[PDF] Top 20 Towards Automated Semi-Supervised Learning

... the learning process, which do not finalize a systematical solution and are not automated ...an automated learning system ( AUTO - SSL ) for ... See full document

... All experiments start from a seed set of 20 ran- domly selected examples and, in each iteration, 50 new examples are selected using AL. The ef- ficiency of the different selection mechanisms is determined by ... See full document

... While end-to-end neural machine transla- tion (NMT) has made remarkable progress recently, NMT systems only rely on par- allel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, ... See full document

... In this work, we empirically investigate whether we can successfully apply SSL (i.e., whether we can achieve better performance with SSL than with supervised learning) to build predictive QSAR models. To ... See full document

... We propose a sequence labeling frame- work with a secondary training objec- tive, learning to predict surrounding words for every word in the dataset. This lan- guage modeling objective incentivises the system to ... See full document

... a semi-supervised learning approach that uses level-wise feature engineering to construct an abbreviation identifier, from using a small set of labeled clinical texts and exploiting a larger set of ... See full document

... at learning time. We argue that novelty detection in this semi-supervised setting is naturally solved by a general reduction to a binary classification ...approach, semi-supervised ... See full document

... fully supervised methods for learning to extract such facts from text work well, the cost of collecting many labeled examples of each type of knowledge to be extracted is ...explored ... See full document

... state-of-the-art supervised mod- els, it has some disadvantages, which made them be out-performed by simpler Brown cluster fea- tures (Turian et al, 2010) and made them computa- tionally ... See full document

... In contrast to many machine learning ap- proaches that address the problem of paradigm ex- traction, the current method is intended to produce human-readable output of its generalizations. That is, the paradigms ... See full document

... Neural Semi-supervised ...problem, semi-supervised learning has been investigated to incorporate unlabeled data for modeling training (Dai and Le, 2015; Ramachan- dran et ...Multi-task ... See full document

... It is crucial for applications to add support for new functionalities and improve them con- tinuously. An efficient method for this is semi- supervised learning (SSL), where the model learns from ... See full document

... tain information about each other. This has been shown by means of several toy examples (Janzing and Sch¨ olkopf, 2010; Daniusis et al., 2010; Janzing et al., 2012) using appropriate formalizations of the independence ... See full document

... Our semi-supervised approach consists of two stages of ...multiple learning iterations of co-train- ing are applied in order to increase the size of the la- beled set for that experiment, in order to ... See full document

... the semi-supervised ...safe semi- supervised ...any semi-supervised learning strategy that is not worse than the supervised classiier for all possible labelings of ... See full document

... tive clustering, a single group containing all the data, P = {1}. A small number of random points are drawn from this cluster and queried; these initial labels provide the first indication of the underlying distribution ... See full document

... Supervised learning methods have been widely applied to ac- tivity ...Distribution-based Semi-Supervised Learning, to tackle the aforementioned ...through semi-supervised ... See full document

... Graph-based semi-supervised learning (SSL) methods play an increasingly important role in prac- tical machine learning systems, particularly in agnostic settings when no parametric information ... See full document

... The first data set is very clean speech data drawn from a collection of read speech collected under lab- oratory conditions by (Xu, 1999). In these mate- rials, speakers read a set of short sentences where syllable tone ... See full document

... Stacking features. We also evaluated a stacked ar- chitecture in which we use our model’s predictions as an additional feature to improve the state-of-the- art Twitter POS tagger of Owoputi et al. (2013). This system is ... See full document