. Cogn. Sci., 2014)

Predicting a Child's Trajectory of Lexical Acquisition. - Nicole Beckage; Michael Mozer; Eliana Colunga (CogSci, 2015)

Learning to generate images with perceptual similarity metrics. - Karl Ridgeway; Jake Snell; Brett Roads; Richard S. Zemel; Michael C. Mozer (CoRR, 2015)

Designing Engaging Games Using Bayesian Optimization. - Mohammad Khajah; Brett D. Roads; Robert V. Lindsey; Yun-En Liu; Michael C. Mozer (CHI, 2016)

How Deep is Knowledge Tracing? - Mohammad Khajah; Robert V. Lindsey; Michael Mozer (EDM, 2016)

Learning to generate images with perceptual similarity metrics. - Jake Snell; Karl Ridgeway; Renjie Liao; Brett D. Roads; Michael C. Mozer; Richard S. Zemel (ICIP, 2017)

Forgetting of Foreign-Language Skills: A Corpus-Based Analysis of Online Tutoring Software. - Karl Ridgeway; Michael C. Mozer; Anita R. Bowles (Cogn. Sci., 2017)

Improving Human-Machine Cooperative Classification Via Cognitive Theories of Similarity. - Brett D. Roads; Michael C. Mozer (Cogn. Sci., 2017)

Discrete Event, Continuous Time RNNs. - Michael C. Mozer; Denis Kazakov; Robert V. Lindsey (CoRR, 2017)

Improving Human-Machine Cooperative Visual Search With Soft Highlighting. - Ronald T. Kneusel; Michael C. Mozer (ACM Trans. Appl. Percept., 2017)

Boosting Engagement with Educational Software Using Near Wins. - Mohammad M. Khajah; Michael C. Mozer; Sean Kelly; Brent Milne (AIED (2), 2018)

Does Deep Knowledge Tracing Model Interactions Among Skills? - Shirly Montero; Akshit Arora; Sean Kelly; Brent Milne; Michael Mozer (EDM, 2018)

Textbook annotations as an early predictor of student learning. - Adam Winchell; Michael Mozer; Andrew S. Lan; Phillip Grimaldi; Harold Pashler (EDM, 2018)

Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding. - Nan Rosemary Ke; Anirudh Goyal; Olexa Bilaniuk; Jonathan Binas; Michael C. Mozer; Chris Pal; Yoshua Bengio (NeurIPS, 2018)

Learning Deep Disentangled Embeddings With the F-Statistic Loss. - Karl Ridgeway; Michael C. Mozer (NeurIPS, 2018)

Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning. - Tyler R. Scott; Karl Ridgeway; Michael C. Mozer (NeurIPS, 2018)

State-Denoised Recurrent Neural Networks. - Michael C. Mozer; Denis Kazakov; Robert V. Lindsey (CoRR, 2018)

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding. - Nan Rosemary Ke; Anirudh Goyal; Olexa Bilaniuk; Jonathan Binas; Michael C. Mozer; Chris Pal; Yoshua Bengio (CoRR, 2018)

Open-Ended Content-Style Recombination Via Leakage Filtering. - Karl Ridgeway; Michael C. Mozer (CoRR, 2018)

Deep neural network improves fracture detection by clinicians. - Robert V. Lindsey; Aaron Daluiski; Sumit Chopra; Alexander Lachapelle; Michael Mozer; Serge Sicular; Douglas Hanel; Michael Gardner; Anurag Gupta; Robert Hotchkiss; Hollis Potter (Proc. Natl. Acad. Sci. USA, 2018)

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. - Alex Lamb; Jonathan Binas; Anirudh Goyal; Sandeep Subramanian; Ioannis Mitliagkas; Yoshua Bengio; Michael Mozer (ICML, 2019)

Sequential mastery of multiple tasks: Networks naturally learn to learn. - Guy Davidson; Michael C. Mozer (CoRR, 2019)

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. - Alex Lamb; Jonathan Binas; Anirudh Goyal; Sandeep Subramanian; Ioannis Mitliagkas; Denis Kazakov; Yoshua Bengio; Michael C. Mozer (CoRR, 2019)

Convolutional Bipartite Attractor Networks. - Michael L. Iuzzolino; Yoram Singer; Michael C. Mozer (CoRR, 2019)

Stochastic Prototype Embeddings. - Tyler R. Scott; Karl Ridgeway; Michael C. Mozer (CoRR, 2019)

Inferring Student Comprehension from Highlighting Patterns in Digital Textbooks: An Exploration in an Authentic Learning Platform. - David Y. J. Kim; Adam Winchell; Andrew E. Waters; Phillip Grimaldi; Richard G. Baraniuk; Michael Mozer (iTextbooks@AIED, 2020)

Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to Forget. - Guy Davidson; Michael C. Mozer (CVPR, 2020)

Identity Crisis: Memorization and Generalization Under Extreme Overparameterization. - Chiyuan Zhang; Samy Bengio; Moritz Hardt; Michael C. Mozer; Yoram Singer (ICLR, 2020)

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. - Sarthak Mittal; Alex Lamb; Anirudh Goyal; Vikram Voleti; Murray Shanahan; Guillaume Lajoie; Michael Mozer; Yoshua Bengio (ICML, 2020)

Highlights as an Early Predictor of Student Comprehension and Interests. - Adam Winchell; Andrew S. Lan; Michael Mozer (Cogn. Sci., 2020)

Exploring the Memorization-Generalization Continuum in Deep Learning. - Ziheng Jiang; Chiyuan Zhang; Kunal Talwar; Michael C. Mozer (CoRR, 2020)

Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems. - Anirudh Goyal; Alex Lamb; Phanideep Gampa; Philippe Beaudoin; Sergey Levine; Charles Blundell; Yoshua Bengio; Michael Mozer (CoRR, 2020)

Transforming Neural Network Visual Representations to Predict Human Judgments of Similarity. - Maria Attarian; Brett D. Roads; Michael C. Mozer (CoRR, 2020)

Quantifying the Role of Vocabulary Knowledge in Predicting Future Word Learning. - Nicole M. Beckage; Michael C. Mozer; Eliana Colunga (IEEE Trans. Cogn. Dev. Syst., 2020)

Using Semantics of Textbook Highlights to Predict Student Comprehension and Knowledge Retention. - David Y. J. Kim; Tyler R. Scott; Debshila Basu Mallick; Michael C. Mozer (iTextbooks@AIED, 2021)

Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers. - Alex Lamb; Anirudh Goyal; Agnieszka Slowik; Michael Mozer; Philippe Beaudoin; Yoshua Bengio (AISTATS, 2021)

von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning. - Tyler R. Scott; Andrew C. Gallagher; Michael C. Mozer (ICCV, 2021)

Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments. - Anirudh Goyal; Alex Lamb; Phanideep Gampa; Philippe Beaudoin; Charles Blundell; Sergey Levine; Yoshua Bengio; Michael Curtis Mozer (ICLR, 2021)

Wandering within a world: Online contextualized few-shot learning. - Mengye Ren; Michael Louis Iuzzolino; Michael Curtis Mozer; Richard S. Zemel (ICLR, 2021)

Characterizing Structural Regularities of Labeled Data in Overparameterized Models. - Ziheng Jiang; Chiyuan Zhang; Kunal Talwar; Michael C. Mozer (ICML, 2021)

Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers. - Piotr Teterwak; Chiyuan Zhang; Dilip Krishnan; Michael C. Mozer (ICML, 2021)

Neural Production Systems. - Aniket Didolkar; Anirudh Goyal; Nan Rosemary Ke; Charles Blundell; Philippe Beaudoin; Nicolas Heess; Michael Mozer; Yoshua Bengio (NeurIPS, 2021)

Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss. - Michael L. Iuzzolino; Michael C. Mozer; Samy Bengio (NeurIPS, 2021)

Soft Calibration Objectives for Neural Networks. - Archit Karandikar; Nicholas Cain; Dustin Tran; Balaji Lakshminarayanan; Jonathon Shlens; Michael C. Mozer; Becca Roelofs (NeurIPS, 2021)

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. - Nan Rosemary Ke; Aniket Didolkar; Sarthak Mittal; Anirudh Goyal; Guillaume Lajoie; Stefan Bauer; Danilo Jimenez Rezende; Michael Mozer; Yoshua Bengio; Chris Pal (NeurIPS Datasets and Benchmarks, 2021)

Discrete-Valued Neural Communication. - Dianbo Liu; Alex Lamb; Kenji Kawaguchi; Anirudh Goyal; Chen Sun 007; Michael C. Mozer; Yoshua Bengio (NeurIPS, 2021)

Compositional Embeddings for Multi-Label One-Shot Learning. - Zeqian Li; Michael Mozer; Jacob Whitehill (WACV, 2021)

Training cascaded networks for speeded decisions using a temporal-difference loss. - Michael L. Iuzzolino; Michael C. Mozer; Samy Bengio (CoRR, 2021)

Learning Neural Causal Models with Active Interventions. - Nino Scherrer; Olexa Bilaniuk; Yashas Annadani; Anirudh Goyal; Patrick Schwab; Bernhard Schölkopf; Michael C. Mozer; Yoshua Bengio; Stefan Bauer; Nan Rosemary Ke (CoRR, 2021)

