Selected Publications
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2024
- Didolkar, A., Goyal, A., Ke, N. R., Guo, S., Valko, M., Lillicrap, T., Rezende, D., Bengio, Y., Mozer, M. C., & Arora, S. (2024). Metacognitive capabilities of LLMs: An exploration in mathematical problem solving. Neural Information Processing Systems. Also arXiv.org:2405.12205 [cs.AI]; ICML 2024 Workshop on AI4MATH.
- Didolkar, A., Zadaianchuk, A., Goyal, A., Mozer, M. C., Bengio, Y., Martius, G., & Seitzer, M. (2024) Zero-shot object-centric learning. arXiv.org:2408.09162 [cs.CV]
- Gopalakrishnan, A., Stanić, A., Schmidhuber, J., & Mozer, M. C. (2024). Recurrent complex-weighted autoencoders for unsupervised object discovery. Neural Information Processing Systems. (Also arXiv.org:2405.17283 [cs.LG])
- Hermann, K. L., Mobahi, H., Fel, T., & Mozer, M. C. (2024). On the foundations of shortcut learning. International Conference on Learning Representations (ICLR). Also arXiv.org:2310.16228 [cs.LG]
- Ke, N. R., Sawyer, D., Soyer, H., Engelcke, M., Reichert, D. Hudson, D., Reid, J., Lerchner, A., Rezende, D., Lilicrap, T., Mozer, M., & Wang, J. (2024). Can foundation models actively gather information in interactive environments to test hypotheses? arXiv.org:2412.06438 [cs.LG]
- Lepori, M. A., Mozer, M. C., & Ghandeharioun, A. (2024). Racing thoughts: Explaing large language model contextualization errors. arXiv.org:2410.02102 [cs.CL]
- Muttenthaler, L., Greff, K., Born, F., Spitzer, B., Kornblith, S., Mozer, M. C., Mueller, K.-R., Unterthiner, T., & Lampinen, A. K. (2024). Aligning machine and human visual representations across abstraction levels. arXiv.org:2409.06509 [cs.CV]
- Shah, R., Yan, M., Mozer, M. C., & Liu, D. (2024). Improving discrete optimization via decoupled straight-through Gumbel-softmax. arXiv.org:2410.13331 [cs.LG]
- Shah, V., Goyal, A., Yu,D., Lyu, K., Park, S., Ke, N. R., McClelland, J. L., Bengio, Y., Arora, S., Mozer, M. C. (2024). AI-assisted generation of difficult math questions. In ICML AI4MATH Workshop.
- Wang, H., Zou, J., Mozer, M., Zhang, L., Goyal, A., Lamb, A., Deng, Z., Xie, M. Q., Brown, H., & Kawaguchi, K. (2024). Can AI be as creative as humans? arXiv.org:2401.01623 [cs.AI]
- Yang, Y., Jones, M., Mozer, M. C., & Ren, M. (2024). Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training. Neural Information Processing Systems. Also arXiv.org:2403.09613 [cs.LG]
2023
- Aithal, S. K., Goyal, A., Lamb, A., Bengio, Y., & Mozer, M. C. (2023). Leveraging the third dimension in contrastive learning. arXiv.org:2301.11790 [cs.CV]. Also NeurIPS 2022 Workshop on Self-Supervised Learning: Theory and Practice.
- Chakravarthy, A., Nguyen, T., Goyal, A., Bengio, Y., & Mozer, M. C. (2023). Spotlight attention: Robust object-centric learning with a spatial locality prior. arXiv.org:2305.19550 [cs.CV]
- Jones, M., Scott, T. R., Ren, M. Elsayed, G. F., Hermann, K., Mayo, D., & Mozer, M. C. (2023). Learning in temporally structured environments. Proceedings of the International Conference on Learning Representations (ICLR 2023).
- Ke, N. R, Chiappa, S., Wang, J., Bornschein, J., Weber, T., Goyal, A., Botvinick, M., Mozer, M. C., Rezende, D. J. (2023). Learning to induce causal structure. Proceedings of the International Conference on Learning Representations (ICLR 2023). Also arXiv.org:2204.04875 [stat.ML]
- Ke, N. R., Bilaniuk, O., Goyal, A., Bauer, S., Larochelle, H., Schoelkopf, B., Mozer, M. C., Pal, C. Bengio, Y. (2023). Neural causal structure discovery from interventions. Transactions on Machine Learning Research.
- Ke, N. R., Dunn, S.-J., Bornschein, J., Chiappa, S., Rey, M., Lespiau, J.-B., Cassirer, A., Wang, J. Weber, T., Barrett, D., Botvinick, M., Goyal, A., Mozer, M. C., & Rezende, D. (2023). DiscoGen: Learning to discover gene regulatory networks. arXiv.org:2304.05823 [q-bio.MN]
- Liu, D., Lamb, A., Ji, X., Notsawo, P., Mozer, M. C., Bengio, Y., Kawaguchi, K. (2023). Adaptive discrete communication bottlenecks with dynamic vector quantization for heterogeneous representational coarseness. In Thirthy-Seventh AAAI Conference on Artificial Intelligence. Also arXiv.org:2202.01334 [cs.LG]
- Liu, D., Shah, V., Boussif, O., Meo, C., Goyal, A., Shu, T., Mozer, M. C., Heess, N., & Bengio, Y. (2023). Stateful active facilitator: Coordination and environmental heterogeneity in cooperative multi-agent reinforcement learning. Proceedings of the International Conference on Learning Representations (ICLR 2023). Also arXiv.org:2210.03022 [cs.AI]
- Maini, P., Mozer, M. C., Sedghi, H., Lipton, Z. C., Kolter, J. Z., Zhang, C. (2023). Can neural network memorization be localized? Proceedings of the International Conference on Learning Representations (ICLR 2023).
- Mayo, D., Scott, T. R., Ren, M., Elsayed, G., Hermann, K., Jones, M., & Mozer, M. C. (2023). Multitask learning via interleaving: A neural network investigation. In Proceedings of the 45th Annual Conference of the Cognitive Science Society (COGSCI 2023).
