[[start]]
Welcome
I am a research scientist with broad interests in practical and theoretical machine learning. My work on large scale learning and stochastic gradient algorithms has received attention in the recent years. I am also known for the DjVu document compression system.
Use the sidebar to navigate this site.
News
Semantic Extraction with a Neural Network Architecture
It is the nineties again. Ronan Collobert from NEC Labs just released a noncommercial version of his neural network system for semantic extraction. Given an input sentence in plain english, Senna outputs a host of Natural Language Processing (NLP) tags: part-of-speech (POS) tags, chunking (CHK), name entity recognition (NER), and semantic role labeling (SRL). Senna does this with state-of-the-art accuracies, roughly two hundred times faster than competing approaches.
The Senna source code represents about 2000 lines of C. This is probably one thousand times smaller than your usual natural language processing program. In fact all the Senna tagging tasks are performed using the same neural network simulation code.
Download Senna here. A Senna paper has been submitted to JMLR.
SGDQN
The SGDQN paper has been published on the JMLR site. This variant of stochastic gradient got very good results during the first PASCAL Large Scale Learning Challenge. The paper gives a lot of explanation on the design of the algorithm. Source code is available from Antoine's web site.
ICML 2009
ICML 2009 took place in June.
Michael Littman and I were the program co-chairs.
Since we were expecting a lot of work, we tried to make it interesting
by experimenting with a number of changes in the review process.
Read more for a little explanation and a few conclusions…
OLaRank Implementation Released
Antoine Bordes provides an implementation of the OLaRank algorithm.
OLaRank is an online solver of the dual formulation of support vector machines for structured output spaces. The algorithm can use exact or greedy inference. Its running time scales linearly with the data size, competitive with a perceptron based on the same inference procedure. Its accuracy however is much better as it replicates the accuracy of a structured SVM. See the ECML/PKDD paper "Sequence Labelling SVMs Trained in One Pass" for details.
