4 accepted papers
We study the problem of learning tree-structured Markov random fields (MRF) on discrete random variables with common support when the observations are corrupted by a k-ary symmetric noise channel with unknown probability of error. For Ising models (support size = 2), past work has shown that graph s…
The presence of outliers can potentially significantly skew the parameters of machine learning models trained via stochastic gradient descent (SGD). In this paper we propose a simple variant of the simple SGD method: in each step, first choose a set of k samples, then from these choose the one with…
We introduce Negative Sampling in Semi-Supervised Learning (NS^3L), a simple, fast, easy to tune algorithm for semi-supervised learning (SSL). NS^3L is motivated by the success of negative sampling/contrastive estimation. We demonstrate that adding the NS^3L loss to state-of-the-art SSL algorithms,…
Uneven illumination and shadows in document images cause a challenge for digitization applications and automated workflows. In this work, we propose a new method to recover unshadowed document images from images with shadows/un-even illumination. We pose this problem as one of estimating the shading…