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Article
Correction to: TurboLift: fast accuracy lifting for historical data recovery
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Article
TurboLift: fast accuracy lifting for historical data recovery
Historical data are frequently involved in situations where the available reports on time series are temporally aggregated at different levels, e.g., the monthly counts of people infected with measles. In real da...
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Article
TensorCast: forecasting and mining with coupled tensors
Given an heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to imp...
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Chapter and Conference Paper
GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid
Given sensor readings over time from a power grid consisting of nodes (e.g. generators) and edges (e.g. power lines), how can we most accurately detect when an electrical component has failed? More challenging...
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Chapter and Conference Paper
PowerCast: Mining and Forecasting Power Grid Sequences
What will be the power consumption of our institution at 8am for the upcoming days? What will happen to the power consumption of a small factory, if it wants to double (or half) its production? Technologies as...
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Chapter and Conference Paper
Matrices, Compression, Learning Curves: Formulation, and the GroupNteach Algorithms
Suppose you are a teacher, and have to convey a set of object-property pairs (‘lions eat meat’). A good teacher will convey a lot of information, with little effort on the student side. What is the best and mo...
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Article
Noise-Robust Detection of Symmetric Axes by Self-Correcting Artificial Neural Network
Perception of symmetric image patterns is one of the important stages in visual information processing. However, local interference of the input image disturbs the detection of symmetry in artificial neural ne...
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Chapter and Conference Paper
Flexible Reasoning of Boolean Constraints in Recurrent Neural Networks with Dual Representation
In this paper, we propose a recurrent neural network that can flexibly make inferences to satisfy given Boolean constraints. In our proposed network, each Boolean variable is represented in dual representation...
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Chapter and Conference Paper
Hierarchical Representation Using NMF
In this paper, we propose a representation model that demonstrates hierarchical feature learning using nsNMF. We stack simple unit algorithm into several layers to take step-by-step approach in learning. By ut...
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Article
A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech
In this study we propose a new feature extraction algorithm, dNMF (discriminant non-negative matrix factorization), to learn subtle class-related differences while maintaining an accurate generative capability...
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Chapter and Conference Paper
Self-correcting Symmetry Detection Network
In this paper, we propose a symmetry axis detection network that can correct asymmetric parts by itself. Our network compares directional blurring of omnidirectional image edges, which plays a significant role...
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Chapter and Conference Paper
Enhanced Discrimination of Face Orientation Based on Gabor Filters
In general, a face analysis relies on the face orientation; therefore, face orientation discrimination is very important for interpreting the situation of people in an image. In this paper, we propose an enhan...