- Shah, V., Träuble, F., Malik, A., Larochelle, H., Mozer, M., Arora, S., Bengio, Y., & Goyal, A. (2023). Unlearning via sparse representations. arXiv.org:2311.15268 [cs.LG]
- Träuble, F., Goyal, A., Rahaman, N., Mozer, M. C., Kawaguchi, K., Bengio, Y., & Schölkopf, B. (2023). Discrete key-value bottleneck. In Proceedings of the 40th International Conference on Machine Learning. arXiv.org:2207.11240 [cs.LG]
- Veerabadran, V., Goldman, J., Shankar, S., Cheung, B., Papernot, N., Kurakin, A., Goodfellow, I., Shlens, J., Sohl-Dickstein, J., Mozer, M. C., & Elsayed , G. F. (2023). Subtle adversarial image manipulations influence both human and machine perception. Nature Communications, 14, 4933. https://doi.org/10.1038/s41467-023-40499-0
- Wu, Y.-F., Greff, K., Elsayed, G., Mozer, M., Kipf, T., van Steenkiste S. (2023). Inverted-attention transformers can learn object representations: Insights from slot attention. NeurIPS Workshop on Causal Representation Learning.
2022
- Elsayed, G. F., Mahendran, A., van Steenkiste, S., Greff, K., Mozer, M. C., & Kipf, T. (2022). SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos. Advances in Neural Information Processing Systems 35. Also arXiv.org:2206.07764 [cs.CV]
- Evci, U., Dumoulin, V., Larochelle, H., & Mozer, M. C. (2022). Head2Toe: Utilizing intermediate representations for better transfer learning. Proceedings of the 39th International Conference on Machine Learning. Also arXiv.org:2201.03529 [cs.LG]
- Goyal, A., Didolkar, A., Lamb, A., Badola, K., Ke, N. R., Rahaman, N., Binas, J., Blundell, C., Mozer, M. C., & Bengio, Y. (2022). Coordination among neural modules through a shared global workspace. International Conference on Learning Representations (ICLR). Also arXiv:2103.01197 [cs.Lg]
- Jones, M., Mayo, D., Scott, T., Ren, M., ElSayed, G., Hermann, K, & Mozer, M. C. (2022). Neural network online training with sensitivity to multiscale temporal structure. Proceedings of the NeurIPS 2022 Workshop on Memory in Artificial and Real Intelligence.
- Khalifa, A., Mozer, M. C., Sedghi, H., Neyshabur, B., & Alabdulmohsin, I. (2022). Layer-stack temperature scaling. arXiv.org:2211.10193 [cs.Lg]
- Roelofs, R., Cain, N., Shlens, J., & Mozer, M. C. (2022). Mitigating bias in calibration error estimation. Twenty Fifth International Conference on Artificial Intelligence and Statistics (AIStats). Also arXiv:2012.08668 [cs.LG]
- Scott, T. R., Liu, T., Mozer, M. C., & Gallagher, A. C. (2022). An empirical study on clustering pretrained embeddings: Is deep strictly better? In Proceedings of the NeurIPS ICBINB Workshop. Also arXiv:221105183 [cs.CV]
- Sukumar, S., Ward, A. F., Elliott-Williams, C., Hakimi, S., Mozer, M. C. (2022). Overcoming temptation: Incentive design for intertemporal choice. arXiv.org:2203.05782 [cs.LG / q-bio.NC]
2021
- Didolkar, A., Goyal, A., Ke, N. R., Blundell, C., Beaudoin, P. Heess, N., Mozer, M. C., & Bengio, Y. (2021). Neural production systems. In Advances in Neural Information Processing Systems 34. Also arXiv:2103.01937 [cs.AI]
- Goyal, A., Lamb, A., Gampa, P., Beaudoin, P., Levine, S., Blundell, C., Bengio, Y., & Mozer, M. C., (2021). Object files and schemata: Factorizing declarative and procedural knowledge in dynamical systems. International Conference on Learning Representations. Also arXiv:2006.16225 [cs.LG]
- Iuzzolino, M. L., Mozer, M. C., & Bengio, S. (2021). Improving anytime prediction with parallel cascaded networks and a temporal-difference loss. In Advances in Neural Information Processing Systems 34. Also arXiv:2102.09808 [cs.LG]
[code repository] - Jiang, Z., Zhang, C., Talwar, K., & Mozer, M. C. (2021). Characterizing structural regularities of labeled data in overparameterized models. Proceedings of the 38th International Conference on Machine Learning, PMLR 139:5034-5044. Also arXiv:2002.03206 [cs.LG].
[code, images, results] - Karandikar, A., Cain, N., Tran, D., Lakshminarayanan, B., Shlens, J., Mozer, M. C., & Roelofs, B. (2021). Soft calibration objectives for neural networks. In Advances in Neural Information Processing Systems 34. Also arXiv.org:2018.00106 [cs.LG]
- Kim, B., Reif, E., Wattenberg, M., Bengio, S., & Mozer, M. C. (2021). Neural networks trained on natural scene exhibit Gestalt closure. Computational Brain and Behavior, 4(3), 251-263. https://doi.org/10.1007/s42113-021-00100-7
- Kim, D. Y. J., Scott, T. R., Mallick, D., & Mozer, M. C. (2021). Using semantics of textbook highlights to predict student comprehension and knowledge retention. In S. Sosnovsky, P. Brusilovsky, R. G. Baraniuk, & A. S. Lan (Eds.), Proceedings of the Third International Workshop on Intelligent Textbooks (iTextbooks) (pp. 108--120). Springer.
- Lamb, A., Goyal, A., Stowik, A., Mozer, M. C., Beaudoin, P., & Bengio, Y. (2021). Neural function modules with sparse arguments: A dynamic approach to integrating information across layers. AISTATS 2021. Also arXiv:2010.08012 [cs.LG]
- Li, Z., Mozer, M. C., & Whitehill, J. (2021). Compositional embeddings for multi-label one-shot learning. IEEE Winter Conference on Applications of Computer Vision. Also arXiv:2002.04193 [cs.LG]
- Liu, D., Lamb, A., Kawaguchi, K., Goyal, A., Sun, C., Mozer, M. C., & Bengio, Y. (2021). Discrete-valued neural communication in structured architectures enhances generalization.. In Advances in Neural Information Processing Systems 34. Also arXiv.org:2107.02367 [cs.LG]
- Mozer, F. S., Bale, S. D., Bonnell, J. W., Drake, J. F., Hanson, E. L. M., & Mozer, M. C. (2021). On the origin of switchbacks observed in the solar wind. Journal of Astrophysics, 919:60, 1--10.
