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2019
Zhang, Y., Marciniak, K. & Freiwald, W. A. Analysis of Macaque Monkeys’ Social and Physical Interaction Processing with Eye tracking Data. The Rockefeller University 2019 Summer Science Research Program (SSRP) (2019).
Mhaskar, H. N. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. (2019).PDF icon CBMM-Memo-098.pdf (687.36 KB)PDF icon CBMM Memo 098 v4 (08/2019) (2.63 MB)
Jozwik, K. M., Lee, H., Kanwisher, N. & DiCarlo, J. J. Are topographic deep convolutional neural networks better models of the ventral visual stream?. Conference on Cognitive Computational Neuroscience (2019).
Muecke, N., Neu, G. & Rosasco, L. Beating SGD Saturation with Tail-Averaging and Minibatching. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9422-beating-sgd-saturation-with-tail-averaging-and-minibatching.pdf (389.35 KB)
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).PDF icon gk7779.pdf (721.53 KB)
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
Adler, A., Wax, M. & Pantazis, D. Brain Signals Localization by Alternating Projections. arXiv (2019).PDF icon CBMM-Memo-099.pdf (421.67 KB)
Kubilius, J. et al. Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 2019-10-28 NeurIPS-camera_ready.pdf (1.88 MB)
Kryven, M., Niemi, L., Paul, L. & Tenenbaum, J. B. Choosing a Transformative Experience . Cognitive Sciences Society (2019).
Adler, A. & Wax, M. Constant modulus algorithms via low-rank approximation. Signal Processing 160, 263 - 270 (2019).
Lewis, O. & Hermann, K. Data for free: Fewer-shot algorithm learning with parametricity data augmentation. ICLR 2019 (2019).
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/>
Kell, A. J. E. & McDermott, J. H. Deep neural network models of sensory systems: windows onto the role of task constraints. Current Opinion in Neurobiology 55, 121 - 132 (2019).
Adler, A., Araya-Polo, M. & Poggio, T. Deep Recurrent Architectures for Seismic Tomography. 81st EAGE Conference and Exhibition 2019 (2019).
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
Adler, A. & Wax, M. Direct Localization by Partly Calibrated Arrays: A Relaxed Maximum Likelihood Solution. 27th European Signal Processing Conference, EUSIPCO 2019 (2019). at <http://eusipco2019.org/technical-program>
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).
Norman-Haignere, S. V., Kanwisher, N., McDermott, J. H. & Conway, B. R. Divergence in the functional organization of human and macaque auditory cortex revealed by fMRI responses to harmonic tones. Nature Neuroscience (2019). doi:10.1038/s41593-019-0410-7
Kryven, M., Scholl, B. & Tenenabum, J. B. Does intuitive inference of physical stability interruptattention?. Cognitive Sciences Society (2019).
Poggio, T., Kur, G. & Banburski, A. Double descent in the condition number. (2019).PDF icon Fixing typos, clarifying error in y, best approach is crossvalidation (837.18 KB)PDF icon Incorporated footnote in text plus other edits (854.05 KB)PDF icon Deleted previous discussion on kernel regression and deep nets: it will appear, extended, in a separate paper (795.28 KB)PDF icon RevisedPNASV2.pdf (261.24 KB)

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