Results 1 - 10 of 560

Scale-invariant random spatial networks

by David J. Aldous
"... Real-world road networks have an approximate scale-invariance property; can one devise mathematical models of random networks whosedistributionsareexactlyinvariantunderEuclideanscaling? This requires working in the continuum plane. We introduce an axiomatization of a class of processes we call scale ..."
Abstract - Cited by 7 (5 self) - Add to MetaCart
Real-world road networks have an approximate scale-invariance property; can one devise mathematical models of random networks whosedistributionsareexactlyinvariantunderEuclideanscaling? This requires working in the continuum plane. We introduce an axiomatization of a class of processes we call

Scale-Invariant Convolutional Neural Network

by Yichong Xu, Tianjun Xiao, Jiaxing Zhang, Kuiyuan Yang, Zheng Zhang
"... Even though convolutional neural networks (CNN) has achieved near-human performance in various computer vi-sion tasks, its ability to tolerate scale variations is lim-ited. The popular practise is making the model bigger first, and then train it with data augmentation using ex-tensive scale-jitterin ..."
Abstract - Add to MetaCart
-jittering. In this paper, we propose a scale-invariant convolutional neural network (SiCNN), a model designed to incorporate multi-scale feature exaction and classification into the network structure. SiCNN uses a multi-column architecture, with each column focusing on a particular scale. Unlike previous multi

Biological pathway kinetic rate constants are scale-invariant

by Scott Grandison , Richard J Morris , 2008
"... ABSTRACT Motivation: Scale-free networks have had a profound impact in Biology. Network theory is now used routinely to visualize, navigate through, and help understand gene networks, protein-protein interactions, regulatory networks and metabolic pathways. Here we analyse the numerical rather than ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
an underlying power-law. This implies that these data are scale-invariant, thus placing biological network topology and their chemistry on an equivalent 'scale-free' power-law foundation. Contact: Richard.Morris@bbsrc.ac.uk

A Novel Ensemble of Scale-Invariant Feature Maps

by Bruno Baruque, Emilio Corchado, Bruno Baruque
"... Abstract. A novel method for improving the training of some topology preserving algorithms as the Scale Invariant Feature Map (SIM) and the Maximum Likelihood Hebbian Learning Scale Invariant Map (MAX-SIM) is presented and analyzed in this study. It is called Weighted Voting Superposition (WeVoS), p ..."
Abstract - Add to MetaCart
Abstract. A novel method for improving the training of some topology preserving algorithms as the Scale Invariant Feature Map (SIM) and the Maximum Likelihood Hebbian Learning Scale Invariant Map (MAX-SIM) is presented and analyzed in this study. It is called Weighted Voting Superposition (We

Characterization Of Self-Similarity Properties Of Discrete-Time Linear Scale-Invariant Systems

by Seungsin Lee, et al. , 2001
"... Discrete-time linear systems that possess scale-invariance properties even in the presence of continuous dilation were proposed by Zhao and Rao. The principal purpose of this article is to describe results of subsequent investigation which have led to characterization of self-similarity properties o ..."
Abstract - Add to MetaCart
Discrete-time linear systems that possess scale-invariance properties even in the presence of continuous dilation were proposed by Zhao and Rao. The principal purpose of this article is to describe results of subsequent investigation which have led to characterization of self-similarity properties

Scale-invariant neuronal avalanche dynamics and the cut-off in size distributions

by Shan Yu, Andreas Klaus, Hongdian Yang, Dietmar Plenz - PLOS ONE 9:e99761. doi: 10.1371/journal.pone.0099761 , 2014
"... Identification of cortical dynamics strongly benefits from the simultaneous recording of as many neurons as possible. Yet current technologies provide only incomplete access to the mammalian cortex from which adequate conclusions about dynamics need to be derived. Here, we identify constraints intro ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
. Thus, avalanche analysis needs to be constrained to the size of the observation window to reveal the underlying scale-invariant organization produced by locally

Face Recognition: A Convolutional Neural Network Approach

by Steve Lawrence, C. Lee Giles, Ah Chung Tsoi, Andrew D. Back - IEEE Transactions on Neural Networks , 1997
"... Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult [43]. We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sampling, a self-organizing map n ..."
Abstract - Cited by 234 (0 self) - Add to MetaCart
changes in the image sample, and the convolutional neural network provides for partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform

POSTER PRESENTATION Open Access Sequential patterns of spikes and scale-invariance in modular networks

by Timothee Leleu, Kazuyuki Aihara
"... It has been reported that there are consistent sequential patterns of spikes after the transitions to the up state during slow wave sleep[1]. The up states may be charac-terized by critical dynamics[2] for which the avalanche sizes distribution is scale-invariant[3]. In order to under-stand the mech ..."
Abstract - Add to MetaCart
It has been reported that there are consistent sequential patterns of spikes after the transitions to the up state during slow wave sleep[1]. The up states may be charac-terized by critical dynamics[2] for which the avalanche sizes distribution is scale-invariant[3]. In order to under

Context-Based Scene Recognition Using Bayesian Networks with Scale-Invariant Feature Transform

by Seung-bin Im, Sung-bae Cho - ACIVS 2006, LNCS 4179 , 2006
"... Abstract. Scene understanding is an important problem in intelligent robotics. Since visual information is uncertain due to several reasons, we need a novel method that has robustness to the uncertainty. Bayesian probabilistic approach is robust to manage the uncertainty, and powerful to model high- ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
invariant. This information is provided to Bayesian networks for robust inference in scene understanding. Experiments in complex real environments show that the pro-posed method is useful. 1

Data networks as cascades: Investigating the multifractal nature of Internet WAN traffic

by A. Feldmann, A. C. Gilbert, W. Willinger , 1998
"... In apparent contrast to the well-documented self-similar (i.e., monofractal) scaling behavior of measured LAN traffic, recent studies have suggested that measured TCP/IP and ATM WAN traffic exhibits more complex scaling behavior, consistent with multifractals. To bring multifractals into the realm o ..."
Abstract - Cited by 220 (14 self) - Add to MetaCart
of networking, this paper provides a simple construction based on cascades (also known as multiplicative processes) that is motivated by the protocol hierarchy of IP data networks. The cascade framework allows for a plausible physical explanation of the observed multifractal scaling behavior of data traffic
Results 1 - 10 of 560