weakly supervised learning
Advances in Weakly Supervised Learning of Morphology
... chapter discusses the weakly supervised learning of morphological segmentation in a semi-supervised setting with a small annotated data set and a large set of unannotated words.. We begi[r] ...
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Weakly supervised learning via statistical sufficiency
... summary Supervised learning is by far the most effective application of the machine learning ...consequence, learning is often performed with sparse, aggregated-level and/or noisy training ...
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Weakly Supervised Learning Algorithms and an Application to Electromyography
... the weakly supervised learning approach does not have this ...in learning, but this is due to the assumptions of the EMG muscle data that make clustering a sound ...the weakly ...
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Weakly Supervised Learning for Hedge Classification in Scientific Literature
... a weakly supervised learning per- ...on weakly supervised learning, where for in- stance in the case of text categorization (Blum and Mitchell, 1998; Nigam et ...
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Cross lingual Projected Expectation Regularization for Weakly Supervised Learning
... multilingual weakly supervised learning scenario where knowledge from an- notated corpora in a resource-rich language is transferred via bitext to guide the learning in other ...
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Weakly supervised learning of allomorphy
... Such information is rarely aligned to the relevant parts of the words—i.e. the al- lomorphs, as such annotation would be very costly. These unaligned weak label- ings are commonly provided by annotated NLP corpora such ...
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Weakly Supervised Learning for Semantic Segmentation
... generality across every class, at any given time, without concentrating on a particular training example or object class. The idea is that we eliminate the complicated black box of convolutional neural networks ...
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Weakly Supervised Learning of Objects and Attributes.
... of learning from only few weak annotations and a large volume of only partially relevant unla- belled ...indeed learning a good generalisable localisation mechanism and is not over-fitted to the training ...
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Sentence Subjectivity Detection with Weakly Supervised Learning
... as weakly-supervised generative model learning where the only input to the model is a small amount of domain independent subjective/neutral ...
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Weakly Supervised Learning for Unconstrained Face Processing
... deep learning to object recognition and face veri- fication, using a modification to binomial units that they refer to as noisy rectified linear ...make learning computationally tractable, they subsample ...
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A Weakly Supervised Learning Approach for Spoken Language Understanding
... weakly supervised training scenario with the pool size of 200, the active learning and self- training procedure ran 8 ...with supervised training for semantic classifica- ...the ...
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Weakly Supervised Learning of Presupposition Relations between Verbs
... a weakly supervised algorithm for learning presup- position relations between verbs that dis- tinguishes five semantic relations: presup- position, entailment, temporal inclusion, antonymy and ...
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Weakly Supervised Learning with Cost Augmented Contrastive Estimation
... Abstract We generalize contrastive estimation in two ways that permit adding more knowl- edge to unsupervised learning. The first allows the modeler to specify not only the set of corrupted inputs for each ...
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Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions
... Although many of these challenges have been studied separately, as we will review in Section 3, this paper represents, to the best of our knowledge, the first attempt at a comprehensive model that ad- dresses them all. ...
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Weakly supervised learning of biomedical information extraction from curated data.
... The large number of curated biomedical databases avail- able in the public domain provides an unprecedented opportunity to train NLP systems to comprehend biomed- ical publications. In this paper, we present an approach ...
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Weakly Supervised Bayesian Learning of a CCG Supertagger
... ‡ Department of Linguistics, The University of Texas at Austin ∗ Corresponding author: [email protected] Abstract We present a Bayesian formulation for weakly-supervised learning of a Combina- tory ...
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Adversarial Learning for Weakly-Supervised Social Network Alignment
... a supervised model, ULink achieves an undesirable perfor- mance in the weakly-supervised learning setting as it needs a large portion of annotations ...
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Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
... Standard supervised training requires bounding box annotations of object ...in weakly supervised learning. In this case, the supervised information is restricted to binary labels that ...
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Weakly-Supervised Reinforcement Learning for Controllable Behavior
... We build upon the work of Shu et al. [68] for learning disentangled representations, though other methods could be used. Their method trains an encoder, generator, and discriminator by optimizing the losses in Eq. ...
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