Weakly Supervised Learning and Evidence Pinpointing
Weakly supervised evidence pinpointing and description
... a learning method to identify which specific regions and features of images contribute to a certain ...the evidence regions where the abnormalities are most likely to appear, and the discriminative features ...
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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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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
... We study further the asymmetric label noise setting and consider multi-class clas- sification with deep neural networks, including recurrent neural networks. Once more, the only component we operate on is the loss ...
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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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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 Algorithms and an Application to Electromyography
... for weakly supervised classifica- tion is introduced, where a limited number of available labelled instances (those belonging to normal bags of the muscle dataset) and a larger set of unlabelled instances ...
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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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Weakly Supervised Localization and Learning with Generic Knowledge
... In this pa- per, we explore a scenario where generic knowledge about object classes is first learned during a meta-training stage when images of many different classes are provided along[r] ...
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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 for Hedge Classification in Scientific Literature
... The work presented here has application in the wider academic community; in fact a key motivation in this study is to incorporate hedge classification into an interactive system for aiding curators in the con- struction ...
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Deep Residual Learning for Weakly Supervised Relation Extraction
... A major issue for relation extraction is the lack of labeled training data. In recent years, distant supervision (Mintz et al., 2009; Hoffmann et al., 2011; Surdeanu et al., 2012) emerges as the most popular method for ...
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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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Instance search based on weakly supervised feature learning
... result, weakly supervised object detection (WSOD), which only requires image level annotations, is ...instance learning (MIL) [37] ...complementary learning method is proposed to discover new ...
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Adversarial Learning for Weakly-Supervised Social Network Alignment
... into supervised, semi-supervised and unsuper- vised ...are supervised, which aim to train a binary classifier to separate the matched user identity pairs from the unmatched ones (Vosecky, Hong, and ...
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