Publication

Found 908 results
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M
Myanganbayar, B. et al. Partially Occluded Hands: A challenging new dataset for single-image hand pose estimation. (2018).PDF icon CBMM-Memo-097.pdf (8.53 MB)
Myanganbayar, B. et al. Partially Occluded Hands: A challenging new dataset for single-image hand pose estimation. The 14th Asian Conference on Computer Vision (ACCV 2018) (2018). at <http://accv2018.net/>PDF icon partially-occluded-hands-6.pdf (8.29 MB)
Mutch, J. hmin: A Minimal HMAX Implementation. (2010).
Mutch, J. HMAX Package for CNS. (2012).File hmax.tar (210 KB)
Mutch, J., Knoblich, U. & Poggio, T. CNS (“Cortical Network Simulator”): a GPU-based framework for simulating cortically-organized networks. (2010).File cns.tar (1.46 MB)PDF icon MIT-CSAIL-TR-2010-013.pdf (389.38 KB)File (last version before switch to classdef syntax)  (1.05 MB)
Mutch, J. et al. Computational and Cognitive Neuroscience of Vision 85-104 (Springer, 2017).
Mutch, J. & Turaga, S. cnpkg: 3-D Convolutional Network Package for CNS. (2012).File cnpkg.tar (50 KB)
N. Murty, A. Ratan, Bashivan, P., Abate, A., DiCarlo, J. J. & Kanwisher, N. Computational models of category-selective brain regions enable high-throughput tests of selectivity. Nature Communications 12, (2021).PDF icon s41467-021-25409-6.pdf (6.47 MB)
N. Murty, A. Ratan & Arun, S. P. Effect of silhouetting and inversion on view invariance in the monkey inferotemporal cortex. Journal of Neurophysiology 11823, 353 - 362 (2017).
N. Murty, A. Ratan & Arun, S. P. A Balanced Comparison of Object Invariances in Monkey IT Neurons. eneuro 4, ENEURO.0333-16.2017 (2017).
N. Murty, A. Ratan & Pramod, R. T. To What Extent Does Global Shape Influence Category Representation in the Brain?. Journal of Neuroscience 36, 4149 - 4151 (2016).
N. Murty, A. Ratan & Arun, S. P. Dynamics of 3D view invariance in monkey inferotemporal cortex. Journal of Neurophysiology 11319212373232821, 2180 - 2194 (2015).
N. Murty, A. Ratan & Arun, S. P. Seeing a straight line on a curved surface: decoupling of patterns from surfaces by single IT neurons. Journal of Neurophysiology 11773, 104 - 116 (2017).
Muir, D., Fang, X. & Meyers, E. Brain-Observatory-Toolbox. (2018).
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)
Mroueh, Y., Voinea, S. & Poggio, T. Learning with Group Invariant Features: A Kernel Perspective. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5798-learning-with-group-invariant-features-a-kernel-perspective>PDF icon LearningInvarianceKernel_NIPS2015.pdf (292.18 KB)
Mottaghi, R., Fidler, S., Yuille, A., Urtasun, R. & Parikh, D. Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding. (2014).PDF icon CBMM-Memo-020.pdf (1.89 MB)
Mormann, F. et al. Single neuron studies of the human brain. Probing cognition (2014).
Morère, O. et al. Group Invariant Deep Representations for Image Instance Retrieval. (2016).PDF icon CBMM-Memo-043.pdf (2.66 MB)
Morère, O., Veillard, A., Chandrasekhar, V. & Poggio, T. Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. arXiv.org (2016). at <https://arxiv.org/abs/1603.04595>PDF icon 1603.04595.pdf (2.9 MB)
Morales, A., Premtoon, V., Avery, C., Felshin, S. & Katz, B. Learning to Answer Questions from Wikipedia Infoboxes. The 2016 Conference on Empirical Methods on Natural Language Processing (EMNLP 2016) (2016).PDF icon Morales-EMNLP2016.pdf (197.28 KB)
Montagna, F., Noceti, N., Rosasco, L., Zhang, K. & Locatello, F. Scalable Causal Discovery with Score Matching. NeurIPS 2022 (2022). at <https://openreview.net/forum?id=v56PHv_W2A>
Montagna, F. et al. Assumption violations in causal discovery and the robustness of score matching. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2024). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/93ed74938a54a73b5e4c52bbaf42ca8e-Paper-Conference.pdf>
Mlynarski, W. & McDermott, J. H. Learning Mid-Level Codes for Natural Sounds. Advances and Perspectives in Auditory Neuroscience (2016).PDF icon APAN_large_JHM kopia.pdf (19.74 MB)
Mlynarski, W. & McDermott, J. H. Co-occurrence statistics of natural sound features predict perceptual grouping. Computational and Systems Neuroscience (Cosyne) 2018 (2018).

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