48 citations · 167 across the 10 of their papers we have counts for
10 papers
Eliminating Catastrophic Interference with Biased Competition
Amelia Elizabeth Pollard, Jonathan L. Shapiro
We present here a model to take advantage of the multi-task nature of complex datasets by learning to separate tasks and subtasks in and end to end manner by biasing competitive in…
Visual Question Answering as a Multi-Task Problem
Amelia Elizabeth Pollard, Jonathan L. Shapiro
Visual Question Answering(VQA) is a highly complex problem set, relying on many sub-problems to produce reasonable answers. In this paper, we present the hypothesis that Visual Que…
Thompson Sampling in Switching Environments with Bayesian Online Change Point Detection
Joseph Mellor, Jonathan Shapiro
Thompson Sampling has recently been shown to be optimal in the Bernoulli Multi-Armed Bandit setting[Kaufmann et al., 2012]. This bandit problem assumes stationary distributions for…
Bayesian Mixture Models for Frequent Itemset Discovery
Ruefei He, Jonathan Shapiro
In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability mode…
Effects of noise on convergent game learning dynamics
James B. T. Sanders, Tobias Galla, Jonathan Shapiro
We study stochastic effects on the lagging anchor dynamics, a reinforcement learning algorithm used to learn successful strategies in iterated games, which is known to converge to…
An associative memory for the on-line recognition and prediction of temporal sequences
J. Bose, S. B. Furber, J. L. Shapiro
This paper presents the design of an associative memory with feedback that is capable of on-line temporal sequence learning. A framework for on-line sequence learning has been prop…