- Ren, M., Iuzzolino, M. L., Mozer, M. C., & Zemel, R. S. (2021). Wandering within a world: Online contextualized few-shot learning. International Conference on Learning Representations. Also arXiv:2007.04546 [cs.LG]
- Ren, M., Scott, T. R., Iuzzolino, M. L., Mozer, M. C., & Zemel, R. S. (2021). Online unsupervised learning of visual representations and categories. arXiv.org:2109.05675 [cs.LG]
- Roads, B. D., & Mozer, M. C. (2021). Predicting the ease of human category learning using radial basis function networks. Neural Computation, 33, 376-397.
- Scherrer, N., Bilaniuk, O., Annadani, Y., Goyal, A., Schwab, P. Schoelkopf, B., Mozer, M. C., Bengio, Y., & Ke, N. R. (2021). Learning neural causal models with active interventions. NeurIPS Workshop on Causal Inference and Machine Learning (WHY-21). Also arXiv.org:2019.02429 [stat.ML]
- Scott, T. R., Gallagher, A. C., & Mozer, M. C. (2021). Von Mises-Fisher loss: An exploration of embedding geometries for supervised learning. Proceedings of the IEEE/CVF International Conference on Computer Vision. Also arXiv:2103.15718 [cs.LG]
- Teterwak, P., Zhang, C., Krishnan, D., & Mozer, M. C. (2021). Understanding invariance via feedforward inversion of discriminatively trained classifiers. Proceedings of the 38th International Conference on Machine Learning, PMLR 139:10225-10235. Also arXiv: 2103.07470 [cs.LG]
[code: robust model] [code: non-robust model]
2020
- Attarian, M., Roads, B. D., & Mozer, M. C. (2020). Transforming neural network representations to predict human judgments of similarity. Workshop on Shared Visual Representations in Human and Machine Intelligence (SVRHM 2020). Also arXiv:2010.06512 [cs.NE]
- Beckage, N., Colunga, E., & Mozer, M. C. (2020). Quantifying the role of vocabulary knowledge in predicting future word learning. IEEE Transactions on Cognitive and Developmental Systems, 12, 148-159. DOI:10.1109/TCDS.2019.2928023
- Davidson, G., and Mozer, M. C. (2020). Sequential mastery of multiple tasks: Networks naturally learn to learn and forget to forget. IEEE Conference on Computer Vision and Pattern Recognition, 9282-9293. Also arXiv:1905.10837 [cs.LG]
- Kim, D. Y. J, Winchell, A., Waters, A. E., Grimaldi, P. J., Baraniuk, R., & Mozer, M. C. (2020). Inferring student comprehension from highlighting patterns in digital textbooks: An exploration in an authentic learning platform. In S. Sosnovsky, P. Brusilovsky, R. G. Baraniuk, & A. S. Lan (Eds.), Second Workshop on Intelligent Textbooks, Springer.
- Mittal, S., Lamb, A., Goyal, A., Voleti, V., Shanahan, M., Lajoie, G., Mozer, M. C., & Bengio, Y. (2020). Learning to combine top-down and bottom-up signals in recurrent neural networks with attention over modules. International Conference on Machine Learning.
- Winchell, A., Lan, A., and Mozer, M. C. (2020). Textbook highlights as an early predictor of student learning. Cognitive Science: A Multidisciplinary Journal. 44: e12901. doi:10.1111/cogs.12901
- Zhang, C., Bengio, S., Hardt, M., Mozer, M. C., and Singer, Y. (2020). Identity crisis: Memorization and generalization under extreme overparameterization. In International Conference on Learning Representations (ICLR 2020). Also arXiv:1902.04698 [stat.ML]
2019
- Iuzzolino, M., Singer, Y., and Mozer, M. C. (2019). Convolutional bipartite attractor networks. ArXiv:1906.03504 [cs.LG]
- Lamb, A., Binas, J., Goyal, A., Subramanian, S., Mitliagkas, I., Kazakov, D., Bengio, Y., & Mozer, M. C. (2019). State-reification networks: Improving generalization by modeling the distribution of hidden representations. Proceedings of the 36th International Conference on Machine Learning, 97, 3622-3631.
- Mozer, M. C., Wiseheart, M., and Novikoff T. (2019). Artificial intelligence to support human instruction. Proceedings of the National Academy of Sciences, 116 (10), 3953-3955. doi:10.1073/pnas.1900370116
- Ridgeway, K., & Mozer, M. C. (2019). Open-ended content-style recombination via leakage filtering. arXiv.org:1810.00110v1 [cs.LG]
- Roads, B. D., & Mozer, M. C. (2019). Obtaining psychological embeddings through joint kernel and metric learning. Behavioral Research Methods, 51, 2180-2193. doi:10.3758/s13428-019-01285-3.
- Scott, T. R., Ridgeway, K., and Mozer, M. C. (2019). Stochastic prototype embeddings. ArXiv:1909.11702 [stat.ML]
- Sense, F., Jastrzembski, T., Mozer, M. C., Krusmark, M., and van Rijn, H. (2019). Perspectives on computational models of learning and forgetting. Proceedings of the Seventeenth International Conference on Cognitive Modeling (53-58). State College, PA: Applied Cognitive Science Lab.
2018
- Ke, N. R., Goyal, A., Bilaniuk, O., Binas, J., Mozer, M. C., Pal, C., & Bengio, Y. (2018). Sparse attentive backtracking: Temporal credit assignment through reminding. In S. Bengio et al. (Eds.), Advances in Neural Information Processing Systems 31 (pp. 7651-7662). Curran Associates.
- Khajah, M. M., Mozer, M. C., Kelly, S., & Milne, B. (2018). Boosting engagement with educational software using near wins. In C. Rosé et al. (Eds.), Nineteenth International Conference on Artificial Intelligence in Education (pp. 171-175). Springer.
[short version] - Lindsey, R., Daluski, A., Chopra, S., Lachapelle, A., Mozer, M., Sicular, S., Hanel, D., Gardner, M., Gupta, A., Hotchkiss, R., & Potter, H. (2018). A deep neural network improves fracture dectection by clinicians. Proceedings of the National Academy of Sciences, 115, 11591-11596. DOI: 10.1073/pnas.1806905115.
