Alessandro Achille PhD student
University of California, Los Angeles
achille at cs.ucla.edu
alexachi
Since Fall 2015, I am a PhD student at the Computer Science department of UCLA, working with Prof. Stefano Soatto in the Vision Lab. I have also been a research scientist intern at Deep Mind since November 2017. My research interests include information theory, variational inference, representation learning, deep learning and their applications to computer vision.
Before coming to UCLA, I obtained a Master in Pure Math at the Scuola Normale Superiore and the University of Pisa, where I studied model theory, algebraic topology, and their intersection with Prof. Alessandro Berarducci. During that period, I have also been a visiting student at the University of Leeds Math department.
Publications

Critical Learning Periods in Deep Neural NetworksArXiv preprint
@ARTICLE{achille2017critical, author = {{Achille}, A. and {Rovere}, M. and {Soatto}, S.}, title = "{Critical Learning Periods in Deep Neural Networks}", journal = {ArXiv eprints}, archivePrefix = "arXiv", eprint = {1711.08856}, primaryClass = "cs.LG", keywords = {Computer Science  Learning, Quantitative Biology  Neurons and Cognition, Statistics  Machine Learning}, year = 2017, month = nov, }

A Separation Principle for Control in the Age of Deep LearningTo appear in Annual Reviews of Control, Robotics and Autonomous Systems
@ARTICLE{achille2017separation, author = {{Achille}, A. and {Soatto}, S.}, title = "{A Separation Principle for Control in the Age of Deep Learning}", journal = {ArXiv eprints}, archivePrefix = "arXiv", eprint = {1711.03321}, primaryClass = "stat.ML", keywords = {Statistics  Machine Learning, Computer Science  Learning}, year = 2017, month = nov, adsurl = {http://adsabs.harvard.edu/abs/2017arXiv171103321A}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }

Emergence of Invariance and Disentangling in Deep RepresentationsProceedings of the ICML 2017 Workshop on Principled Approaches to Deep Learning
@ARTICLE{achille2017emergence, author = {{Achille}, A. and {Soatto}, S.}, title = "{Emergence of Invariance and Disentangling in Deep Representations}", journal = {Proceedings of the ICML Workshop on Principled Approaches to Deep Learning}, eprint = {1706.01350}, primaryClass = "cs.LG", keywords = {Computer Science  Learning, Computer Science  Artificial Intelligence, Statistics  Machine Learning}, year = 2017 }

Information Dropout: learning optimal representations through noisy computationTransactions on Pattern Analysis and Machine Intelligence (PAMI)
@ARTICLE{achille2018information, author={A. Achille and S. Soatto}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, title={Information Dropout: Learning Optimal Representations Through Noisy Computation}, year={2018}, volume={PP}, number={99}, pages={11}, keywords={Bayes methods;Information theory;Machine learning;Neural networks;Noise measurement;Training;Representation learning;deep learning;information bottleneck;invariants;minimality;nuisances}, doi={10.1109/TPAMI.2017.2784440}, ISSN={01628828}, month={},} }

A VietorisSmale mapping theorem for the homotopy of hyperdefinable setsArXiv preprint
@ARTICLE{achille2017vietoris, author = {{Achille}, A. and {Berarducci}, A.}, title = "{A VietorisSmale mapping theorem for the homotopy of hyperdefinable sets}", journal = {ArXiv eprints}, archivePrefix = "arXiv", eprint = {1706.02094}, primaryClass = "math.LO", keywords = {Mathematics  Logic}, year = 2017, month = jun }