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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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Mroueh, Y., Voinea, S. & Poggio, T. Learning with Group Invariant Features: A Kernel Perspective. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5798-learning-with-group-invariant-features-a-kernel-perspective>

LearningInvarianceKernel_NIPS2015.pdf (292.18 KB)

Rosasco, L. & Villa, S. Learning with incremental iterative regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/6015-learning-with-incremental-iterative-regularization>

Learning with Incremental Iterative Regularization_1405.0042v2.pdf (504.66 KB)

Owens, A., Isola, P., McDermott, J. H., Freeman, W. T. & Torralba, A. Lecture Notes in Computer ScienceComputer Vision – ECCV 2016Ambient Sound Provides Supervision for Visual Learning. 14th European Conference on Computer Vision 801 - 816 (2016). doi:10.1007/978-3-319-46448-010.1007/978-3-319-46448-0_48

Xu, C. et al. Lecture Notes in Computer ScienceComputer Vision – ECCV 2022Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models. 13697, 638 - 656 (Springer Nature Switzerland, 2022).

Rudi, A., Camoriano, R. & Rosasco, L. Less is More: Nyström Computational Regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5936-less-is-more-nystrom-computational-regularization>

Less is More- Nystr ̈om Computational Regularization_1507.04717v4.pdf (287.14 KB)

Zaslavsky, N., Maldonado, M. & Culbertson, J. Let's talk (efficiently) about us: Person systems achieve near-optimal compression. Proceedings of the Annual Meeting of the Cognitive Science Society 43, (2021).

Anzellottti, S., Houlihan, S. Dae, Liburd, Jr, S. & Saxe, R. Leveraging facial expressions and contextual information to investigate opaque representations of emotions. Emotion (2021). doi:10.1037/emo0000685

Anzellotti 2021 Emotion.pdf (1.08 MB)

Traer, J. & McDermott, J. H. A library of real-world reverberation and a toolbox for its analysis and measurement. Annual Meeting of Acoustical Society of America (2017).

Allen, K. R. et al. Lifelong learning of cognitive styles for physical problem-solving: The effect of embodied experienceAbstract. Psychonomic Bulletin & Review (2023). doi:10.3758/s13423-023-02400-4

Spokes, A. C., Howard, R., Mehr, S. A. & Krasnow, M. M. Like Adults, children make consistent welfare tradeoff allocations. Budapest CEU Conference on Cognitive Development (2017).

Spokes, A. C., Howard, R., Mehr, S. A. & Krasnow, M. M. Like adults, children make consistent welfare tradeoff allocations. Society for Research in Child Development Biennial Meeting (2017).

Telenczuk, B. et al. Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex. Nature Scientific Reports (2017). doi:10.1038/srep40211

srep40211.pdf (2.53 MB)

Weisholtz, D. S. et al. Localized task-invariant emotional valence encoding revealed by intracranial recordingsAbstract. Social Cognitive and Affective Neuroscience (2021). doi:10.1093/scan/nsab134

Levine, S., Kleiman-Weiner, M., Schulz, L., Tenenbaum, J. B. & Cushman, F. A. The logic of universalization guides moral judgment. Proceedings of the National Academy of Sciences (PNAS) 202014505 (2020). doi:10.1073/pnas.2014505117

Katz, B. & Barbu, A. A look back at the June 2016 BMM Workshop in Sestri Levante, Italy. (2016).

Sestri Levante Review (359.33 KB)

Zhang, M. et al. Look twice: A generalist computational model predicts return fixations across tasks and species. PLOS Computational Biology 18, e1010654 (2022).

journal.pcbi_.1010654.pdf (4.51 MB)

Scott, K. M. & Schulz, L. Lookit (Part 1): a new online platform for developmental research. Open Mind 1, (2017).

UNCORRECTED PROOF (561.21 KB)

Scott, K. M., Chu, J. & Schulz, L. Lookit (Part 2): Assessing the viability of online developmental research, Results from three case studies. Open Mind 1, (2017).

lookitpart2.pdf (464.02 KB)

Poggio, T. & Cooper, Y. Loss landscape: SGD has a better view. (2020).

CBMM-Memo-107.pdf (1.03 MB)

Typos and small edits, ver11 (955.08 KB)

Small edits, corrected Hessian for spurious case (337.19 KB)

Mlynarski, W. & McDermott, J. H. Lossy Compression of Sound Texture by the Human Auditory System. Society for Neuroscience Meeting (2016).

Mlynarski, W. & McDermott, J. H. Lossy Compression of Uninformative Stimuli in the Auditory System. Association for Otolaryngology Mid-Winter Meeting (2017).

Gerstenberg, T. et al. Lucky or clever? From changed expectations to attributions of responsibility. Cognition (2018).

Koch, C. Lust and the Turing test [Nature] . (2015). at <http://blogs.nature.com/aviewfromthebridge/2015/05/27/lust-and-the-turing-test/>

Lust and the Turing Test.pdf (203.1 KB)

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Tang, H. et al. A machine learning approach to predict episodic memory formation. 2016 Annual Conference on Information Science and Systems (CISS) 539 - 544 (2016). doi:10.1109/CISS.2016.7460560

Zhang, C., Frogner, C., Araya-Polo, M. & Hohl, D. Machine Learning Based Automated Fault Detection in Seismic Traces. EAGE Conference and Exhibition 2014 (2014). at <http://cbcl.mit.edu/publications/eage14.pdf>

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