- Montero, S., Arora, A., Kelly, S., Milne, B., & Mozer, M. C. (2018). Does deep knowledge tracing model interactions among skills? In K. E. Boyer & M. Yudelson (Eds.), Proceedings of the 11th International Conference on Educational Data Mining (pp. 462-466). EDM Society Press.
- Mozer, M. C., Kazakov, D., & Lindsey, R. V. (2018). State denoised recurrent neural networks. arXiv:1805.08394 [cs.NE]
- Ridgeway, K., & Mozer, M. C. (2018). Learning deep disentangled representations with the F-statistic loss. In S. Bengio et al. (Eds.), Advances in Neural Information Processing Systems 31 (pp. 185-194). Curran Associates. Also as arXiv:1802.05312v2 [cs.LG]
- Scott, T. R., Ridgeway, K., & Mozer, M. C. (2018). Adapted deep embeddings: A synthesis of methods for k-shot inductive transfer learning. In S. Bengio et al. (Eds.), Advances in Neural Information Processing Systems 31 (pp. 76-85). Curran Associates. Also arXiv:1805.08402 [cs.LG]
- Vatterott, D. B., Mozer, M. C., & Vecera, S. P. (2018). Rejecting salient distractors: Generalization from experience. Attention, Perception, and Psychophysics, 80, 485-499. DOI:10.3758/s13414-017-1465-8
- Winchell, A., Mozer, M. C., Lan, A., Grimaldi, P., & Pashler, H. (2018). Can textbook annotations serve as an early predictor of student learning? In K. E. Boyer & M. Yudelson (Eds.), Proceedings of the 11th International Conference on Educational Data Mining (pp. 431-437). EDM Society Press.
2017
- Kneusel, R. T., & Mozer, M. C. (2017). Improving human-machine cooperative visual search with soft highlighting. ACM Transactions on Applied Perception, 15, 3:1-3:21. Also arXiv:1612.08117 [cs.HC]
- Mozer, M. C., & Lindsey, R. V. (2017). Predicting and improving memory retention: Psychological theory matters in the big data era. In M. Jones (Ed.), Big Data in Cognitive Science (pp. 34-64). New York: Routledge.
- Mozer, M. C., Kazakov, D., & Lindsey, R. V. (2017). Discrete-event continuous-time recurrent networks. arXiv:1710.04110 [cs.NE].
- Ridgeway, K., Mozer, M. C., & Bowles, A. (2017). Forgetting of foreign language skills: A corpus-based analysis of online tutoring software. Cognitive Science: A Multidisciplinary Journal, 41(4), 924-949. DOI: 10.1111/cogs.12385
- Roads, B. D., & Mozer, M. C. (2017). Improving human-machine cooperative classification via cognitive theories of similarity. Cognitive Science: A Multidisciplinary Journal, 41, 1394-1411. DOI: 10.1111/cogs.12400.
[slides from NIPS 2016 Interactive Machine Learning workshop] - Snell, J., Ridgeway, K., Liao, R., Roads, B. D., Mozer, M. C., & Zemel, R. S. (2017). Learning to generate images with perceptual similarity metrics. IEEE International Conference on Image Processing. arXiv:1511.06409v3 [cs.LG]
2016
- Khajah, M., Lindsey, R. V., & Mozer, M. C. (2016). How deep is knowledge tracing? In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the Ninth International Conference on Educational Data Mining (pp. 94-101). Educational Data Mining Society Press.
*Awarded Best Paper at EDM2016*
[our code for extended BKT used in the paper] [our implementation of DKT] - Khajah, M., Roads, B. D., Lindsey, R. V., Liu, Y.-E., & Mozer, M. C. (2016). Designing engaging games using Bayesian optimization. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 5571-5582). New York: ACM.
- Mozer, M. C., Lindsey, R. V., & Kazakov, D. (2016). Neural Hawkes process memories. Paper in preparation.
[Slides from NIPS 2016 Symposium on Recurrent Neural Networks] - Roads, B. D., Mozer, M. C., & Busey, T. A. (2016). Using highlighting to train attentional expertise. PLoS ONE 11(1): e0146266. doi:10.1371/journal.pone.0146266
- Wilson, K.H., Xiong, X., Khajah, M., Lindsey, R.V., Zhao, S., Karklin, Y., Van Inwegen, E.G., Han, B., Ekanadham, C., Beck, J.E., Heffernan, N., & Mozer, M.C. (2016). Estimating student proficiency: Deep learning is not the panacea. In R. G. Baraniak, J. Ngiam, C. Studer, P. Grimaldi, & A. S. Lan (Eds.), Proceedings of the 2016 NIPS Workshop on Machine Learning for Education.
[slides from NIPS 2016 Machine Learning for Education workshop]
2015
2014
- Kang, S. H. K., Lindsey, R. V., Mozer, M. C., & Pashler, H. (2014). Retrieval practice over the long term: Should spacing be expanding or equal-interval? Psychonomic Bulletin & Review, 21, 1544-50.
- Khajah, M., Huang, Y., Gonzales-Brenes, J. P., Mozer, M. C., & Brusilovsky, P. (2014). Integrating knowledge tracing and item response theory: A tale of two frameworks. In M. Kravcik, O. C. Santos, J. G. Boticario (Eds.), Proceedings of the 4th International Workshop on Personalization Approaches in Learning Environments (pp. 7-15). CEUR Workshop Proceedings, ISSN 1613-0073.
- Khajah, M., Lindsey, R., & Mozer, M. C. (2014). Maximizing students' retention via spaced review: Practical guidance from computational models of memory. Topics in Cognitive Science, 6, 157-169.
*Awarded Cognitive Modeling Prize at CogSci2013* - Khajah, M., Wing, R. M., Lindsey, R. V., & Mozer, M. C. (2014) Incorporating latent factors into knowledge tracing to predict individual differences in learning. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds), Proceedings of the 7th International Conference on Educational Data Mining (pp. 99-106). Educational Data Mining Society Press.
*Awarded Best Paper at EDM2014* - Lindsey, R. V., & Mozer, M. C. (2014). Predicting individual differences in student learning via collaborative filtering.
- Lindsey, R. V., Khajah, M., & Mozer, M. C. (2014). Automatic discovery of cognitive skills to improve the prediction of student learning. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27 (pp. 1386-1394). La Jolla, CA: Curran Associates Inc.
