Publication

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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).
Ben-Yosef, G., Kreiman, G. & Ullman, S. Minimal videos: Trade-off between spatial and temporal information in human and machine vision. Cognition (In Press).
Lotter, W., Kreiman, G. & Cox, D. A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. Nature Machine Learning (In Press).PDF icon 1805.10734.pdf (9.59 MB)
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)
Kreiman, G. & Serre, T. Beyond the feedforward sweep: feedback computations in the visual cortex. The Year in Cognitive Neuroscience (2020). doi:10.1111/nyas.14320PDF icon gk7812.pdf (1.93 MB)
Jacquot, V., Ying, J. & Kreiman, G. Can Deep Learning Recognize Subtle Human Activities?. CVPR 2020 (2020).
Shalev-Shwartz, S. & Shashua, A. Can we Contain Covid-19 without Locking-down the Economy?. (2020).PDF icon CBMM-Memo-104.pdf (425.12 KB)
Poggio, T. A., Liao, Q. & Banburski, A. Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020).PDF icon s41467-020-14663-9.pdf (431.68 KB)
Yildirim, I., Belledonne, M., Freiwald, W. & Tenenbaum, J. Efficient inverse graphics in biological face processing. Science Advances 6, eaax5979 (2020).PDF icon eaax5979.full_.pdf (3.22 MB)
Liu, S. Nature and origins of intuitive psychology in human infants. (2020).
Zhang, M., Tseng, C. & Kreiman, G. Putting visual object recognition in context. CVPR 2020 (2020).PDF icon gk7876.pdf (3.12 MB)
Han, Y., Roig, G., Geiger, G. & Poggio, T. Scale and translation-invariance for novel objects in human vision. Scientific Reports 10, (2020).PDF icon s41598-019-57261-6.pdf (1.46 MB)
Poggio, T. A. Stable Foundations for Learning: a foundational framework for learning theory in both the classical and modern regime. (2020).PDF icon CBMM-Memo-103.pdf (584.54 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).
Xiao, W., Chen, H., Liao, Q. & Poggio, T. Biologically-plausible learning algorithms can scale to large datasets. 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

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