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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C

Villalobos, K. M. et al. Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?. (2018).

CBMM-Memo-095.pdf (1.96 MB)

Jacquot, V., Ying, J. & Kreiman, G. Can Deep Learning Recognize Subtle Human Activities?. CVPR 2020 (2020).

Liao, Q., Leibo, J. Z., Mroueh, Y. & Poggio, T. Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?. (2014).

CBMM-Memo-003.pdf (963.66 KB)

B

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

machines_that_think.pdf (3.45 MB)

Liao, Q. & Poggio, T. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. (2016).

CBMM Memo No. 047 (1.29 MB)

Schrimpf, M. & Kubilius, J. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv preprint (2018). doi:10.1101/407007

Brain-Score bioRxiv.pdf (789.83 KB)

Muir, D., Fang, X. & Meyers, E. Brain-Observatory-Toolbox. (2018).

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)

Dobs, K., Martinez-Trujillo, J., Kell, A. J. E. & Kanwisher, N. Brain-like functional specialization emerges spontaneously in deep neural networks. Science Advances 8, (2022).

Wang, C. et al. BrainBERT: Self-supervised representation learning for Intracranial Electrodes. International Conference on Learning Representations (2023). at <https://openreview.net/forum?id=xmcYx_reUn6>

985_brainbert_self_supervised_repr.pdf (9.71 MB)

Adler, A., Wax, M. & Pantazis, D. Brain Signals Localization by Alternating Projections. arXiv (2019).

CBMM-Memo-099.pdf (421.67 KB)

Gómez-Laberge, C., Smolyanskaya, A., Nassi, J. J., Kreiman, G. & Born, R. T. Bottom-up and Top-down Input Augment the Variability of Cortical Neurons. Neuron 91(3), 540-547 (2016).

Adler, A. & Wax, M. Blind Constant Modulus Multiuser Detection via Low-Rank Approximation. IEEE Signal Processing Letters 1 - 1 (2019). doi:10.1109/LSP.9710.1109/LSP.2019.2918001

Xiao, W., Chen, H., Liao, Q. & Poggio, T. Biologically-plausible learning algorithms can scale to large datasets. International Conference on Learning Representations, (ICLR 2019) (2019).

gk7779.pdf (721.53 KB)

Xiao, W., Chen, H., Liao, Q. & Poggio, T. Biologically-plausible learning algorithms can scale to large datasets. (2018).

CBMM-Memo-092.pdf (1.31 MB)

Reddy, M. Vuyyuru, Banburski, A., Pant, N. & Poggio, T. Biologically Inspired Mechanisms for Adversarial Robustness. (2020).

CBMM_Memo_110.pdf (3.14 MB)

Kreiman, G. Biological and Computer Vision. (Cambridge University Press, 2021). doi:10.1017/9781108649995

Kreiman, G. & Serre, T. Beyond the feedforward sweep: feedback computations in the visual cortex. Ann. N.Y. Acad. Sci. | Special Issue: The Year in Cognitive Neuroscience 1464, 222-241 (2020).

gk7812.pdf (1.93 MB)

Kreiman, G. & Serre, T. Beyond the feedforward sweep: feedback computations in the visual cortex. Annals of the New York Academy of Sciences 1464, 222 - 241 (2020).

Madan, S. et al. Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. NeurIPS 2024 (2024).

Dobs, K., Yuan, J., Martinez, J. & Kanwisher, N. Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition. Proceedings of the National Academy of Sciences 120, (2023).

Zhang, M. & Kreiman, G. Beauty is in the eye of the machine. Nature Human Behaviour 5, 675 - 676 (2021).

Muecke, N., Neu, G. & Rosasco, L. Beating SGD Saturation with Tail-Averaging and Minibatching. Neural Information Processing Systems (NeurIPS 2019) (2019).

9422-beating-sgd-saturation-with-tail-averaging-and-minibatching.pdf (389.35 KB)

Chen, Z., Linderman, S. W. & Wilson, M. A. Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes. IEEE Workshop on Machine Learning for Signal Processing (2016).

MLSP16 (1).pdf (1.04 MB)

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