Computer Science

Permanent URI for this collectionhttps://hdl.handle.net/10679/43

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Browsing by Subject "Active learning"

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    OCR-aided person annotation and label propagation for speaker modeling in TV shows
    (IEEE, 2016) Budnik, M.; Besacier, L.; Khodabakhsh, Ali; Demiroğlu, Cenk; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Khodabakhsh, Ali
    In this paper, we present an approach for minimizing human effort in manual speaker annotation. Label propagation is used at each iteration of an active learning cycle. More precisely, a selection strategy for choosing the most suitable speech track to be labeled is proposed. Four different selection strategies are evaluated and all the tracks in a corresponding cluster are gathered using agglomerative clustering in order to propagate human annotations. To further reduce the manual labor required, an optical character recognition system is used to bootstrap annotations. At each step of the cycle, annotations are used to build speaker models. The quality of the generated speaker models is evaluated at each step using an i-vector based speaker identification system. The presented approach shows promising results on the REPERE corpus with a minimum amount of human effort for annotation.
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    Variational closed-Form deep neural net inference
    (Elsevier, 2018-09) Kandemir, Melih; Computer Science; KANDEMİR, Malih
    We introduce a Bayesian construction for deep neural networks that is amenable to mean field variational inference that operates solely by closed-form update rules. Hence, it does not require any learning rate to be manually tuned. We show that by this virtue it becomes possible with our model to perform effective deep learning on three setups where conventional neural nets are known to perform suboptimally: i) online learning, ii) learning from small data, and iii) active learning. We compare our approach to earlier Bayesian neural network inference techniques spanning from expectation propagation to gradient-based variational Bayes, as well as deterministic neural nets with various activations functions. We observe our approach to improve on all these alternatives in two mainstream vision benchmarks and two medical data sets: diabetic retinopathy screening and exudate detection from eye fundus images.