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Results 1 - 10
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7,663
On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
- STATISTICS AND COMPUTING
, 2000
"... In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is develop ..."
Abstract
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Cited by 1051 (76 self)
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is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously
Software Transactional Memory
, 1995
"... As we learn from the literature, flexibility in choosing synchronization operations greatly simplifies the task of designing highly concurrent programs. Unfortunately, existing hardware is inflexible and is at best on the level of a Load Linked/Store Conditional operation on a single word. Building ..."
Abstract
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Cited by 695 (14 self)
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As we learn from the literature, flexibility in choosing synchronization operations greatly simplifies the task of designing highly concurrent programs. Unfortunately, existing hardware is inflexible and is at best on the level of a Load Linked/Store Conditional operation on a single word. Building
Combined Object Categorization and Segmentation With An Implicit Shape Model
- In ECCV workshop on statistical learning in computer vision
, 2004
"... We present a method for object categorization in real-world scenes. Following a common consensus in the field, we do not assume that a figure-ground segmentation is available prior to recognition. However, in contrast to most standard approaches for object class recognition, our approach automatical ..."
Abstract
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Cited by 406 (10 self)
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novel MDL-based criterion. In addition, we present an extensive evaluation of our method on a standard dataset for car detection and compare its performance to existing methods from the literature. Our results show that the proposed method significantly outperforms previously published methods while
A Unifying Review of Linear Gaussian Models
, 1999
"... Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observa ..."
Abstract
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Cited by 351 (18 self)
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that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive
Finding frequent items in data streams
, 2002
"... Abstract. We present a 1-pass algorithm for estimating the most frequent items in a data stream using very limited storage space. Our method relies on a novel data structure called a count sketch, which allows us to estimate the frequencies of all the items in the stream. Our algorithm achieves bett ..."
Abstract
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Cited by 341 (0 self)
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Abstract. We present a 1-pass algorithm for estimating the most frequent items in a data stream using very limited storage space. Our method relies on a novel data structure called a count sketch, which allows us to estimate the frequencies of all the items in the stream. Our algorithm achieves
Unpacking “Privacy” for a Networked World
, 2003
"... Although privacy is broadly recognized as a dominant concern for the development of novel interactive technologies, our ability to reason analytically about privacy in real settings is limited. A lack of conceptual interpretive frameworks makes it difficult to unpack interrelated privacy issues in s ..."
Abstract
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Cited by 316 (8 self)
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Although privacy is broadly recognized as a dominant concern for the development of novel interactive technologies, our ability to reason analytically about privacy in real settings is limited. A lack of conceptual interpretive frameworks makes it difficult to unpack interrelated privacy issues
Learning policies for partially observable environments: Scaling up
, 1995
"... Partially observable Markov decision processes (pomdp's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor feedback. While the study of pomdp's is motivated by a need to address realistic problems, existing techniques for fin ..."
Abstract
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Cited by 296 (11 self)
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for a selection of extremely small pomdp's taken from the learning literature. In contrast, we show that none are able to solve a slightly larger and noisier problem based on robot navigation. We find that a combination of two novel approaches performs well on these problems and suggest methods
Matrix Completion with Noise
"... On the heels of compressed sensing, a remarkable new field has very recently emerged. This field addresses a broad range of problems of significant practical interest, namely, the recovery of a data matrix from what appears to be incomplete, and perhaps even corrupted, information. In its simplest ..."
Abstract
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Cited by 255 (13 self)
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form, the problem is to recover a matrix from a small sample of its entries, and comes up in many areas of science and engineering including collaborative filtering, machine learning, control, remote sensing, and computer vision to name a few. This paper surveys the novel literature on matrix
On Maximum-Likelihood Detection and the Search for the Closest Lattice Point
- IEEE TRANS. INFORM. THEORY
, 2003
"... Maximum-likelihood (ML) decoding algorithms for Gaussian multiple-input multiple-output (MIMO) linear channels are considered. Linearity over the field of real numbers facilitates the design of ML decoders using number-theoretic tools for searching the closest lattice point. These decoders are colle ..."
Abstract
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Cited by 273 (9 self)
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are collectively referred to as sphere decoders in the literature. In this paper, a fresh look at this class of decoding algorithms is taken. In particular, two novel algorithms are developed. The first algorithm is inspired by the Pohst enumeration strategy and is shown to offer a significant reduction
A Taxonomy of Software Visualization
- Journal of Visual Languages and Computing
, 1992
"... Software visualization is the use of interactive computer graphics, typography, graphic design, animation, and cinematography to enhance the interface between the software engineer or the computer science student and their programs. Although several taxonomies of software visualization have been pro ..."
Abstract
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Cited by 263 (6 self)
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proposed, they use few dimensions and do not span the space of important distinctions between systems. We attempt to fill this gap in the literature by proposing a novel and systematic taxonomy of six areas making up thirty characteristic features of software visualization technology. The taxonomy
Results 1 - 10
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7,663
