A two-step procedure for weakly supervised algorithms
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 Definition Extraction
... prior step for constructing taxonomies, ontologies, auto- matic glossaries or dictionary ...a weakly supervised bootstrapping approach for identi- fying textual definitions with higher linguis- tic ...
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Weakly Supervised Learning for Semantic Segmentation
... the two-dimensional perceptron makes this ...of algorithms to improve the weakly supervised performance of CNNs that do not hold as explicit of convergence ...
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Weakly supervised evidence pinpointing and description
... the two classification tasks are shown in ...with supervised methods, trained on the same densely annotated subset but requires no landmarks to be ...on weakly annotated ...labelling step is ...
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Weakly supervised learning via statistical sufficiency
... from label proportions, where nothing is known about the target variable but its pro- portion over subsets of the training set, the bags. This setting is inspired by several applications where individual labels are not ...
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Weakly supervised POS tagging without disambiguation
... 3. THE PROPOSED APPROACH Assuming a full list of POS tags O and a dictionary of words and their corresponding possible POS tags D, we aim to predict the POS tag for a given word w in a sentence. Firstly, each word w in ...
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A convex relaxation for weakly supervised relation extraction
... expensive step to compute those gra- dients is to compute the matrix product XX > ...these two operations is O(NF K), where F is the average number of fea- tures per relation mention ...
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Iterative Search for Weakly Supervised Semantic Parsing
... iterative procedure that alternates between statically-computed and dynamically-computed training ...these two contributions greatly improve seman- tic parsing performance, leading to new state-of- the-art ...
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Weakly-Supervised Reinforcement Learning for Controllable Behavior
... We highlight two potential impacts for this work. Most immediately, weak supervision from humans may be an inexpensive yet effective step towards human-AI alignment [34, 55]. While prior work [3, 11, 42] ...
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Weakly supervised object detection with posterior regularization
... Discriminative supervised classifiers have been proven to be very effective and accurate tools for learning the correlation between input and precisely annotated ...proposes weakly supervised ...
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Weakly Supervised Learning for Unconstrained Face Processing
... Figure 2.1: Sample images from LFW (first row), FRGC (second row), and BioID (third row), representative of variation within each database (best viewed in color). On the other hand, in order to study more general, ...
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Detecting Online Hate Speech Using Both Supervised and Weakly-Supervised Approaches
... Annotation Procedure We identified two native English speakers for annotating online user ...The two anno- tators first discussed and practices before they started ...the two annotators to ...
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Education. Admission to the Teacher Education Program: Step One. Admission to Supervised Internship (Student Teaching): Step Two
... Admission to the Teacher Education Program: Step One There is a formal admission procedure to the teacher education program. A student is admitted to the program upon completion of an official application ...
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Weakly supervised activity analysis with spatio-temporal localisation
... In order to demonstrate the proposed methods’ capability of removing noisy instances, a qualitative analysis of the effectiveness of instance selec- tion is conducted. First, we compare the recognition performance ...
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Weakly supervised deep learning for the detection of domain generation algorithms
... a weakly labeled dataset, referring to the fact that the labels are noisy ...a weakly super- vised ...the weakly labeled data to train DGA detectors that can classify new domain names as malicious or ...
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Weakly supervised learning of allomorphy
... The fact that both R-W and the perceptron al- gorithm seem to perform poorly for labeled mor- pheme identification can be explained by the fact that both algorithms are trained to predict each of the morphological ...
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Weakly-supervised text classification
... Motivated by the enjoyable properties of deep neural networks, we explore using deep neural networks for hierarchical text classification. Despite the success of deep neural models in flat text classification and their ...
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Weakly Supervised Training of Semantic Parsers
... One limitation of our method is the reliance on hand-built dependency parse patterns for lexicon ini- tialization. Although these patterns capture a va- riety of linguistic phenomena, they require manual engineering and ...
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Weakly Supervised Approaches for Ontology Population
... new weakly super- vised approach for Ontology Population, called Class-Example, and confronted it with two other ...presented weakly supervised Class-Example approach re- quires as a training ...
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Weakly Supervised Learning of Objects and Attributes.
... solving two sub-problems simultaneously: 1) locating the objects of interest in each training image, 2) training an object detector based on the annotation results from ...
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