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Filters: Author is F. Anselmi [Clear All Filters]
Computational and Cognitive Neuroscience of Vision 85-104 (Springer, 2017).
Symmetry Regularization. (2017).
CBMM-Memo-063.pdf (6.1 MB)
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View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology 27, 1-6 (2017).
On invariance and selectivity in representation learning. Information and Inference: A Journal of the IMA iaw009 (2016). doi:10.1093/imaiai/iaw009
imaiai.iaw009.full_.pdf (267.87 KB)
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View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. (2016).
faceMirrorSymmetry_memo_ver01.pdf (3.93 MB)
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Visual Cortex and Deep Networks: Learning Invariant Representations. 136 (The MIT Press, 2016). at <https://mitpress.mit.edu/books/visual-cortex-and-deep-networks>
Deep Convolutional Networks are Hierarchical Kernel Machines. (2015).
CBMM Memo 035_rev5.pdf (975.65 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. PLOS Computational Biology 11, e1004390 (2015).
journal.pcbi_.1004390.pdf (2.04 MB)
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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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I-theory on depth vs width: hierarchical function composition. (2015).
cbmm_memo_041.pdf (1.18 MB)
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Notes on Hierarchical Splines, DCLNs and i-theory. (2015).
CBMM Memo 037 (1.83 MB)
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Unsupervised learning of invariant representations. Theoretical Computer Science (2015). doi:10.1016/j.tcs.2015.06.048
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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