
- Read more about DISTRIBUTED DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS
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We propose a distributed differentially-private canonical correlation analysis (CCA) algorithm to use on multi-view data. CCA finds a subspace for each view such that projecting the views onto these subspaces simultaneously reduces the dimension and maximizes correlation. In applications involving privacy-sensitive data, such as medical imaging, distributed privacy-preserving algorithms can let data holders maintain local control of their data while participating in joint computations with other data holders.
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- Read more about CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS
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Video transrating has become an essential task in streaming service providers that need to transmit and deliver different versions of the same content for a multitude of users that operate under different network conditions. As the transrating operation is comprised of a decoding and an encoding step in sequence, a huge computational cost is required in such large-scale services, especially when considering the use of complex state-of-the-art codecs, such as the High Efficiency Video Coding (HEVC).
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- Read more about Compressing Unstructured Mesh Data Using Spline Fits, Compressed Sensing, and Regression Methods
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Compressing unstructured mesh data from computer simulations poses several challenges that are not encountered in the compression of images or videos. Since the spatial locations of the points are not on a regular grid, as in an image, it is difficult to identify near neighbors of a point whose values can be exploited for compression.
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- Read more about Self-Supervised Anomaly Detection for Narrowband SETI Presentation
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- Read more about RANDOM ENSEMBLE OF LOCALLY OPTIMUM DETECTORS FOR DETECTION OF ADVERSARIAL EXAMPLES
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- Read more about Tensor Ensemble Learning
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In big data applications, classical ensemble learning is typically infeasible on the raw input data and dimensionality reduction techniques are necessary. To this end, novel framework that generalises classic flat-view ensemble learning to multidimensional tensor- valued data is introduced. This is achieved by virtue of tensor decompositions, whereby the proposed method, referred to as tensor ensemble learning (TEL), decomposes every input data sample into multiple factors which allows for a flexibility in the choice of multiple learning algorithms in order to improve test performance.
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- Read more about Backdoor Attacks on Neural Network Operations
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Machine learning is a rapidly growing field that has been expanding into various aspects of technology and science in recent years. Unfortunately, it has been shown recently that machine learning models are highly vulnerable to well-crafted adversarial attacks. This paper develops a novel method for maliciously inserting a backdoor into a well-trained neural network causing misclassification that is only active under rare input keys.
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- Read more about A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS ON FACES FROM DIFFERENT DOMAINS
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- Read more about SEMI-SUPERVISED LEARNING OF CAMERA MOTION FROM A BLURRED IMAGE
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We address the problem of camera motion estimation from a single blurred image with the aid of deep convolutional neural networks.
Unlike learning-based prior works that estimate a space-invariant blur kernel, we solve for the global camera motion which in turn
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