In models that define probabilities via energies, maximum likelihood learning typically involves using Markov Chain Monte Carlo to sample from the model’s distribution. If the ...
We present an extensive analysis of long-term statistics of the queries to websites using logs collected on several web caches in Russian academic networks and on US IRCache cache...
Serge A. Krashakov, Anton B. Teslyuk, Lev N. Shchu...
The method presented here is aimed to a direct fast setting of the parameters of a RBF network for function approximation. It is based on a hierarchical gridding of the input spac...
Some online algorithms for linear classification model the uncertainty in their weights over the course of learning. Modeling the full covariance structure of the weights can prov...
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer...
We consider applications scenarios where an untrusted aggregator wishes to continually monitor the heavy-hitters across a set of distributed streams. Since each stream can contain ...