Weakly Supervised and Unsupervised Category Learning
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 pedestrian detector training by unsupervised prior learning and cue fusion in videos
... pedestrian detector given only C without being provided any bounding box annotations (which the traditional supervised training requires). Our algorithm is made up of 3 stages. In the 1 st stage, we learn a ...
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Unsupervised Learning and Data Mining. Unsupervised Learning and Data Mining. Clustering. Supervised Learning. Supervised Learning
... ó ó Use a program developed by chemists ( Use a program developed by chemists (fortran fortran) to ) to convert 3-D atom coordinates into average atomic convert 3-D atom coordinates into[r] ...
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Supervised & unsupervised transfer learning
... lem by using the randomized low-rank approximation technique according to [Vemp 04, Bela 07], cf. Section 6.1.4, which effectively translates D into a matrix ˜ D which is of negative type. The Ewens process model makes it ...
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Supervised and unsupervised learning - 1
... 3 Supervised and unsupervised learning - 1 ...of learning plays a key role in the field of statistics, data mining, artificial intelligence, intersecting with areas in engineering, finance and ...
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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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Supervised and Unsupervised Learning for Sentence Compression
... In this way, we approximate P expand (l | s) with- out parallel data. Since some of these “training” pairs are likely to be fairly poor compressions, due to the artifi- ciality of the construction, we restrict generation ...
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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
... for weakly-supervised learning of a Combina- tory Categorial Grammar (CCG) supertag- ger with an ...common category structures as well as transitions between tags that can combine locally ...
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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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Unsupervised Learning of Word Category Guessing Rules
... At the rule extraction phase, three sets of word-guessing rules (morphological prefix guessing rules, morpho- logical suffix guessing rules and ending-guessing rules[r] ...
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Supervised and Unsupervised Transfer Learning for Question Answering
... the unsupervised transfer learning process of QACNN, we visualize the changes of the word-level attention map during training Epoch 1, 4, 7, and 10 in Figure ...
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Neuromorphic Learning Systems for Supervised and Unsupervised Applications
... It is an emerging topic of learning to bridge the gap between image and natural languages. Some works [55, 63, 92] have focused on generating novel captions from query images. A typical pipeline in Vinyals et al. ...
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