2012.baylearn.org
BayLearn: Bay Area Machine Learning Symposium 2012
http://2012.baylearn.org/home
The BayLearn Symposium aims at gathering scientists in machine learning from the San Francisco Bay Area. It promotes community building between local researchers from academic and industrial institutions but also welcomes visitors. This one-day event combines invited talks and posters to foster exchange of ideas. Only abstracts will be shared through an online repository. Our primary goal is to foster discussion! July 30th, 2012 - Abstract submission deadline. August 14th, 2012 - Author notification.
sites.google.com
learningsemantics2014
https://sites.google.com/site/learningsemantics2014
Montréal, Friday December 12th. Welcome to the NIPS 2014. Workshop on Learning Semantics! Understanding the semantic structure of unstructured data - text, dialogs, images - is a critical challenge given their central role in many applications, including question answering, dialog systems, information retrieval. In recent years, there has been much interest in designing models and algorithms to automatically extract and manipulate these semantic representations from raw data. University of Washington, Sy...
sites.google.com
usps_cnn - hpenedones
https://sites.google.com/site/hpenedones2/sourcecode/usps_cnn
Handwritten digit recognition with Convolutional Neural Networks. Language interpreter (which comes with torch). Suggested me this problem as a way of getting familiar with torch5 library (during my 2010 Summer internship at NEC Labs. Code: usps cnn.lua. For example, the images of zeros and eights look like this:. T), which keeps the responses of that layer bounded to [-1,1]. We then have a linear layer with 10 outputs (one for each digit). Finally, a SoftMax. Function create network(nb outputs). Is our ...
bengio.abracadoudou.com
NIPS'06 Workshop on Learning to Compare Examples - NIPS 2006 Workshop
http://bengio.abracadoudou.com/lce
Learning to Compare Examples. December 8, 2006, Whistler, BC, Canada. Topics of interest include, but are not limited to:. Algorithmic approaches for distance metric(*) learning,. Comparisons of distance metric learning(*) approaches,. Effect of distance metric(*) learning on retrieval/categorization models,. Learning a distance(*) robust to certain transformations,. Links between distance(*) learning and the ranking/categorization problem,. Criteria, loss bounds for distance(*) learning,. J Dillon, Y...
