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.

Videos
Support Us

Publication

Search

Show only items where

Author

Type

Term

Year

Keyword

Found 906 results

] Title Type Year

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

C

Ceola, F., Rosasco, L., Natale, L. & Ceola, F. RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement LearningRESPRECT: Speeding-Up Multi-Fingered Grasping With Residual Reinforcement Learning_supp1-3363532.mp4. IEEE Robotics and Automation Letters 9, 3045 - 3052 (2024).

Casper, S. et al. Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021 (2021). at <https://dblp.org/rec/conf/aaai/CasperBDGSVK21.html>

1912.04783.pdf (6.69 MB)

Casper, S., Nadeau, M. & Kreiman, G. One thing to fool them all: generating interpretable, universal, and physically-realizable adversarial features. arXiv (2022). doi:10.48550/arXiv.2110.03605

2110.03605.pdf (6.7 MB)

Casper, S., Nadeau, M., Hadfield-Menell, D. & Kreiman, G. Robust Feature-Level Adversaries are Interpretability Tools. NeurIPS (2022). at <https://openreview.net/forum?id=lQ--doSB2o>

8789_robust_feature_level_adversari.pdf (3.79 MB)

Cano-Córdoba, F., Sarma, S. & Subirana, B. Theory of Intelligence with Forgetting: Mathematical Theorems Explaining Human Universal Forgetting using “Forgetting Neural Networks”. (2017).

CBMM-Memo-071.pdf (2.54 MB)

Canas, G. D., Poggio, T. & Rosasco, L. Learning manifolds with k-means and k-flats. Advances in Neural Information Processing Systems 25 (NIPS 2012) (2012). at <https://papers.nips.cc/paper/2012/hash/b20bb95ab626d93fd976af958fbc61ba-Abstract.html>

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).

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>

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

B

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)

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>

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

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)

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)

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>

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)

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)

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).

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

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

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)

Pages