We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
We propose a novel bound on single-variable marginal probability distributions in factor graphs with discrete variables. The bound is obtained by propagating local bounds (convex ...
A (randomized, anonymous) voting rule maps any multiset of total orders (aka. votes) over a fixed set of alternatives to a probability distribution over these alternatives. A voti...
We present an algorithm to generate samples from probability distributions on the space of curves. Traditional curve evolution methods use gradient descent to find a local minimum...
Ayres C. Fan, John W. Fisher III, Jonathan Kane, A...
: Let (C1, C1), (C2, C2), . . . , (Cm, Cm) be a sequence of ordered pairs of 2CNF clauses chosen uniformly at random (with repetition) from the set of all 4 n 2 clauses on n variab...
We consider a mobile sensor network monitoring a spatio-temporal field. Given limited caches at the sensor nodes, the goal is to develop a distributed cache management algorithm to...
Hany Morcos, George Atia, Azer Bestavros, Ibrahim ...
This paper is an argument for two assertions: First, that by representing correspondence probabilistically, drastically more correspondence information can be extracted from image...
Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
A token is hidden in one out of n boxes following some known probability distribution and then all the boxes are locked. The goal of a searcher is to find the token in at most D n...