spiking neurons and learning algorithms

... F and the true value of pa, and sketch it as a function of F for pa= p0= 1/6, pa= 0.25, and pa= 1/2 [Hint: sketch the log evidence as a function of the random variable Faand workout the mean and ... equal to, the set A sets B and A of the sets B and A in set A Before reading Chapter 4, you should have read Chapter 2 and worked on exercises 2.21–2.25 and 2.16 (pp.36–37), and exercise 4.1 below ... 3.8 were found by finding the mean and standard deviation of Fa, then setting Fa to the mean ± two standard deviations to get a 95% plausible range for Fa, and computing the three corresponding...

Ngày tải lên: 13/08/2014, 18:20

... 0.01}: (a) The standard method: use a standard random number generator to generate an integer between 1 and 232 Rescale the integer to(0, 1) Test whether this uniformly distributed random variable ... contents and entropies in this situation Let the value of the upper face’s colour be u andthe value of the lower face’s colour be l Imagine that we draw a random card and learn both u and l What ... know beforehand The coin’s probability ofcoming up a when tossed is pa, and pb= 1− pa; the parameters pa, pbare notknown beforehand The source string s = baaba2 indicates that l was 5 andthe sequence...

Ngày tải lên: 13/08/2014, 18:20

... Summary Random codes are good, but they require exponential resources to encode and decode them Non-random codes tend for the most part not to be as good as random codes For a non-random code, ... community They wererediscovered in 1995 and shown to have outstanding theoretical and prac-tical properties Like turbo codes, they are decoded by message-passingalgorithms We will discuss these beautifully ... the output distribution is Normal(0, v + σ2), since x and the noise are independent random variables, and variances add for independent random variables The mutual information is: Solution to...

Ngày tải lên: 13/08/2014, 18:20

... forward and backward algorithms be-tween points A and B on a grid, how can one draw one path from A to B uniformly at random? (Figure 16.11.) of passing through each node, and(b) a randomly chosen ... random, then the probability of u1u2u3 and v1v2v3 would be uniform, and so would that of x and y, so the probability P (x, y| H1) would be equal to P (x, y| H0), and the two hypothesesH0andH1 ... 17.5a and figures 17.5b and c it looks as if channels B and C have the same capacity as channel A The principal eigenvalues of the three trellises are the same (the eigenvectors for channels A and...

Ngày tải lên: 13/08/2014, 18:20

... (aCauchy distribution) and (2, 4)(light line), and a Gaussiandistribution with mean µ = 3 andstandard deviation σ = 3 (dashedline), shown on linear verticalscales (top) and logarithmicvertical ... lines) and 10, 0.3 (light lines), shown onlinear vertical scales (top) andlogarithmic vertical scales(bottom); and shown as afunction of x on the left and l = ln x on the right Gamma and inverse ... (1, 3) (heavy lines) and 10, 0.3 (light lines), shown onlinear vertical scales (top) andlogarithmic vertical scales(bottom); and shown as afunction of x on the left (23.15)and l = ln x on the...

Ngày tải lên: 13/08/2014, 18:20

... available: X + N arithmetic sum, modulo B, of X and N X− N difference, modulo B, of X and N X⊕ N bitwise exclusive-or of X and N N := randbits(l) sets N to a random l-bit integer A slice-sampling procedure ... width2b: do{ 2c: N := randbits(l)2d: X0:= ((X− U) ⊕ N) + U2e: l := l + 1 2f: } until (l = b) or (P∗(X0) < Y ) These shrinking and stepping out methods shrink and expand by a factor of two ... their values sam-• Simple Metropolis algorithms and Gibbs sampling algorithms, although widely used, perform poorly because they explore the space by a slowrandom walk The next chapter will discuss...

Ngày tải lên: 13/08/2014, 18:20

... computed, and then w is changed by the rule ∆wi= η∂L This popular equation is dimensionally inconsistent: the left-hand side of this equation has dimensions of [wi] and the right-hand side has ... becomes enormous, and ‘doing the right thing’ is not simple, because the expected utility of an action cannot be computed exactly (Russell and Wefald, 1991; Baum and Smith, 1993; Baum and Smith, 1997) ... networks of idealized ‘neurons’ Three motivations underlie work in this broad and interdisciplinary field Biology The task of understanding how the brain works is one of the out-standing unsolved problems...

Ngày tải lên: 13/08/2014, 18:20

... 0.5 and weights θ(1)j , wjl(1), θi(2) and w(2)ij to random values, and plot the resulting function y(x) I set the hidden unit biases θ(1)j to random values from a Gaussian with zero mean and standard ... the input to hidden weights w(1)jl to random values with standard deviation σin; and the bias and output weights θi(2) and w(2)ij to random values with standard deviation σout The sort of functions ... It has input neurons, hidden neurons and output neurons The hidden neurons may be arranged in a sequence of layers The most common multilayer perceptrons have a single hidden layer, and are known...

Ngày tải lên: 13/08/2014, 18:20

... MacKay and Neal, 1995; MacKay and Neal, 1996; Wiberg, 1996; MacKay, 1999b; Spielman, 1996; Sipser and Spielman, 1996) Low-precision decoding algorithms and fast encoding algorithms for Gallager codes ... decoding in terms of group theory and coding theory, see (Forney, 2001; Offer and Soljanin, 2000; Offer Trang 8572 47 — Low-Density Parity-Check Codesand Soljanin, 2001); and for background reading ... random linear code to map on to a low-density H Trang 10Convolutional Codes and Turbo Codes This chapter follows tightly on from Chapter 25 It makes use of the ideas of codes and trellises and...