[our code for BKT with skill assignments] - Lindsey, R. V., Shroyer, J. D., Pashler, H., & Mozer, M. C. (2014). Improving student's long-term knowledge retention with personalized review. Psychological Science, 25, 639-647. doi: 10.1177/0956797613504302.
2013
- Chukoskie, L., Snider, J., Mozer, M. C., Krauzlis, R. J., & Sejnowski, T. J. (2013). Learning where to look: An empirical, computational, and theoretical account of hidden target search performance. Proceedings of the National Academy of Sciences, 110, 10438-445.
- Jones, M., Curran, T., Mozer, M. C., & Wilder, M. H. (2013). Sequential effects in response time reveal learning mechanisms and event representations. Psychological Review, 120, 628-666.
- Lindsey, R., Mozer, M. C., Huggins, W. J., & Pashler, H. (2013). Optimizing instructional policies. In C.J.C. Burges et al. (Eds.), Advances in Neural Information Processing Systems 26 (pp.2778-2786). La Jolla, CA: Curran Associates, Inc.
- Pashler, H., & Mozer, M. C. (2013). When does fading enhance perceptual category learning? Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 1162-73.
- Pashler, H., Kang, S., & Mozer, M. C. (2013). Reviewing erroneous information facilitates memory updating. Cognition, 128(3), 424-430.
- Wilder, M. H., Jones, M., Ahmed, A., Curran, T., & Mozer, M. C. (2013). The persistent impact of incidental experience. Psychonomic Bulletin and Review, 20, 1221-1231.
2012
- Doshi, A., Tran, C., Wilder, M., Mozer, M. C., & Trivedi, M. M. (2012). Sequential dependencies in driving. Cognitive Science, 36, 948-963.
- Lee, H., Mozer, M. C., Kramer, A. F., & Vecera, S. P. (2012). Object-based control of attention is sensitive to recent experience. Journal of Experimental Psychology: Human Perception and Performance, 38, 314-325.
- Lindsey, R., Polsdofer, E., Mozer, M.C., Kang, S., H., K., & Pashler, H. (2012). Long-term recency is nothing more than ordinary forgetting. Unpublished manuscript.
- Mozer, M. C., Pashler, H., Lindsey, R. V., & Jones, J. (2012). Efficient training of visual search via attentional highlighting. Unpublished manuscript.
2011
- Kang, S. H. K., Pashler, H., Cepeda, N. J., Rohrer, D., Carpenter, S. K., & Mozer, M. C. (2011). Does incorrect guessing impair fact learning? Journal of Educational Psychology, 103, 48-59.
- Kinoshita, S., Mozer, M. C., & Forster, K. I. (2011). Dynamic adaptation to history of trial difficulty explains the effect of congruency proportion on masked priming. Journal of Experimental Psychology: General. 140, 622-636.
- Link, B. V., Kos, B., Wager, T. D., & Mozer, M. C. (2011). Past experience influences judgment of pain: Prediction of sequential dependencies. In L. Carlson, C. Hoelscher, & T. F. Shipley (Eds.), Proceedings of the 33d Annual Conference of the Cognitive Science Society (pp. 1248-1253). Austin, TX: Cognitive Science Society.
- Mozer, M. C., Link, B. V., & Pashler, H. (2011). An unsupervised decontamination procedure for improving the reliability of human judgments. In Shawe-Taylor, J., Zemel, R. S., Bartlett, P., Pereira, & Weinberger, K. Q. (Eds.), Advances in Neural Information Processing Systems 24 (pp. 1791-1799). La Jolla, CA: NIPS Foundation.
- Wilder, M. H., Mozer, M. C., & Wickens, C. D. (2011). An integrative, experience-based theory of attentional control. Journal of Vision, 11, 1-30.
2010
- Lindsey, R., Lewis, O., Pashler, H., & Mozer, M. C. (2010). Predicting students' retention of facts from feedback during training. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2332-2337). Austin, TX: Cognitive Science Society.
- Mozer, M. C., Pashler, H., Wilder, M., Lindsey, R., Jones, M. C., & Jones, M. N. (2010). Decontaminating human judgments to remove sequential dependencies. In J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, & A. Culota (Eds.), Advances in Neural Information Processing Systems 23 (pp. 1705-1713). La Jolla, CA: NIPS Foundation.
- Wilder, M. H., Ahmed, A. A., Mozer, M. C., & Jones, M. (2010). Sequential effects in motor adaptation: The importance of far back trials. Poster presentation at Society For Neuroscience. San Diego, CA, November 15, 2010.
- Wilder, M., Jones, M., & Mozer, M. C. (2010). Sequential effects reflect parallel learning of multiple environmental regularities. In Y. Bengio, D. Schuurmans, J. Lafferty, C.K.I. Williams, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 22 (pp. 2053-2061). La Jolla, CA: NIPS Foundation.
2009
- Cepeda, N. J., Coburn, N., Rohrer, D., Wixted, J. T., Mozer, M. C., & Pashler, H. (2009). Optimizing distributed practice: Theoretical analysis and practical implications. Experimental Psychology, 56, 236-246.
- Jones, M., Mozer, M. C., & Kinoshita, S. (2009). Optimal response initiation: Why recent experience matters. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in Neural Information Processing Systems 21, 785-792.
- Knights, D., Mytkowicz, T., Sweeney, P. F., Mozer, M. C., & Diwan, A. (2009). Blind optimization for exploiting hardware features. In O. de Moor & M. I. Schwartzbach (Eds.), Lecture Notes in Computer Science, v. 5501: Compiler Construction 2009 (pp. 251-265). New York: Springer.
- Lee, H., Mozer, M. C., & Vecera, S. P. (2009). Mechanisms of priming of pop-out: Stored representations or feature-gain modulations? Attention, Perception, & Psychophysics, 71, 1059-1071.
- Lindsey, R., Mozer, M. C., Cepeda, N. J., & Pashler, H. (2009). Optimizing memory retention with cognitive models. In A. Howes, D. Peebles, & R. Cooper (Eds.), Proceedings of the Ninth International Conference on Cognitive Modeling (ICCM). Manchester, UK.
- Mozer, M. C. (2009). Attractor networks. In P. Wilken, A. Cleeremans, & T. Bayne (Eds.), Oxford Companion to Consciousness(pp. 86-89). Oxford U. Press.
