Publication

Found 62 results
Author Title Type [ Year(Desc)]
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2013
Villa, S. et al. Empirical Inference 59 - 69 (Springer Berlin Heidelberg, 2013). doi:10.1007/978-3-642-41136-610.1007/978-3-642-41136-6_7PDF icon Author's Version (147.25 KB)
Villa, S. et al. Empirical Inference 59 - 69 (Springer Berlin Heidelberg, 2013). doi:10.1007/978-3-642-41136-610.1007/978-3-642-41136-6_7PDF icon Author's Version (147.25 KB)
2014
Amir, N. et al. Abstracts of the 2014 Brains, Minds, and Machines Summer Course. (2014).PDF icon CBMM-Memo-024.pdf (2.86 MB)
Zhang, C., Evangelopoulos, G., Voinea, S., Rosasco, L. & Poggio, T. A Deep Representation for Invariance and Music Classification. ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2014). doi:10.1109/ICASSP.2014.6854954
Zhang, C., Evangelopoulos, G., Voinea, S., Rosasco, L. & Poggio, T. A Deep Representation for Invariance And Music Classification. (2014).PDF icon CBMM-Memo-002.pdf (1.63 MB)
Evangelopoulos, G., Voinea, S., Zhang, C., Rosasco, L. & Poggio, T. Learning An Invariant Speech Representation. (2014).PDF icon CBMM-Memo-022-1406.3884v1.pdf (1.81 MB)
Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L. & Poggio, T. Phone Classification by a Hierarchy of Invariant Representation Layers. INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association (International Speech Communication Association (ISCA), 2014). at <http://www.isca-speech.org/archive/interspeech_2014/i14_2346.html>
Prevedel, R. et al. Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy. Nature Methods 11, 727 - 730 (2014).
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Speech Representations based on a Theory for Learning Invariances. (2014).
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Word-level Invariant Representations From Acoustic Waveforms. INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association (International Speech Communication Association (ISCA), 2014). at <http://www.isca-speech.org/archive/interspeech_2014/i14_2385.html>
2015
Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nature Neuroscience 18, 1844 (2015).PDF icon Preprint (40.28 MB)
Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L. & Poggio, T. Discriminative Template Learning in Group-Convolutional Networks for Invariant Speech Representations. INTERSPEECH-2015 (International Speech Communication Association (ISCA), 2015). at <http://www.isca-speech.org/archive/interspeech_2015/i15_3229.html>
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)
Rosasco, L. & Villa, S. Learning with incremental iterative regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/6015-learning-with-incremental-iterative-regularization>PDF icon Learning with Incremental Iterative Regularization_1405.0042v2.pdf (504.66 KB)
Siegle, J. H., Hale, G. J., Newman, J. P. & Voigts, J. Neural ensemble communities: open-source approaches to hardware for large-scale electrophysiology. Current Opinion in Neurobiology 32, 53 - 59 (2015).
Vaziri-Pashkam, M. Predicting actions before they occur. (2015).PDF icon PredictingActions (1.43 MB)File Supplemental Video 1: Experimental set up and task (16.38 MB)File Supplemental Video 2: An example FullVid and CutVid trial clips from experiment 4 (5.47 MB)
Cormiea, S., Vaziri-Pashkam, M. & Nakayama, K. Unconscious perception of an opponent's goal. Vision Sciences Society Annual Meeting (VSS 2015) (2015). doi:10.1167/15.12.43
2016
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)
2017
Chandrasekhar, V. et al. Compression of Deep Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1701.04923>PDF icon 1701.04923.pdf (614.33 KB)
Tacchetti, A., Voinea, S. & Evangelopoulos, G. Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets. (2017).PDF icon CBMM-Memo-062.pdf (9.37 MB)
Volokitin, A., Roig, G. & Poggio, T. Do Deep Neural Networks Suffer from Crowding?. (2017).PDF icon CBMM-Memo-069.pdf (6.47 MB)
Volokitin, A. & Roig, G. Do Deep Neural Networks Suffer from Crowding? [code]. (2017).
Spokes, A. C., Venkatesan, T. & Spelke, E. S. Five-month-old infants attend to responsive caregivers. Cognitive Development Society (CDS) (2017). at <https://cogdevsoc.org/wp-content/uploads/2017/10/CDS2017AbstractBook.pdf>
Wu, J., Lu, E., Kohli, P., Freeman, W. T. & Tenenbaum, J. B. Learning to See Physics via Visual De-animation. Advances in Neural Information Processing Systems 30 152–163 (2017). at <http://papers.nips.cc/paper/6620-learning-to-see-physics-via-visual-de-animation.pdf>PDF icon Learning to See Physics via Visual De-animation (1.11 MB)

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