Publication

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Filters: Author is Lorenzo Rosasco  [Clear All Filters]
2019
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)
Pagliana, N. & Rosasco, L. Implicit Regularization of Accelerated Methods in Hilbert Spaces. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9591-implicit-regularization-of-accelerated-methods-in-hilbert-spaces.pdf (451.14 KB)
2018
Banburski, A. et al. Theory III: Dynamics and Generalization in Deep Networks. (2018).PDF icon TheoryIII_ver2 (2.67 MB)PDF icon TheoryIII_ver11 (4.17 MB)PDF icon TheoryIII_ver12 (4.74 MB)PDF icon TheoryIII_ver13 (4.75 MB)PDF icon TheoryIII_ver14 (3.89 MB)PDF icon TheoryIII_ver15 (3.9 MB)PDF icon TheoryIII_ver20 (3.91 MB)PDF icon TheoryIII_ver22 (4.97 MB)PDF icon TheoryIII_ver25 (1.19 MB)PDF icon TheoryIII_ver28 (1.17 MB)PDF icon TheoryIII_ver29 (1.17 MB)PDF icon TheoryIII_ver30 (1.17 MB)PDF icon TheoryIII_ver31 (most typos and other errors corrected in main text) (1.18 MB)PDF icon TheoryIII_ver35 (more edits; regression note in appendix) (1.56 MB)PDF icon TheoryIII_ver39 (look at footnote 5) (2.14 MB)
2017
Mutch, J. et al. Computational and Cognitive Neuroscience of Vision 85-104 (Springer, 2017).
Anselmi, F., Evangelopoulos, G., Rosasco, L. & Poggio, T. Symmetry Regularization. (2017).PDF icon CBMM-Memo-063.pdf (6.1 MB)
Poggio, T. et al. Theory of Deep Learning III: explaining the non-overfitting puzzle. (2017).PDF icon CBMM-Memo-073.pdf (2.65 MB)PDF icon CBMM Memo 073 v2 (revised 1/15/2018) (2.81 MB)PDF icon CBMM Memo 073 v3 (revised 1/30/2018) (2.72 MB)PDF icon CBMM Memo 073 v4 (revised 12/30/2018) (575.72 KB)
Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. International Journal of Automation and Computing 1-17 (2017). doi:10.1007/s11633-017-1054-2PDF icon art%3A10.1007%2Fs11633-017-1054-2.pdf (1.68 MB)
2016
Nickel, M., Rosasco, L. & Poggio, T. Holographic Embeddings of Knowledge Graphs. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (2016).PDF icon 1510.04935v2.pdf (360.65 KB)
Anselmi, F., Rosasco, L. & Poggio, T. On invariance and selectivity in representation learning. Information and Inference: A Journal of the IMA iaw009 (2016). doi:10.1093/imaiai/iaw009PDF icon imaiai.iaw009.full_.pdf (267.87 KB)
Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?. (2016).PDF icon CBMM-Memo-058v1.pdf (2.42 MB)PDF icon CBMM-Memo-058v5.pdf (2.45 MB)PDF icon CBMM-Memo-058-v6.pdf (2.74 MB)
2015
Anselmi, F., Rosasco, L., Tan, C. & Poggio, T. Deep Convolutional Networks are Hierarchical Kernel Machines. (2015).PDF icon CBMM Memo 035_rev5.pdf (975.65 KB)
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>
Nickel, M., Rosasco, L. & Poggio, T. Holographic Embeddings of Knowledge Graphs. (2015).PDF icon holographic-embeddings.pdf (677.87 KB)
Anselmi, F., Rosasco, L. & Poggio, T. On Invariance and Selectivity in Representation Learning. (2015).PDF icon CBMM Memo No. 029 (812.07 KB)
Poggio, T., Anselmi, F. & Rosasco, L. I-theory on depth vs width: hierarchical function composition. (2015).PDF icon cbmm_memo_041.pdf (1.18 MB)
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)
Rudi, A., Camoriano, R. & Rosasco, L. Less is More: Nyström Computational Regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5936-less-is-more-nystrom-computational-regularization>PDF icon Less is More- Nystr ̈om Computational Regularization_1507.04717v4.pdf (287.14 KB)
Poggio, T., Rosasco, L., Shashua, A., Cohen, N. & Anselmi, F. Notes on Hierarchical Splines, DCLNs and i-theory. (2015).PDF icon CBMM Memo 037 (1.83 MB)
Anselmi, F. et al. Unsupervised learning of invariant representations. Theoretical Computer Science (2015). doi:10.1016/j.tcs.2015.06.048
2014
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>
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Speech Representations based on a Theory for Learning Invariances. (2014).
Anselmi, F. et al. Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?. (2014).PDF icon CBMM Memo No. 001 (940.36 KB)

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