- Mozer, M. C., & Wilder, M. H. (2009). A unified theory of exogenous and endogenous attentional control. In D. Heinke & E. Mavritsaki (Eds.), Computational modeling in behavioral neuroscience: Closing the gap between neurophysiology and behaviour (pp. 245-265). London: Psychology Press.
- Mozer, M. C., Pashler, H., Cepeda, N., Lindsey, R., & Vul, E. (2009). Predicting the optimal spacing of study: A multiscale context model of memory. In Y. Bengio, D. Schuurmans, J. Lafferty, C.K.I. Williams, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 22 (pp. 1321-1329). La Jolla, CA: NIPS Foundation.
- Reynolds, J., & Mozer, M. C. (2009). Temporal dynamics of cognitive control. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in Neural Information Processing Systems 21, 1353-1360.
2008
- Kinoshita, S., Forster, K. I., & Mozer, M. C. (2008). Unconscious cognition isn't that smart: Modulation of masked repetition priming effect in the word naming task. Cognition, 107, 623-649.
- Mozer, M. C., & Baldwin, D. S. (2008). Experience-guided search: A theory of attentional control. In J. Platt, D. Koller, & Y. Singer (Eds.), Advances in Neural Information Processing Systems 20 (pp. 1033-1040). Cambridge, MA: MIT Press
- Mozer, M. C., & Fan, A. (2008). Top-down modulation of neural responses in visual perception: A computational exploration. Natural Computing, 7, 45-55.
- Mozer, M. C., Pashler, H., & Homaei, H. (2008). Optimal predictions in everyday cognition: The wisdom of individuals or crowds? Cognitive Science: A Multidisciplinary Journal, 32, 1133-1147.
2007
- Bohte, S., & Mozer, M. C. (2007). A computational theory of spike-timing dependent plasticity: Achieving robust neural responses via response variability minimization. Neural Computation, 19, 371-403.
- Hochreiter, S., & Mozer, M. C. (2007). Monaural speech separation by support vector machines: Bridging the divide between supervised and unsupervised learning methods. In Conference on Blind Signal Separation.
- Mozer, M. C., Jones, M., & Shettel, M. (2007). Context effects in category learning: An investigation of four probabilistic models. Neural Information Processing Systems 19. Cambridge, MA: MIT Press.
- Mozer, M. C., Kinoshita, S., & Shettel, M. (2007). Sequential dependencies offer insight into cognitive control. In W. Gray (Ed.), Integrated Models of Cognitive Systems (pp. 180-193). Oxford University Press.
- Richardson, S., Otte, M., Mozer, M. C., Diwan, A., Sweeney, P., Connors, D., & Lacovara, K. (2007). Discovering the runtime structure of software with probabilistic generative models.
2006
- Baldwin, D., & Mozer, M. C. (2006). Controlling attention with noise: The cue-combination model of visual search. In R. Sun & N. Miyake (Eds.), Proceedings of the Twenty Eighth Annual Conference of the Cognitive Science Society (pp. 42-47). Hillsdale, NJ: Erlbaum Associates.
- Kinoshita, S., & Mozer, M. C. (2006). How lexical decision is affected by recent experience: Symmetric versus asymmetric frequency blocking effects. Memory and Cognition, 34, 726-742.
- Mozer, M. C., Shettel, M., & Vecera, S. P. (2006). Control of visual attention: A rational account. In Y. Weiss, B. Schoelkopf, & J. Platt (Eds.), Neural Information Processing Systems 18 (pp. 923-930). Cambridge, MA: MIT Press.
2005
- Bohte, S., & Mozer, M. C. (2005). Reducing spike train variability: A computational theory of spike-timing dependent plasticity. In L. K. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in Neural Information Processing Systems 17 (pp. 201-208). Cambridge, MA: MIT Press.
- Colagrosso, M. D., & Mozer, M. C. (2005). Theories of access consciousness. In L. K. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in Neural Information Processing Systems 17 (pp. 289-296). Cambridge, MA: MIT Press.
- Hauswirth, M., Diwan, A., Sweeney, P. F., & Mozer, M. C. (2005). Automated vertical profiling. In 20th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA'05).
- Mozer, M. C. (2005). Lessons from an adaptive house. In D. Cook & R. Das (Eds.), Smart environments: Technologies, protocols, and applications (pp. 273-294). Hoboken, NJ: J. Wiley & Sons.
- Mozer, M. C., & Vecera, S. P. (2005). Object- and space-based attention. In L. Itti, G. Rees, & J. Tsotsos (Eds.), The encyclopedia of the neurobiology of attention (pp. 130-134). Elsevier Press.
- Mozer, M. C., Mytkowicz, T., & Zemel, R. S. (2005). Stimulus-specific adaptation of neural responses: Insights from neurophysiology and computational models. Poster presented at the Cognitive Neuroscience Conference. New York City, April 2005.
2004
- Mozer, M. C., Howe, M., & Pashler, H. (2004). Using testing to enhance learning: A comparison of two hypotheses. Proceedings of the Twenty Sixth Annual Conference of the Cognitive Science Society (pp. 975-980). Hillsdale, NJ: Erlbaum Assoccciates.
- Mozer, M. C., Kinoshita, S., & Davis, C. (2004). Control of response initiation: Mechanisms of adaptation to recent experience. Proceedings of the Twenty Sixth Annual Conference of the Cognitive Science Society (pp. 981-986). Hillsdale, NJ: Erlbaum Assoccciates.
- Mozer, M. C., Mytkowicz, T., & Zemel, R. S. (2004). Achieving robust neural representations: An account of repetition suppression. Unpublished manuscript.
2003
- Mozer, M. C., Colagrosso, M. D., & Huber, D. E. (2003). Mechanisms of long-term repetition priming and skill refinement: A probabilistic pathway model. In Proceedings of the Twenty Fifth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum Associates.
- Yan, L., Dodier, R., Mozer, M. C., & Wolniewicz, R. (2003). Optimizing classifier performance via the Wilcoxon-Mann-Whitney statistic. In Proceedings of the International Conference on Machine Learning (ICML) (pp. 848-855).
2002
- Mozer, M. C. (2002). Frames of reference in unilateral neglect and spatial attention: A computational perspective. Psychological Review, 109, 156-185.
- Mozer, M. C., Colagrosso, M. D., & Huber, D. H. (2002). A rational analysis of cognitive control in a speeded discrimination task. In T. Dietterich, S. Becker, & Ghahramani, Z. (Eds.) Advances in Neural Information Processing Systems 11V (pp. 51-57). Cambridge, MA: MIT Press.
