On the probability distribution of distributed optimization strategies
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
This paper provides an efficient method for analyzing the error probability of the belief propagation (BP) decoder applied to LT Codes. Each output symbol is generated independently by sampling from a distribution and adding the input symbols corresponding ...
We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...
We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...
Distributed processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in sensor networks. In this paper, we address the issue of determining a initial probability distribution of multiple target states in ...
We propose a very fast and accurate algorithm for pricing swaptions when the underlying term structure dynamics are affine. The efficiency of the algorithm stems from the fact that the moments of the underlying asset (i.e., a coupon bond) possess simple cl ...
Two distinct methods underlying the existence of power-law distribution were studied. The distribution ws stationary under the process of merging and splitting of classes and the distribution of the entities under study was invariant. An explanation for th ...
In this paper, we discuss some of the issues that arise with the computation of the implied value of travel-time savings in the case of discrete choice models allowing for random taste heterogeneity. We specifically look at the case of models producing a n ...
We present a general method for maintaining estimates of the distribution of parameters in arbitrary models. This is then applied to the estimation of probability distribution over actions in value-based reinforcement learning. While this approach is simil ...
This paper introduces an approach to wavelet packet best basis searches using the generalized Renyi entropy. The approach extends on work by Coifman and Wickerhauser who showed how Shannon entropy can be used as an additive cost function in the wavelet pac ...
We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...
