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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Filters: First Letter Of Last Name is B [Clear All Filters]

2018

Hamrick, J. B. et al. Relational inductive bias for physical construction in humans and machines. In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci 2018) (2018).

1806.01203.pdf (1022.51 KB)

Hamrick, J. B. et al. Relational inductive bias for physical construction in humans and machines. In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci 2018) (2018).

1806.01203.pdf (1022.51 KB)

Adhya, D. et al. Shared gene co-expression networks in autism from induced pluripotent stem cell (iPSC) neurons. BioRxiv (2018). doi:10.1101/349415

Arend, L. et al. Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).

CBMM-Memo-093.pdf (2.99 MB)

Arend, L. et al. Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).

CBMM-Memo-093.pdf (2.99 MB)

Ben-Yosef, G., Kreiman, G. & Ullman, S. Spatiotemporal interpretation features in the recognition of dynamic images. (2018).

CBMM-Memo-094.pdf (1.21 MB)

CBMM-Memo-094-dynamic-figures.zip (1.8 MB)

fig1.ppsx (147.67 KB)

fig2.ppsx (419.72 KB)

fig4.ppsx (673.41 KB)

figS1.ppsx (587.88 KB)

figS2.ppsx (281.56 KB)

Banburski, A. et al. Theory III: Dynamics and Generalization in Deep Networks. (2018).

Original, intermediate versions are available under request (2.67 MB)

CBMM Memo 90 v12.pdf (4.74 MB)

Theory_III_ver44.pdf Update Hessian (4.12 MB)

Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB)

fixing errors and sharpening some proofs (2.45 MB)

Tacchetti, A., Voinea, S. & Evangelopoulos, G. Trading robust representations for sample complexity through self-supervised visual experience. Advances in Neural Information Processing Systems 31 (Bengio, S. et al.) 9640–9650 (Curran Associates, Inc., 2018). at <http://papers.nips.cc/paper/8170-trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf>

trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf (3.32 MB)

NeurIPS2018_Poster.pdf (6.12 MB)

2019

Kubilius, J. et al. Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019).

2019-10-28 NeurIPS-camera_ready.pdf (1.88 MB)

Kubilius, J. et al. Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019).

2019-10-28 NeurIPS-camera_ready.pdf (1.88 MB)

Kuo, Y. - L., Katz, B. & Barbu, A. Deep Compositional Robotic Planners that Follow Natural Language Commands. Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), (2019). at <https://vigilworkshop.github.io/>

Barbu, A., Banda, D. & Katz, B. Deep video-to-video transformations for accessibility with an application to photosensitivity. Pattern Recognition Letters (2019). doi:10.1016/j.patrec.2019.01.019

Barbu, A., Banda, D. & Katz, B. Deep video-to-video transformations for accessibility with an application to photosensitivity. Pattern Recognition Letters (2019). doi:10.1016/j.patrec.2019.01.019

Becker, L. A., Penagos, H., Manoach, D. S., Wilson, M. A. & Varela, C. Disruption of CA1 Sharp-Wave Ripples by the nonbenzodiazepine hypnotic eszopiclone . Society for Neuroscience (2019).

Poggio, T., Kur, G. & Banburski, A. Double descent in the condition number. (2019).

Fixing typos, clarifying error in y, best approach is crossvalidation (837.18 KB)

Incorporated footnote in text plus other edits (854.05 KB)

Deleted previous discussion on kernel regression and deep nets: it will appear, extended, in a separate paper (795.28 KB)

correcting a bad typo (261.24 KB)

Deleted plot of condition number of kernel matrix: we cannot get a double descent curve (769.32 KB)

Banburski, A. et al. Dynamics & Generalization in Deep Networks -Minimizing the Norm. NAS Sackler Colloquium on Science of Deep Learning (2019).

Harrod, J., Purdon, P. L., Brown, E. N. & Flores, F. J. Identification of vigilance states in freely behaving animals using thalamocortical activity and Deep Belief networks. Society for Neuroscience (2019).

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

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

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

Jozwik, K. M., Lee, M., Marques, T., Schrimpf, M. & Bashivan, P. Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge. The Algonauts Project: Explaining the Human Visual Brain Workshop 2019 (2019). doi:10.1101/689844

Ross, C., Berzak, Y., Katz, B. & Barbu, A. Learning Language from Vision. Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS) (2019).

Ross, C., Berzak, Y., Katz, B. & Barbu, A. Learning Language from Vision. Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS) (2019).

Srivastava, S., Ben-Yosef, G. & Boix, X. Minimal images in deep neural networks: Fragile Object Recognition in Natural Images. International Conference on Learning Representations (ICLR) (2019). at <https://arxiv.org/pdf/1902.03227.pdf>

Srivastava, S., Ben-Yosef, G. & Boix, X. Minimal images in deep neural networks: Fragile Object Recognition in Natural Images. International Conference on Learning Representations (ICLR) (2019). at <https://arxiv.org/pdf/1902.03227.pdf>

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