- Mozer, M. C., Dodier, R., Colagrosso, M. D., Guerra-Salcedo, C., & Wolniewicz, R. (2002). Prodding the ROC curve: Constrained optimization of classifier performance. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems 14 (pp. 1409-1415). Cambridge, MA: MIT Press.
- Pashler, H., Mozer, M. C., & Harris, C. R. (2002). Mating strategies in a Darwinian microworld: Simulating the consequences of female reproductive refractoriness. Adaptive Behavior, 9, 5-15.
- Zemel, R. S., Behrmann, M., Mozer, M. C., & Bavelier, D. (2002). Experience-dependent perceptual grouping and object-based attention. Journal of Experimental Psychology: Human Perception and Performance, 28, 202-217.
2001
- Grimes, D., & Mozer, M. C. (2001). The interplay of symbolic and subsymbolic processes in anagram problem solving. In T. K. Leen, T. Dietterich, & V. Tresp (Eds.), Advances in Neural Information Processing Systems 13 (pp. 17-23). Cambridge, MA: MIT Press.
- Hochreiter, S., & Mozer, M. C. (2001). A discrete probabilistic memory model for discovering dependencies in time. In G. Dorffner, H. Bischof, & K. Hornig (Eds.), Proceedings of the International Conference on Artificial Neural Networks (ICANN). Springer-Verlag.
- Hochreiter, S., & Mozer, M. C. (2001). Beyond maximum likelihood and density estimation: A sample-based criterion for unsupervised learning of complex models. In T. K. Leen, T. Dietterich, & V. Tresp (Eds.), Advances in Neural Information Processing Systems 13 (pp. 535-541). Cambridge, MA: MIT Press.
- Hochreiter, S., & Mozer, M. C. (2001). Monaural separation and classification of mixed signals: A support-vector regression perspective. Proceedings of the Third International Conference on Independent Component Analysis and Blind Signal Separation, San Diego, CA.
- Zemel, R. S., & Mozer, M. C. (2001). Localist attractor networks. Neural Computation, 13, 1045-1064.
2000
- Behrmann, M., Zemel, R. S., Mozer, M. C. (2000). Occlusion, symmetry, and object-based attention: Reply to Saiki (2000). Journal of Experimental Psychology: Human Perception and Performance, 26, 1497-1505.
- Lee, S.-Y., & Mozer, M. C. (2000). Robust recognition of noisy and superimposed patterns via selective attention. In S. A. Solla, T. K. Leen & K.-R. Mueller (Eds.), Advances in Neural Information Processing Systems 12 (pp. 31-37). Cambridge, MA: MIT Press.
- Mozer, M. C., & the Athene Advanced Technology Group. (2000). Prediction and classification. Pittfalls for the unwary.
- Mozer, M. C., Wolniewicz, R., Grimes, D., Johnson, E., & Kaushanksy, H. (2000). Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Transactions on Neural Networks, 11, 690-696.
- Sitton, M., Mozer, M. C., & Farah, M. (2000). Superadditive effects of multiple lesions in a connectionist architecture: Implications for the neuropsychology of optic aphasia, Psychological Review, 107, 709-734.
1999
- Alexander, J. A., & Mozer, M. C. (1999). Template-based procedures for neural network interpretation. Neural Networks, 12, 479-498.
- Mozer, M. C. (1999). A principle for unsupervised hierarchical decomposition of visual scenes. In M. S. Kearns, S. A. Solla, & D. Cohn (Eds.), Advances in Neural Information Processing Systems 11 (pp. 52-58). Cambridge, MA: MIT Press.
- Mozer, M. C. (1999). An intelligent environment must be adaptive. IEEE Intelligent Systems and their Applications, 14(2) , 11-13.
- O'Reilly, R. C., Mozer, M. C., Munakata, Y., & Miyake, A. (1999). Discrete representations in working memory: A hypothesis and computational investigations. In Proceedings of the Second International Conference on Cognitive Science (pp. 183-188). Tokyo, Japan: Japanese Cognitive Science Society.
1998
- Behrmann, M., Zemel, R. S., and Mozer, M. C. (1998). Object-based attention and occlusion: Evidence from normal subjects and a computational model. Journal of Experimental Psychology: Human Perception and Performance, 24, 1011-1036.
- Das, S., & Mozer, M. C. (1998). Dynamic on-line clustering and state extraction: An approach to symbolic learning. Neural Networks, 11, 53-64.
- Mozer, M. C. (1998). The neural network house: An environment that adapts to its inhabitants. In M. Coen (Ed.), Proceedings of the American Association for Artificial Intelligence Spring Symposium on Intelligent Environments (pp. 110-114). Menlo, Park, CA: AAAI Press.
- Mozer, M. C., & Miller, D. (1998). Parsing the stream of time: The value of event-based segmentation in a complex, real-world control problem. In C. L. Giles & M. Gori (Eds.), Adaptive processing of temporal sequences and data structures (pp. 370-388). Berlin: Springer Verlag.
- Mozer, M. C., & Sitton, M. (1998). Computational modeling of spatial attention. In H. Pashler (Ed.), Attention (pp. 341-393). London: UCL Press.
1997
- Calder, B., Grunwald, D., Jones, M., Lindsay, Dsdlkjlk., Martin, J., Mozer, M., & Zorn, B. (1997). Evidence-based static branch prediction using machine learning. ACM Transactions on Programming Languages and Systems, 19, 188-222.
- Mozer, M. C., Halligan, P. W., & Marshall, J. C. (1997). The end of the line for a brain-damaged model of unilateral neglect. Cognitive Neuroscience, 9, 171-190.
- Mozer, M. C., Vidmar, L., & Dodier, R. H. (1997). The Neurothermostat: Predictive optimal control of residential heating systems. In M. C. Mozer, M. I. Jordan, & T. Petsche (Eds.), Advances in Neural Information Processing Systems 9 (pp. 953-959). Cambridge, MA: MIT Press.
- Uno, Y., & Mozer, M. C. (1997). Neural net architectures in modeling compositional syntax: Prediction and perception of continuity in minimalist works by Phillip Glass and Louis Andriessen. Proceedings of the International Computer Music Conference, Greece.
