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They can be used for non-linear regression, time-series modelling, classification, and many other problems.
Differentially private database release via kernel mean embeddings. We lay theoretical foundations for new database release mechanisms that allow third-parties to construct consistent estimators of population statistics, while ensuring that the privacy of each individual contributing to the database is protected.
The proposed framework rests on two main ideas. First, releasing an estimate of the kernel mean embedding of the data generating random variable instead of the database itself still allows third-parties to construct consistent estimators of a wide class of population statistics.
Second, the algorithm can satisfy the definition of differential privacy by basing the released kernel mean embedding on entirely synthetic data points, while controlling accuracy through the metric available in a Reproducing Kernel Hilbert Space. We describe two instantiations of the proposed framework, suitable under different scenarios, and prove theoretical results guaranteeing differential privacy of the resulting algorithms and the consistency of estimators constructed from their outputs.
Scalable magnetic Speech coding thesis slam in 3d using gaussian process maps. We present a method for scalable and fully 3D magnetic field simultaneous localisation and mapping SLAM using local anomalies in the magnetic field as a source of position information. These anomalies are due to the presence of ferromagnetic material in the structure of buildings and in objects such as furniture.
We represent the magnetic field map using a Gaussian process model and take well-known physical properties of the magnetic field into account. We build local magnetic field maps using three-dimensional hexagonal block tiling. To make our approach computationally tractable we use reduced-rank Gaussian process regression in combination with a Rao-Blackwellised particle filter.
We show that it is possible to obtain accurate position and orientation estimates using measurements from a smartphone, and that our approach provides a scalable magnetic SLAM algorithm in terms of both computational complexity and map storage.
Antithetic and Monte Carlo kernel estimators for partial rankings. In the modern age, rankings data is ubiquitous and it is useful for a variety of applications such as recommender systems, multi-object tracking and preference learning.
However, most rankings data encountered in the real world is incomplete, which prevents the direct application of existing modelling tools for complete rankings. Our contribution is a novel way to extend kernel methods for complete rankings to partial rankings, via consistent Monte Carlo estimators for Gram matrices: We also present a novel variance reduction scheme based on an antithetic variate construction between permutations to obtain an improved estimator for the Mallows kernel.
The corresponding antithetic kernel estimator has lower variance and we demonstrate empirically that it has a better performance in a variety of Machine Learning tasks. Both kernel estimators are based on extending kernel mean embeddings to the embedding of a set of full rankings consistent with an observed partial ranking.
For wireless remote access security, forensics, border control and surveillance applications, there is an emerging need for biometric speaker recognition systems to be robust to speech coding distortion. This thesis examines the robustness issue for three coders, namely, the ITU-T kilobits per second (kbps) G, the ITU-T 8 kbps . Representing Voiced Speech Using Prototype Waveform Interpolation for Low Rate Speech Coding. timberdesignmag.com Thesis, October Amir K. Khandani. Shaping Multi-dimensional Signal Spaces. Ph.D. Thesis, May Majid Foodeei. Low-Delay Speech Coding at 16 kb/s and Below. ALABAMA LANGUAGE Official Language of the Alabama-Coushatta Tribe of Texas. Over "" (U.S. Census - Supplementary Table - Native North American Languages - Conducted - Released December ) Speakers in the U.S. Writing System: Latin .
They form a computationally tractable alternative to previous approaches for partial rankings data. An overview of the existing kernels and metrics for permutations is also provided. Streaming sparse Gaussian process approximations.
Sparse approximations for Gaussian process models provide a suite of methods that enable these models to be deployed in large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which the posterior distribution over function values and the hyperparameters are updated in an online fashion.
The small number of existing approaches either use suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from catastrophic forgetting or slow updating when new data arrive. This paper develops a new principled framework for deploying Gaussian process probabilistic models in the streaming setting, providing principled methods for learning hyperparameters and optimising pseudo-input locations.
The proposed framework is experimentally validated using synthetic and real-world datasets.
The first two authors contributed equally. The unreasonable effectiveness of structured random orthogonal embeddings.This is the text of my keynote speech at the 34th Chaos Communication Congress in Leipzig, December (You can also watch it on YouTube, but it runs to about 45 minutes.).
Moving average vector quantization in speech coding This Master’s Thesis has been submitted for official examination for the degree of Master of Science in Espoo on January 27, Supervisor of the Thesis: Professor Matti Karjalainen Instructor of the Thesis: Vesa Ruoppila, timberdesignmag.com The Bellman Award is given for distinguished career contributions to the theory or application of automatic control.
It is the highest recognition of professional achievement for US control systems engineers and scientists. The Call for Code Global Initiative is a rallying cry to developers to use their skills and mastery of the latest technologies to drive positive and long-lasting change across the world with their code.
For , the Call for Code Global Challenge asks developers to create solutions that significantly improve preparedness for natural disasters and relief when they hit in order to safeguard the.
Bernstein believes that a coding principle is, "a rule governing what to say and how to say it in a particular context." Lisa Coutu, an ethnographer, helped to formulate the second proposition of Speech Codes Theory. This proposition states that within any given speech community, there are multiple speech codes.
speaker-dependent speech coding a thesis submitted to the department of electrical and electronics engineering and the institute of engineering and sciences of bilkent university in partial fullfilment of the requirements for the .