Online Unsupervised Learning of Visual Representations and Categories. - Mengye Ren; Tyler R. Scott; Michael L. Iuzzolino; Michael C. Mozer; Richard S. Zemel (CoRR, 2021)

Predicting the Ease of Human Category Learning Using Radial Basis Function Networks. - Brett D. Roads; Michael C. Mozer (Neural Comput., 2021)

Mitigating Bias in Calibration Error Estimation. - Rebecca Roelofs; Nicholas Cain; Jonathon Shlens; Michael C. Mozer (AISTATS, 2022)

Coordination Among Neural Modules Through a Shared Global Workspace. - Anirudh Goyal; Aniket Rajiv Didolkar; Alex Lamb; Kartikeya Badola; Nan Rosemary Ke; Nasim Rahaman; Jonathan Binas; Charles Blundell; Michael Curtis Mozer; Yoshua Bengio (ICLR, 2022)

Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. - Utku Evci; Vincent Dumoulin; Hugo Larochelle; Michael C. Mozer (ICML, 2022)

SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos. - Gamaleldin F. Elsayed; Aravindh Mahendran; Sjoerd van Steenkiste; Klaus Greff; Michael C. Mozer; Thomas Kipf (NeurIPS, 2022)

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization. - Dianbo Liu; Alex Lamb; Xu Ji; Pascal Notsawo; Michael Mozer; Yoshua Bengio; Kenji Kawaguchi (CoRR, 2022)

Overcoming Temptation: Incentive Design For Intertemporal Choice. - Shruthi Sukumar; Adrian F. Ward; Camden Elliott-Williams; Shabnam Hakimi; Michael C. Mozer (CoRR, 2022)

Learning to Induce Causal Structure. - Nan Rosemary Ke; Silvia Chiappa; Jane Wang 001; Jörg Bornschein; Theophane Weber; Anirudh Goyal; Matthew M. Botvinick; Michael Mozer; Danilo Jimenez Rezende (CoRR, 2022)

Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel. - Dianbo Liu; Vedant Shah; Oussama Boussif; Cristian Meo; Anirudh Goyal; Tianmin Shu; Michael Mozer; Nicolas Heess; Yoshua Bengio (CoRR, 2022)

An Empirical Study on Clustering Pretrained Embeddings: Is Deep Strictly Better? - Tyler R. Scott; Ting Liu 005; Michael C. Mozer; Andrew C. Gallagher (CoRR, 2022)

Layer-Stack Temperature Scaling. - Amr Khalifa; Michael C. Mozer; Hanie Sedghi; Behnam Neyshabur; Ibrahim Alabdulmohsin (CoRR, 2022)

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness. - Dianbo Liu; Alex Lamb; Xu Ji; Pascal Tikeng Notsawo Jr.; Michael Mozer; Yoshua Bengio; Kenji Kawaguchi (AAAI, 2023)

Multitask Learning Via Interleaving: A Neural Network Investigation. - David Mayo; Tyler R. Scott; Mengye Ren; Gamaledin Elsayed; Katherine L. Hermann; Matt Jones 001; Michael Mozer (CogSci, 2023)

Learning in temporally structured environments. - Matt Jones 001; Tyler R. Scott; Mengye Ren; Gamaleldin Fathy Elsayed; Katherine L. Hermann; David Mayo; Michael Curtis Mozer (ICLR, 2023)

Learning to Induce Causal Structure. - 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 (ICLR, 2023)

Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. - Dianbo Liu; Vedant Shah; Oussama Boussif; Cristian Meo; Anirudh Goyal; Tianmin Shu; Michael Curtis Mozer; Nicolas Heess; Yoshua Bengio (ICLR, 2023)

Can Neural Network Memorization Be Localized? - Pratyush Maini; Michael Curtis Mozer; Hanie Sedghi; Zachary Chase Lipton; J. Zico Kolter; Chiyuan Zhang (ICML, 2023)

Discrete Key-Value Bottleneck. - Frederik Träuble; Anirudh Goyal; Nasim Rahaman; Michael Curtis Mozer; Kenji Kawaguchi; Yoshua Bengio; Bernhard Schölkopf (ICML, 2023)

Leveraging the Third Dimension in Contrastive Learning. - Sumukh K. Aithal; Anirudh Goyal; Alex Lamb; Yoshua Bengio; Michael Mozer (CoRR, 2023)

DiscoGen: Learning to Discover Gene Regulatory Networks. - 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; et al. (CoRR, 2023)

