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Functional brain connectivity in patients with <i>de novo</i> Parkinson's disease. [PDF]
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LiSSA: Localized Stochastic Sensitive Autoencoders
IEEE Transactions on Cybernetics, 2021The training of autoencoder (AE) focuses on the selection of connection weights via a minimization of both the training error and a regularized term. However, the ultimate goal of AE training is to autoencode future unseen samples correctly (i.e., good generalization).
Ting Wang +3 more
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Kernelized Locality-Sensitive Hashing
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012Fast retrieval methods are critical for many large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance functions into a low-dimensional Hamming space where items can be efficiently searched.
Brian, Kulis, Kristen, Grauman
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Ensemble sensitivity localization
2023Ensemble sensitivity is a tool to quantitatively determine which initial conditions influence a forecast quantity of choice. This information can then be used to understand the sources and dynamics of forecast uncertainty, quantify the impact of observations (e.g., E-FSOI), and determine where to best deploy observations to improve the forecast (e.g ...
Philipp Johannes Griewank +2 more
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1996
Abstract We review several methods for assessing the effect of small changes to the prior distribution. Our emphasis is on a variety of derivative-like quantities. Some of these have deficiencies that make them unsuitable as diagnostics. We explore the reasons for this and we look at some attempts to avoid these problems.
P Gustafson* +2 more
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Abstract We review several methods for assessing the effect of small changes to the prior distribution. Our emphasis is on a variety of derivative-like quantities. Some of these have deficiencies that make them unsuitable as diagnostics. We explore the reasons for this and we look at some attempts to avoid these problems.
P Gustafson* +2 more
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Locality-Sensitive Hashing for Chi2 Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012In the past 10 years, new powerful algorithms based on efficient data structures have been proposed to solve the problem of Nearest Neighbors search (or Approximate Nearest Neighbors search). If the Euclidean Locality Sensitive Hashing algorithm, which provides approximate nearest neighbors in a euclidean space with sublinear complexity, is probably ...
Gorisse, David +2 more
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Locality sensitive hashing revisited
Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013Locality Sensitive Hashing (LSH) is widely recognized as one of the most promising approaches to similarity search in high-dimensional spaces. Based on LSH, a considerable number of nearest neighbor search algorithms have been proposed in the past, with some of them having been used in many real-life applications. Apart from their demonstrated superior
Hongya Wang +3 more
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Distributed Locality Sensitivity Hashing
2010 7th IEEE Consumer Communications and Networking Conference, 2010In this paper, we present DLSH Distributed Locality Sensitive Hashing, a similar-data search technology. The huge growth in the size of video content has broken the traditional multi-media index hosting and look-up solutions, these are not able to scale to the size of the current and projected index requirements.
Smita Wadhwa, Pawan Gupta
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Fast locality-sensitive hashing
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011Locality-sensitive hashing (LSH) is a basic primitive in several large-scale data processing applications, including nearest-neighbor search, de-duplication, clustering, etc. In this paper we propose a new and simple method to speed up the widely-used Euclidean realization of LSH.
Anirban Dasgupta +2 more
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