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Filters: Author is Tomaso Poggio [Clear All Filters]
Deep Convolutional Networks are Hierarchical Kernel Machines. (2015).
CBMM Memo 035_rev5.pdf (975.65 KB)
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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>
Holographic Embeddings of Knowledge Graphs. (2015).
holographic-embeddings.pdf (677.87 KB)
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How Important is Weight Symmetry in Backpropagation?. (2015).
1510.05067v3.pdf (615.32 KB)
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On Invariance and Selectivity in Representation Learning. (2015).
CBMM Memo No. 029 (812.07 KB)
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The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2015).
modularity_dataset_ver1.tar.gz (36.14 MB)
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The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLOS Computational Biology 11, e1004390 (2015).
journal.pcbi_.1004390.pdf (2.04 MB)
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Invariant representations for action recognition in the visual system. Computational and Systems Neuroscience (2015).
Invariant representations for action recognition in the visual system. Vision Sciences Society 15, (2015).
I-theory on depth vs width: hierarchical function composition. (2015).
cbmm_memo_041.pdf (1.18 MB)
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Learning with a Wasserstein Loss. Advances in Neural Information Processing Systems (NIPS 2015) 28 (2015). at <http://arxiv.org/abs/1506.05439>
Learning with a Wasserstein Loss_1506.05439v2.pdf (2.57 MB)
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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>
LearningInvarianceKernel_NIPS2015.pdf (292.18 KB)
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Notes on Hierarchical Splines, DCLNs and i-theory. (2015).
CBMM Memo 037 (1.83 MB)
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A Science of Intelligence . (2015).
A Science of Intelligence.pdf (659.5 KB)
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Unsupervised learning of invariant representations. Theoretical Computer Science (2015). doi:10.1016/j.tcs.2015.06.048
What if.. (2015).
What if.pdf (2.09 MB)
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Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?. (2014).
CBMM-Memo-003.pdf (963.66 KB)
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Computational role of eccentricity dependent cortical magnification. (2014).
CBMM-Memo-017.pdf (1.04 MB)
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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
A Deep Representation for Invariance And Music Classification. (2014).
CBMM-Memo-002.pdf (1.63 MB)
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The dynamics of invariant object recognition in the human visual system. (2014). doi:http://dx.doi.org/10.7910/DVN/KRUPXZ
The dynamics of invariant object recognition in the human visual system. J Neurophysiol 111, 91-102 (2014).
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2014). doi:10.1101/004473
CBMM Memo 004_new.pdf (2.25 MB)
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Learning An Invariant Speech Representation. (2014).
CBMM-Memo-022-1406.3884v1.pdf (1.81 MB)
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Learning invariant representations and applications to face verification. NIPS 2013 (Advances in Neural Information Processing Systems 26, 2014). at <http://nips.cc/Conferences/2013/Program/event.php?ID=4074>
Liao_Leibo_Poggio_NIPS_2013.pdf (687.06 KB)
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