We are often interested in clustering objects that evolve over time and identifying solutions to the problem for every step. Evolutionary provides insight into cluster evolution temporal changes memberships while enabling performance superior achieved by independently data collected at different points. In this article we introduce evolutionary affinity propagation (EAP), an algorithm groups po...

among different discretization approaches, finite difference method (fdm) is widely used for acoustic and elastic full-wave form modeling. an inevitable deficit of the technique, however, is its sever requirement to computational resources. a promising solution is parallelization, where the problem is broken into several segments, and the calculations are distributed over different processors. ...

In this note, I summarize Sections 5.1 and 5.2 of Arian Maleki’s PhD thesis. 1 Notation We denote scalars by small letters e.g. a, b, c, . . ., vectors by boldface small letters e.g. λ,α,x, . . ., matrices by boldface capital letter e.g. A,B,C, . . ., (subsets of) natural numbers by capital letters e.g. N,M, . . .. We denote i’th element of a vector a by ai and (i, j)’th entry of a matrix A by ...

Biclustering is the analog of clustering on a bipartite graph. Existent methods infer biclusters through local search strategies that find one cluster at a time; a common technique is to update the row memberships based on the current column memberships, and vice versa. We propose a biclustering algorithm that maximizes a global objective function using message passing. Our objective function c...

Handling concurrency using a shared memory and locks is tedious and error-prone. One solution is to use message passing instead. We study here a particular, contract-based flavor that makes the ownership transfer of messages explicit. In this case, ownership of the heap region representing the content of a message is lost upon sending, which can lead to efficient implementations. In this paper,...

Bayesian inference is now widely established as one of the principal foundations for machine learning. In practice, exact inference is rarely possible, and so a variety of approximation techniques have been developed, one of the most widely used being a deterministic framework called variational inference. In this paper we introduce Variational Message Passing (VMP), a general purpose algorithm...

In this paper, we present structured message passing (SMP), a unifying framework for approximate inference algorithms that take advantage of structured representations such as algebraic decision diagrams and sparse hash tables. These representations can yield significant time and space savings over the conventional tabular representation when the message has several identical values (context-sp...