http://leon.bottou.org/ 2010-07-18T07:06:22-04:00 http://leon.bottou.org/ http://leon.bottou.org/lib/images/favicon.ico text/html 2007-11-08T15:06:57-04:00 http://leon.bottou.org/talks/cookbook?rev=1194552417&do=diff This lecture co-authored with Yann Le Cun took place at the 1996 NIPS Workshop Tricks of the Trade organized by Klaus-Robert Müller and Genevieve Orr. * See the slides (djvu 199KB) (pdf 2.7MB). * See the corresponding book chapter. text/html 2007-11-08T15:03:06-04:00 http://leon.bottou.org/talks/djvu?rev=1194552186&do=diff DjVu is a document compression system that allows the distribution of scanned documents on the web. DjVu files are very compact. A typical 300dpi bitonal page takes 10-15KB. A typical 300dpi color page takes 40-60KB. The presentation discusses the main technical innovations that made DjVu possible. text/html 2007-11-08T15:01:55-04:00 http://leon.bottou.org/talks/gtn?rev=1194552115&do=diff This lecture describe Graph Transformer Networks It took place at the 2001 ICML workshop Machine Learning for Spatial and Temporal Data organized by Tom Dietterich. Graph Transformer Networks are one of the most powerful and successful method for learning sequential data. About 10% to 20% of the checks written in the U.S. since 1996 have been processed by a Graph Transformer Network. Graph Transformer Networks are related to Conditional Random Fields but have variable geometry and non-linear en… text/html 2010-06-01T14:51:35-04:00 http://leon.bottou.org/talks/largescale?rev=1275418295&do=diff This lecture was prepared for the NIPS 2007 tutorial. Variants of this lecture were given at IPAM Google, Microsoft Research, the International Conference of Nonconvex Programming (NCP'07), the Conference Francophone sur l'Apprentissage Automatique (CAP'08), the NIPS workshop Optimization for Machine Learning (OPT'08) Symposium on Learning and Data Sciences (SLDS'09), and the International Symposium of Mathematical Programming (ISMP'09) text/html 2007-11-08T15:03:44-04:00 http://leon.bottou.org/talks/mlss?rev=1194552224&do=diff This four part lecture was given at the Machine Learning Summer School held in Tübingen in 2003 organized by Olivier Bousquet, Bernhard Schölkopf and Ulrike von Luxburg. The lecture discusses Stochastic Approximations and in particular Stochastic Gradient Descent applied to online learning algorithms.