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In Press
Mlynarski, W. & Hermundstad, A. M. Adaptive Coding for Dynamic Sensory Inference. eLife (In Press).
Sliwa, J., Marvel, S. R., Ianni, G. A. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Organization for Computational Neurosciences - CNS 2018 (In Press). at <http://www.cnsorg.org/cns-2018>
Palepu, A. & Kreiman, G. Development of automated interictal spike detector. 40th International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2018 (In Press). at <https://embc.embs.org/2018/>
Ben-Yosef, G. & Ullman, S. Image interpretation above and below the object level. Proceedings of the Royal Society: Interface Focus (In Press).PDF icon 2018-BenYosef_Ullman-Image_interpretation_above_and_below the object_level.pdf (3.26 MB)
Eric, W., Kevin, W. & Gabriel, K. Learning scene gist with convolutional neural networks to improve object recognition. 2018 52nd Annual Conference on Information Sciences and Systems (CISS) (In Press). doi:10.1109/CISS.2018.8362305PDF icon 08362305.pdf (3.17 MB)
Gerstenberg, T. et al. Lucky or clever? From changed expectations to attributions of responsibility. Cognition (In Press).
Kell, A. J. E., Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V. & McDermott, J. H. A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. Neuron 98, (In Press).
2020
Kim, D. et al. The ability to predict actions of others from distributed cues is still developing in children. PsyArXiv Preprints (2020). doi:10.31234/osf.io/pu3tfPDF icon Action_prediction_in_children.pdf (427.84 KB)
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 (2019).
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)
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).
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).
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>
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
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 Edits, adding bound for test error, stability (845.63 KB)PDF icon Added words and a footnote (887.9 KB)PDF icon Incorporated footnote in text plus other edits (854.05 KB)PDF icon Deleted results on kernel regression and deep nets which will appear in a separate paper (222.64 KB)
Ullman, T. D. et al. Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects. Cognitive Science Society (2019). at <https://mindmodeling.org/cogsci2019/papers/0506/index.html>PDF icon Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects.pdf (2.62 MB)
Kar, K., Kubilius, J., Schmidt, K., Issa, E. B. & DiCarlo, J. J. Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience (2019). doi:10.1038/s41593-019-0392-5PDF icon Author's last draft (1.74 MB)
Ponce, C. R. et al. Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell 177, 1009 (2019).PDF icon Author's last draft (20.26 MB)

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