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior. - Ayush Chakravarthy; Bailey Trang Nguyen; Anirudh Goyal; Yoshua Bengio; Michael C. Mozer (CoRR, 2023)

Unlearning via Sparse Representations. - Vedant Shah; Frederik Träuble; Ashish Malik; Hugo Larochelle; Michael Mozer; Sanjeev Arora; Yoshua Bengio; Anirudh Goyal (CoRR, 2023)

Neural Causal Structure Discovery from Interventions. - Nan Rosemary Ke; Olexa Bilaniuk; Anirudh Goyal; Stefan Bauer; Hugo Larochelle; Bernhard Schölkopf; Michael Curtis Mozer; Christopher Pal; Yoshua Bengio (Trans. Mach. Learn. Res., 2023)

Human-like Learning in Temporally Structured Environments. - Matt Jones 001; Tyler R. Scott; Michael C. Mozer (AAAI Spring Symposia, 2024)

On the Foundations of Shortcut Learning. - Katherine L. Hermann; Hossein Mobahi; Thomas Fel; Michael Curtis Mozer (ICLR, 2024)

Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. - Aniket Didolkar; Anirudh Goyal; Nan Rosemary Ke; Siyuan Guo; Michal Valko; Timothy P. Lillicrap; Danilo Jimenez Rezende; Yoshua Bengio; Michael C. Mozer; Sanjeev Arora (NeurIPS, 2024)

Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery. - Anand Gopalakrishnan; Aleksandar Stanic; Jürgen Schmidhuber; Michael C. Mozer (NeurIPS, 2024)

Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training. - Yanlai Yang; Matt Jones 001; Michael C. Mozer; Mengye Ren (NeurIPS, 2024)

Can AI Be as Creative as Humans? - Haonan Wang; James Zou 001; Michael Mozer; Anirudh Goyal; Alex Lamb; Linjun Zhang; Weijie J. Su; Zhun Deng; Michael Qizhe Xie; Hannah Brown; Kenji Kawaguchi (CoRR, 2024)

AI-Assisted Generation of Difficult Math Questions. - Vedant Shah; Dingli Yu; Kaifeng Lyu; Simon Park 002; Nan Rosemary Ke; Michael Mozer; Yoshua Bengio; Sanjeev Arora; Anirudh Goyal (CoRR, 2024)

Zero-Shot Object-Centric Representation Learning. - Aniket Didolkar; Andrii Zadaianchuk; Anirudh Goyal; Michael C. Mozer; Yoshua Bengio; Georg Martius; Maximilian Seitzer (CoRR, 2024)

Aligning Machine and Human Visual Representations across Abstraction Levels. - Lukas Muttenthaler; Klaus Greff; Frieda Born; Bernhard Spitzer; Simon Kornblith; Michael C. Mozer; Klaus-Robert Müller; Thomas Unterthiner; Andrew K. Lampinen (CoRR, 2024)

Racing Thoughts: Explaining Large Language Model Contextualization Errors. - Michael A. Lepori; Michael Mozer; Asma Ghandeharioun (CoRR, 2024)

Improving Discrete Optimisation Via Decoupled Straight-Through Gumbel-Softmax. - Rushi Shah; Mingyuan Yan; Michael Curtis Mozer; Dianbo Liu (CoRR, 2024)

Can foundation models actively gather information in interactive environments to test hypotheses? - Nan Rosemary Ke; Danny P. Sawyer; Hubert Soyer; Martin Engelcke; David P. Reichert; Drew A. Hudson; John Reid; Alexander Lerchner; Danilo Jimenez Rezende; Timothy P. Lillicrap; Michael Mozer; Jane X. Wang (CoRR, 2024)

On the Transfer of Object-Centric Representation Learning. - Aniket Rajiv Didolkar; Andrii Zadaianchuk; Anirudh Goyal; Michael Curtis Mozer; Yoshua Bengio; Georg Martius; Maximilian Seitzer (ICLR, 2025)

Racing Thoughts: Explaining Contextualization Errors in Large Language Models. - Michael A. Lepori; Michael Curtis Mozer; Asma Ghandeharioun (NAACL (Long Papers), 2025)

Redirection for Erasing Memory (REM): Towards a universal unlearning method for corrupted data. - Stefan Schoepf; Michael Curtis Mozer; Nicole Mitchell; Alexandra Brintrup; Georgios Kaissis; Peter Kairouz; Eleni Triantafillou (CoRR, 2025)

From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization. - Shoaib Ahmed Siddiqui; Adrian Weller; David Krueger 001; Gintare Karolina Dziugaite; Michael Curtis Mozer; Eleni Triantafillou (CoRR, 2025)