Publication

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Submitted
Lin, H. & Tegmark, M. Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language. (Submitted).PDF icon Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language (1.64 MB)
Jing, L. et al. Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. (Submitted). at <https://arxiv.org/abs/1612.05231>PDF icon 1612.05231.pdf (2.3 MB)
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>
Zhang, Z., Xie, C., Wang, J., Xie, L. & Yuille, A. DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion. Conference on Computer Vision and Pattern Recognition (CVPR) (In Press). at <http://cvpr2018.thecvf.com/>
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. IEEE Information Sciences and Systems - CISS 2018 (In Press). at <https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001319>
Gerstenberg, T. et al. Lucky or clever? From changed expectations to attributions of responsibility. Cognition (In Press).
Zhang, Z. et al. Single-Shot Object Detection with Enriched Semantics. Conference on Computer Vision and Pattern Recognition (CVPR) (In Press). at <http://cvpr2018.thecvf.com/>
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).
Isik, L. et al. What is changing when: decoding visual information in movies from human intracranial recordings. NeuroImage (In Press). doi:10.1016/j.neuroimage.2017.08.027PDF icon Human neurophysiological responses during movies (2.78 MB)
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).
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>
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)
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>
Mlynarski, W. & McDermott, J. H. Co-occurrence statistics of natural sound features predict perceptual grouping. Computational and Systems Neuroscience (Cosyne) 2018 (2018).
Livingstone, M. S., Arcaro, M. J. & Schade, P. F. Cortex Is Cortex: Ubiquitous Principles Drive Face-Domain Development. Trends in Cognitive Sciences (2018). doi:10.1016/j.tics.2018.10.009PDF icon 1-s2.0-S1364661318302572-main.pdf (260.4 KB)
Yuille, A. L. & Liu, C. Deep Nets: What have they ever done for Vision?. (2018).PDF icon CBMM-Memo-088.pdf (7.88 MB)
Shen, W. et al. Deep Regression Forests for Age Estimation. (2018).PDF icon CBMM-Memo-085.pdf (2.2 MB)

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