Shahriar Shariat

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This paper presents an approach for sequence alignment based on canonical correlation analysis(CCA). We show that a novel set of constraints imposed on traditional CCA leads to canonical solutions with the time warping property , i.e., non-decreasing monotonicity in time. This formulation generalizes the more traditional dynamic time warping (DTW) solutions(More)
The problem of human activity recognition is a central problem in many real-world applications. In this paper we propose a fast and effective segmental alignment-based method that is able to classify activities and interactions in complex environments. We empirically show that such model is able to recover the alignment that leads to improved similarity(More)
Traditional pairwise sequence alignment is based on matching individual samples from two sequences , under time monotonicity constraints. However, in some instances matching two segments of points may be preferred and can result in increased noise robustness. This paper presents an approach to segmental sequence alignment based on adaptive pairwise(More)
An evolutionarily ancient skill we possess is the ability to distinguish between food and non-food. Our goal here is to identify the neural correlates of visually driven 'edible-inedible' perceptual distinction. We also investigate correlates of the finer-grained likability assessment. Our stimuli depicted food or non-food items with sub-classes of(More)
Online media provides opportunities for marketers through which they can deliver effective brand messages to a wide range of audiences at scale. Advertising technology platforms enable advertisers to reach their target audience by delivering ad impressions to online users in real time. In order to identify the best marketing message for a user and to(More)
Online media offers opportunities to marketers to deliver brand messages to a large audience. Advertising technology platforms enables the advertisers to find the proper group of audiences and deliver ad impressions to them in real time. The recent growth of the real time bidding has posed a significant challenge on monitoring such a complicated system.(More)
Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints. However, in many application settings, matching subsequences (segments) instead of individual samples may bring in additional robustness to noise or local non-causal perturbations. This paper presents an approach to(More)
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. ABSTRACT This paper presents a systematic application of machine learning(More)
Bayesian networks are popular in the classification literature. The simplest kind of Bayesian network, i.e. naïve Bayesian network, has gained the interest of many researchers because of quick learning and inferring. However, when there are lots of classes to be inferred from a similar set of evidences, one may prefer to have a united network. In this paper(More)
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