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).
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).
2019
Adler, A. & Wax, M. Constant modulus algorithms via low-rank approximation. Signal Processing 160, 263 - 270 (2019).
Idiart, M. A. P., Villavicencio, A., Katz, B., Rennó-Costa, C. & Lisman, J. How Does the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms. Frontiers in Computational Neuroscience 13, (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>
Fazeli, N. et al. See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion. Science Robotics 4, eaav3123 (2019).
Ullman, S. Using neuroscience to develop artificial intelligence. Science 363, 692 - 693 (2019).
2018
Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. (2018).PDF icon CBMM-Memo-076.pdf (772.61 KB)
Berzak, Y., Katz, B. & Levy, R. Assessing Language Proficiency from Eye Movements in Reading. 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2018). at <http://naacl2018.org/>PDF icon 1804.07329.pdf (350.43 KB)
Spokes, A. C. & Spelke, E. S. At 4.5 but not 5.5 years, children favor kin when the stakes are moderately high. PLOS ONE 13, (2018).
Muir, D., Fang, X. & Meyers, E. Brain-Observatory-Toolbox. (2018).
Villalobos, K. M. et al. Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?. (2018).PDF icon CBMM-Memo-095.pdf (1.92 MB)
Liao, Q., Miranda, B., Hidary, J. & Poggio, T. Classical generalization bounds are surprisingly tight for Deep Networks. (2018).PDF icon CBMM-Memo-091.pdf (1.43 MB)PDF icon CBMM-Memo-091-v2.pdf (1.88 MB)
Sliwa, J., Marvel, S. R., Ianni, G. A. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Social & Affective Neuroscience Society (SANS) (2018). at <http://www.socialaffectiveneuro.org/conferences.html>
Zisselman, E., Adler, A. & Elad, M. Handbook of Numerical Analysis 19, 3 - 17 (Elsevier, 2018).
Dehghani, N. & Wimmer, R. A computational perspective of the role of Thalamus in cognition. arxiv (2018). at <https://arxiv.org/abs/1803.00997>PDF icon ThalamusComputationArxiv.pdf (5.12 MB)
Adler, A. & Wax, M. Constant Modulus Algorithms via Low-Rank Approximation. (2018).PDF icon CBMM-Memo-077.pdf (795.61 KB)
Adler, A. & Wax, M. Constant Modulus Beamforming Via Low-Rank Approximation. 2018 IEEE Statistical Signal Processing Workshop (SSP) (2018). doi:10.1109/SSP.2018.8450799
Mlynarski, W. & McDermott, J. H. Co-occurrence statistics of natural sound features predict perceptual grouping. Computational and Systems Neuroscience (Cosyne) 2018 (2018).
Mlynarski, W. & McDermott, J. Co-occurrence statistics of natural sound features predict perceptual grouping. Computational and Systems Neuroscience (COSYNE) (2018). at <http://www.cosyne.org/c/index.php?title=Cosyne_18>

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