Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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Found 906 results

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A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

B

Berzak, Y. et al. Universal Dependencies for Learner English. (2016).

memo-52_rev1.pdf (472.67 KB)

Berzak, Y., Nakamura, C., Flynn, S. & Katz, B. Predicting Native Language from Gaze. Annual Meeting of the Association for Computational Linguistics (ACL 2017) (2017).

Berzak, Y. et al. Treebank of Learner English (TLE). (2016). at <http://esltreebank.org/>

acl2016.pdf (163.86 KB)

Berzak, Y., Barbu, A., Harari, D., Katz, B. & Ullman, S. Do You See What I Mean? Visual Resolution of Linguistic Ambiguities. Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal. (2015).

Berzak, Y., Barbu, A., Harari, D., Katz, B. & Ullman, S. Do You See What I Mean? Visual Resolution of Linguistic Ambiguities. (2016).

memo-51.pdf (2.74 MB)

Berzak, Y., Reichart, R. & Katz, B. Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL. (2016).

memo-50.pdf (493.74 KB)

Berzak, Y., Huang, Y., Barbu, A., Korhonen, A. & Katz, B. Anchoring and Agreement in Syntactic Annotations. (2016).

CBMM-Memo-055.pdf (768.54 KB)

Berzak, Y., Reichart, R. & Katz, B. Reconstructing Native Language Typology from Foreign Language Usage. (2014).

CBMM-Memo-007.pdf (683.75 KB)

Berzak, Y., Barbu, A., Harari, D., Katz, B. & Ullman, S. Language and Vision Ambiguities (LAVA) Corpus. (2016). at <http://web.mit.edu/lavacorpus/>

D15-1172.pdf (2.42 MB)

Betta, I. Dalla et al. In silico modeling of temporally interfering electric fields for deep brain stimulation . Society for Neuroscience (2019).

Bigelow, E. J., McCoy, J. P. & Ullman, T. D. Non-commitment in mental imagery. Cognition 238, 105498 (2023).

Bill, J., Gershman, S. J. & Drugowitsch, J. Visual motion perception as online hierarchical inference. Nature Communications 13, (2022).

Bill, J., Pailian, H., Gershman, S. J. & Drugowitsch, J. Hierarchical structure is employed by humans during visual motion perception. Proceedings of the National Academy of Sciences 117, 24581 - 24589 (2020).

Bomatter, P. et al. When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes. International Conference on Computer Vision (ICCV) (2021). doi:10.1109/iccv48922.2021.00032

Bomatter_When_Pigs_Fly_Contextual_Reasoning_in_Synthetic_and_Natural_Scenes_ICCV_2021_paper.pdf (3.24 MB)

Bongiorno, C. et al. Vector-based pedestrian navigation in cities. Nature Computational Science 1, 678 - 685 (2021).

s43588-021-00130-y.pdf (1.96 MB)

Bono, S. et al. The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function. (2025). at <https://arxiv.org/abs/2401.15856>

Bramley, N., Mayrhofer, R., Gerstenberg, T. & Lagnado, D. A. Causal learning from interventions and dynamics in continuous time. Cognitive Science Conference (2017).

Bramley et al. - 2017 - Causal learning from interventions and dynamics in.pdf (1.78 MB)

Bramley, N., Gerstenberg, T. & Tenenbaum, J. B. Natural science: Active learning in dynamic physical microworlds. 38th Annual Meeting of the Cognitive Science Society (2016).

Natural Science (Bramley, Gerstenberg, Tenenbaum, 2016).pdf (5.39 MB)

Brewer, K., Mittman, B., Kominsky, J. & Henes, J. Open Source Subject Database Project (OSSDP). (2019).

Bricken, T., Davies, X., Singh, D., Krotov, D. & Kreiman, G. Sparse distributed memory is a continual learner. International Conference on Learning Representations (2023). at <https://openreview.net/forum?id=JknGeelZJpHP>

6086_sparse_distributed_memory_is_a.pdf (13.3 MB)

Bricken, T., Schaeffer, R., Olshausen, B. & Kreiman, G. Emergence of Sparse Representations from Noise. ICML 2023 (2023). at <https://openreview.net/pdf?id=cxYaBAXVKg>

Buice, M. & de Vries, S. Population Coding, Correlations, and Functional Connectivity in the mouse visual system with the Cortical Activity Map (CAM). Society for Neuroscience 2015 (2015).

2015 SFN Population_Coding.pdf (2.94 MB)

C

Cadieu, C. F. et al. Neural Representation Benchmark [code]. (2013).

Caldarelli, E., Chatalic, A., Colom´e, A. `a, Rosasco, L. & Torras, C. Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees. 7th Conference on Robot Learning (CoRL 2023 (2023). at <https://proceedings.mlr.press/v229/caldarelli23a/caldarelli23a.pdf>

Calero, C. I., Shalom, D. E., Spelke, E. S. & Sigman, M. Language, gesture, and judgment: Children’s paths to abstract geometry. Journal of Experimental Child Psychology 177, 70 - 85 (2019).

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