177,585 research outputs found
Learning a Policy for Opportunistic Active Learning
Active learning identifies data points to label that are expected to be the
most useful in improving a supervised model. Opportunistic active learning
incorporates active learning into interactive tasks that constrain possible
queries during interactions. Prior work has shown that opportunistic active
learning can be used to improve grounding of natural language descriptions in
an interactive object retrieval task. In this work, we use reinforcement
learning for such an object retrieval task, to learn a policy that effectively
trades off task completion with model improvement that would benefit future
tasks.Comment: EMNLP 2018 Camera Read
Order and disorder in everyday action: the roles of contention scheduling and supervisory attention
This paper describes the contention scheduling/supervisory attentional system approach to action selection and uses this account to structure a survey of current theories of the control of action. The focus is on how such theories account for the types of error produced by some patients with frontal and/or left temporoparietal damage when attempting everyday tasks. Four issues, concerning both the theories and their accounts of everyday action breakdown, emerge: first, whether multiple control systems, each capable of controlling action in different situations, exist; second, whether different forms of damage at the neural level result in conceptually distinct disorders; third, whether semantic/conceptual knowledge of objects and actions can be dissociated from control mechanisms, and if so what computational principles govern sequential control; and fourth, whether disorders of everyday action should be attributed to a loss of semantic/conceptual knowledge, a malfunction of control, or some combination of the two
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Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose.
The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients ("Nutrient-Only") or the nutrient and food descriptions ("Nutrient + Text"). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
Attribute recognition, particularly facial, extracts many labels for each
image. While some multi-task vision problems can be decomposed into separate
tasks and stages, e.g., training independent models for each task, for a
growing set of problems joint optimization across all tasks has been shown to
improve performance. We show that for deep convolutional neural network (DCNN)
facial attribute extraction, multi-task optimization is better. Unfortunately,
it can be difficult to apply joint optimization to DCNNs when training data is
imbalanced, and re-balancing multi-label data directly is structurally
infeasible, since adding/removing data to balance one label will change the
sampling of the other labels. This paper addresses the multi-label imbalance
problem by introducing a novel mixed objective optimization network (MOON) with
a loss function that mixes multiple task objectives with domain adaptive
re-weighting of propagated loss. Experiments demonstrate that not only does
MOON advance the state of the art in facial attribute recognition, but it also
outperforms independently trained DCNNs using the same data. When using facial
attributes for the LFW face recognition task, we show that our balanced (domain
adapted) network outperforms the unbalanced trained network.Comment: Post-print of manuscript accepted to the European Conference on
Computer Vision (ECCV) 2016
http://link.springer.com/chapter/10.1007%2F978-3-319-46454-1_
Support Vector Regression Based S-transform for Prediction of Single and Multiple Power Quality Disturbances
This paper presents a novel approach using Support Vector Regression (SVR) based
S-transform to predict the classes of single and multiple power quality disturbances in a
three-phase industrial power system. Most of the power quality disturbances recorded in an
industrial power system are non-stationary and comprise of multiple power quality
disturbances that coexist together for only a short duration in time due to the contribution
of the network impedances and types of customers’ connected loads. The ability to detect
and predict all the types of power quality disturbances encrypted in a voltage signal is vital
in the analyses on the causes of the power quality disturbances and in the identification of
incipient fault in the networks. In this paper, the performances of two types of SVR based
S-transform, the non-linear radial basis function (RBF) SVR based S-transform and the
multilayer perceptron (MLP) SVR based S-transform, were compared for their abilities in
making prediction for the classes of single and multiple power quality disturbances. The
results for the analyses of 651 numbers of single and multiple voltage disturbances gave
prediction accuracies of 86.1% (MLP SVR) and 93.9% (RBF SVR) respectively.
Keywords: Power Quality, Power Quality Prediction, S-transform, SVM, SV
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