1996
- Mathis, D., & Mozer, M. C. (1996). Conscious and unconscious perception: A computational theory. In G. Cottrell (Ed.), Proceedings of the Eighteenth Annual Conference of The Cognitive Science Society (pp. 324-328). Hillsdale, NJ: Erlbaum.
- Mozer, M. C. (1996). Neural network speech processing for toys and consumer electronics. IEEE Expert, 11, 4-5.
1995
- Mathis, D. A., & Mozer, M. C. (1995). On the computational utility of consciousness. In G. Tesauro, D. S. Touretzky, & T. K. Leen (Eds.), Advances in Neural Information Processing Systems 7 (pp. 10-18). Cambridge, MA: MIT Press.
- Mozer, M. C., Dodier, R. H., Anderson, M., Vidmar, L., Cruickshank III, R. F., & Miller, D. (1995). The neural network house: An overview. In L. Niklasson & M. Boden (Eds.), Current trends in connectionism (pp. 371-380). Hillsdale, NJ: Erlbaum.
- Zemel, R. S., Williams, C. K. I., & Mozer, M. C. (1995). Lending direction to neural networks. Neural Networks, 7, 565-579.
1994
- Das, S., & Mozer, M. C. (1994). A unified gradient-descent/clustering architecture for finite-state machine induction. In J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in Neural Information Processing Systems 6 (pp. 19-26). San Mateo, CA: Morgan Kaufmann Publishers.
- Dodier, R. H., Lukianow, D., Ries, J., & Mozer, M. C. (1994). A comparison of neural net and conventional techniques for lighting control. Applied Mathematics and Computer Science, 4, 447-462.
- Mozer, M. C. (1994). Neural network music composition by prediction: Exploring the benefits of psychophysical constraints and multiscale processing. Connection Science, 6, 247-280. [FOLLOW LINK FOR AUDIO SAMPLES]
1993
- Bonnlander, B. V., & Mozer, M. C. (1993). Metamorphosis networks: An alternative to constructive methods. S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in Neural Information Processing Systems 5 (pp. 131-138). San Mateo, CA: Morgan Kaufmann Publishers.
- Mozer, M. C. (1993). Neural network architectures for temporal pattern processing. In A. S. Weigend & N. A. Gershenfeld (Eds.), Time series prediction: Forecasting the future and understanding the past (pp. 243-264). Redwood City, CA: Sante Fe Institute Studies in the Sciences of Complexity, Proceedings Volume XVII, Addison-Wesley Publishing.
- Mozer, M. C., & Das, S. (1993). A connectionist symbol manipulator that discovers the structure of context-free languages. In S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in Neural Information Processing Systems 5 (pp. 863-870). San Mateo, CA: Morgan Kaufmann Publishers.
- Schmidhuber, J. H., Mozer, M. C., & Prelinger, D. (1993). Continuous history compression. In H. Huening, S. Neuhauser, M. Raus, & W. Ritschel (Eds.), Workshop on Neural Networks (pp. 87-95). Aachen: Augustinus.
1992
- McMillan, C., Mozer, M. C., & Smolensky, P. (1992). Rule induction through integrated symbolic and subsymbolic processing. In J. E. Moody, S. J. Hanson, & R. P. Lippmann (Eds.), Advances in neural information processing systems 4 (pp. 969-976). San Mateo, CA: Morgan Kaufmann.
- Mozer, M. C. (1992). Induction of multiscale structure. In J. E. Moody, S. J. Hanson, & R. P. Lippmann (Eds.), Advances in neural information processing systems 4 (pp. 275-282). San Mateo, CA: Morgan Kaufmann.
- Mozer, M. C., & Behrmann, M. (1992). Reading with attentional impairments: A brain-damaged model of neglect and attentional dyslexias. In R. G. Reilly & N. E. Sharkey (Eds.), Connectionst Approaches to Language Processing (pp. 409-460). Hillsdale, NJ: Earlbaum.
- Mozer, M. C., Zemel, R. S., Behrmann, M., & Williams, C. K. I. (1992). Learning to segment images using dynamic feature binding. Neural Computation, 4, 650-665.
1991
- McMillan, C., Mozer, M. C., & Smolensky, P. (1991). The connectionist scientist game: Rule extraction and refinement in a neural network. Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society (pp. 424-430). Hillsdale, NJ: Erlbaum.
- Mozer, M. C. (1991). Discovering discrete distributed representations with iterative competitive learning. In R. P. Lippmann, J. Moody, and D. S. Touretzky (Eds.), Advances in neural information processing systems 3 (pp. 627-634). San Mateo, CA: Morgan Kaufmann.
1990
- Mozer, M. C. (1990). The perception of multiple objects: A connectionist approach. Cambridge, MA: MIT Press.
- Mozer, M. C. (1990). Discovering faithful Wickelfeature representations in a connectionist network. Proceedings of the Twelfth Annual Conference of the Cognitive Science Society (pp. 356-363). Hillsdale, NJ: Erlbaum.
- Mozer, M. C., & Behrmann, M. (1990). On the interaction of selective attention and lexical knowledge: A connectionist account of neglect dyslexia. Journal of Cognitive Neuroscience, 2, 96-123.
- Zemel, R. S., Mozer, M. C., & Hinton G. E. (1990). TRAFFIC: Object recognition using hierarchical reference frame transformations. In D. Touretzky (Ed.), Advances in neural information processing systems 2 (pp. 266-273). San Mateo, CA: Morgan Kaufmann.
1989
- Mozer, M. C. (1989). A focused backpropagation algorithm for temporal pattern recognition. Complex Systems, 3, 349-381.
- Mozer, M. C. (1989). Types and tokens in visual word perception. Journal of Experimental Psychology: Human Perception and Performance, 15, 287-303.
- Mozer, M. C., & Smolensky, P. (1989). Using relevance to reduce network size automatically. Connection Science, 1, 3-16.
1988
1987
1986
- McClelland, J. L., and Mozer, M.C. (1986). Perceptual interactions in two-word displays: Familiarity and similarity effects. Journal of Experimental Psychology: Human Perception and Performance, 12, 18-35.
- Mozer, M. C., & Gross, K. P. (1986). An architecture for experiential learning. In T. M. Mitchell, J. G. Carbonell, R. S. Michalski (Eds.), Machine learning: A guide to current research (pp. 219-226). Boston: Kluwer Academic.