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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Leonard, J. A., Garcia, A. & Schulz, L. How Adults’ Actions, Outcomes, and Testimony Affect Preschoolers’ Persistence. Child Development (2019). doi:10.1111/cdev.13305

Leonard, J. A., Lee, Y. & Schulz, L. Infants make more attempts to achieve a goal when they see adults persist. Science 357, 1290 - 1294 (2017).

Leibo, J. Z., Liao, Q., Anselmi, F., Freiwald, W. A. & Poggio, T. View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology 27, 1-6 (2017).

Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLOS Computational Biology 11, e1004390 (2015).

journal.pcbi_.1004390.pdf (2.04 MB)

Leibo, J. Z., Liao, Q., Freiwald, W. A., Anselmi, F. & Poggio, T. View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. (2016).

faceMirrorSymmetry_memo_ver01.pdf (3.93 MB)

Leibo, J. Z., Liao, Q. & Poggio, T. Subtasks of unconstrained face recognition. (2014).

Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2015).

modularity_dataset_ver1.tar.gz (36.14 MB)

Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2014). doi:10.1101/004473

CBMM Memo 004_new.pdf (2.25 MB)

Leibo, J. Z., Liao, Q. & Poggio, T. Subtasks of Unconstrained Face Recognition. (2014).

Leibo_Liao_Poggio_subtasks_VISAPP_2014.pdf (268.69 KB)

Lee, M. J. & DiCarlo, J. J. An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models. bioRxiv (2023). at <https://www.biorxiv.org/content/10.1101/2022.12.31.522402v1>

Leavitt, M. L., Mendoza-Halliday, D. & J.C., M. - T. Sustained Activity Encoding Working Memories: Not Fully Distributed. Trends in Neurosciences 40 , 328-346 (2017).

Le Van Quyen, M. et al. High-frequency oscillations in human and monkey neocortex during the wake–sleep cycle. Proceedings of the National Academy of Sciences (2016). doi:10.1073/pnas.1523583113

BetaGammaSleepAwakeFull.pdf (3.68 MB)

Lattman, E., Poggio, T. & Westervelt, R. NSF Science and Technology Centers – The Class of 2013. (2013).

NSFGender2013_poster.pdf (2.77 MB)

Landi, S. M. & Freiwald, W. A. Two areas for familiar face recognition in the primate brain. Science 357, 591 - 595 (2017).

591.full_.pdf (928.29 KB)

Landi, S. M., Viswanathan, P., Serene, S. & Freiwald, W. A. A fast link between face perception and memory in the temporal pole. Science eabi6671 (2021). doi:10.1126/science.abi6671

Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. Behavioral and Brain Sciences 40, e253 (2017).

Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. Human-level concept learning through probabilistic program induction. Science 350, 1332-1338 (2015).

Lake, B. M., Ullman, T., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. (2016).

machines_that_think.pdf (3.45 MB)

Lagomarsino-Oneto, D. et al. Physics informed machine learning for wind speed prediction. Energy 268, 126628 (2023).

Lafer-Sousa, R., Conway, B. R. & Kanwisher, N. Color-Biased Regions of the Ventral Visual Pathway Lie between Face- and Place-Selective Regions in Humans, as in Macaques. Journal of Neuroscience 36, 1682 - 1697 (2016).

K

Kuo, Y. - L., Katz, B. & Barbu, A. Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. (2020).

CBMM-Memo-125.pdf (2.12 MB)

Kuo, Y. - L., Barbu, A. & Katz, B. Compositional RL Agents That Follow Language Commands in Temporal Logic. (2021).

CBMM-Memo-127.pdf (2.12 MB)

Kuo, Y. - L., Katz, B. & Barbu, A. Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020). doi:10.1109/IROS45743.2020.9341325

Kuo, Y. - L., Katz, B. & Barbu, A. Deep compositional robotic planners that follow natural language commands . International Conference on Robotics and Automation (ICRA) (2020).

Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. (2022).

CBMM-Memo-132.pdf (1.15 MB)

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