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Found 10 papers, 1 papers with code
Egocentric Deep Multi-Channel Audio-Visual Active Speaker Localization
We propose a novel end-to-end deep learning approach that is able to give robust voice activity detection and localization results.
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Reframing Neural Networks: Deep Structure in Overcomplete Representations
In comparison to classical shallow representation learning techniques, deep neural networks have achieved superior performance in nearly every application benchmark.
Architectural Adversarial Robustness: The Case for Deep Pursuit
Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise.
Dataless Model Selection with the Deep Frame Potential
Choosing a deep neural network architecture is a fundamental problem in applications that require balancing performance and parameter efficiency.
Deep Component Analysis via Alternating Direction Neural Networks
Despite a lack of theoretical understanding, deep neural networks have achieved unparalleled performance in a wide range of applications.
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning
However, methods for subspace learning from subspace-valued data have been notably absent due to incompatibilities with standard problem formulations.
Additive Component Analysis
Principal component analysis (PCA) is one of the most versatile tools for unsupervised learning with applications ranging from dimensionality reduction to exploratory data analysis and visualization.
Blockout: Dynamic Model Selection for Hierarchical Deep Networks
Most deep architectures for image classification--even those that are trained to classify a large number of diverse categories--learn shared image representations with a single model.
Building Dynamic Cloud Maps From the Ground Up
Satellite imagery of cloud cover is extremely important for understanding and predicting weather.
Semantic Component Analysis
If weakly-supervised information is available in the form of image-level tags, SCA factorizes a set of images into semantic groups of superpixels.