Ngày tải lên: 13/08/2014, 18:20

... type and/or strategy of other players Depending on the game, different learning algorithms are applied [46] Reinforcement learning [47] and regret minimization algorithms [48] are the popular learning ... any learning capability Our contributions highlight the importance of learning algorithms in such auctions, and shows the performance of nonparametric learning algorithms where the number and ... uppercase letters) N and R Set of all natural and real numbers, respectively N and N The set of subcarriers and the number of subcarriers, re-spectively So, N = {1, ,N} and |N | = N. K and K The set...

Ngày tải lên: 09/09/2015, 10:19

... text and a reader, and the processof interpreting that text must take into account all three What then do we mean in overall terms by ‘Training Issues’, ‘Learning Algorithms’ and ‘Feedforward and ... hidden layer neurons ‘Learning algorithms’, on the other hand attempts to build on the method used in addressing theformer, (1) to arrive at (i) a multi-objective hybrid learning algorithm, and (ii) ... failed toinject diversity and variety in my thinking and outlook, and whose diligence and enthusiasm hasalways made the business of teaching and research such a pleasant and stimulating one for...

Ngày tải lên: 14/09/2015, 14:13

... batch algorithms and the sequential algorithms are two basic approaches to solve the problem of (2) The batch algorithms include the EM algorithm for a family of heavy-tailed distributions [3,4] and ... of w and reset 1/α nto its default value accordingly 3.2 Specific algorithms Specific algorithms for the three penalty functions can be developed by substituting ψ(e n) and ϕ(e n) into (44) and ... doi:10.1155/2008/459586 Research Article Sequential and Adaptive Learning Algorithms for M-Estimation Guang Deng Department of Electronic Engineering, Faculty of Science, Technology and Engineering, La Trobe University,...

Ngày tải lên: 22/06/2014, 01:20

... synaptic learning rule is used so that neuronscan efficiently make a decision The whole system contains encoding, learningand readout Utilizing the temporal coding and learning, networks of spikingneurons ... howinformation is encoded with spikes, learning rules in spiking neural networkscan be generally assorted into two categories: rate learning and temporallearning The rate learning algorithms, such as the spike-driven ... temporal learning describes how neurons processprecise-timing spikes Further research on temporal coding and temporallearning would provide a better understanding of the biological systems, andwould...

Ngày tải lên: 09/09/2015, 11:31

... finance and medi-cal applications Based on desired outcomes of problems, machine learning algorithmscan be broadly categorized into three paradigms: supervised learning, unsupervisedlearning and ... Semi-supervised learning is a compromise between super-vised learning and unsupervised learning, in which a few labeled and a large amount ofunlabeled data are available Hence, semi-supervised learning ... performance for learningalgorithms are given For example, radius margin bound and span bound for SVM (2.1), without considering loss L(ζ) and bias b, are firstly proposed by Vapnik [81] and Vapnik and Chapelle...

Ngày tải lên: 10/09/2015, 15:47

... introduction to machine learning and Apache Mahout Chapter 2, Clustering, provides an introduction to unsupervised learning and clustering techniques (K-Means clustering and other algorithms) in Apache ... Supervised learning versus unsupervised learning Let's explain the difference between supervised learning and unsupervised learning using a simple example of pebbles: • Supervised learning: Take ... detail with both Java and command-line examples (sequential and parallel executions), and other important clustering algorithms, such as Fuzzy K-Means, canopy clustering, and spectral K-Means...

Ngày tải lên: 04/03/2019, 11:13

... based on transfer learning and kernel k-means,” Journal of Science and Technology on Information and Communications, pp 1-14, 2017 (accepted) [20] L Vo, C Schatten, C Mazziotti, and L Schmidt-Thieme, ... the kernel k-means and spectral feature alignment algorithms with a new learning process including the automatic adjustment of the enhanced feature space once running transfer learning at the representation ... classification and regression, respectively, not to clustering On the other hand, transfer learning in [20] is also different from ours as using Matrix Factorization for sparse data handling In...

Ngày tải lên: 29/01/2020, 23:48

... based on transfer learning and kernel k-means,” Journal of Science and Technology on Information and Communications, pp 1-14, 2017 (accepted) [20] L Vo, C Schatten, C Mazziotti, and L Schmidt-Thieme, ... the kernel k-means and spectral feature alignment algorithms with a new learning process including the automatic adjustment of the enhanced feature space once running transfer learning at the representation ... classification and regression, respectively, not to clustering On the other hand, transfer learning in [20] is also different from ours as using Matrix Factorization for sparse data handling In...

Ngày tải lên: 27/01/2021, 06:20

... based on transfer learning and kernel k-means,” Journal of Science and Technology on Information and Communications, pp 1-14, 2017 (accepted) [20] L Vo, C Schatten, C Mazziotti, and L Schmidt-Thieme, ... the kernel k-means and spectral feature alignment algorithms with a new learning process including the automatic adjustment of the enhanced feature space once running transfer learning at the representation ... classification and regression, respectively, not to clustering On the other hand, transfer learning in [20] is also different from ours as using Matrix Factorization for sparse data handling In...

Ngày tải lên: 17/03/2021, 20:30

... 19Optimization Algorithms in Machine Learning• The heart of many (most practical?) machine learning algorithms: • Linear regression Minimize Errors Trang 20Optimization Algorithms in Machine Learning• ... 1Optimizing Supervised Machine Learning Algorithms and Implementing Deep Learning in HPCC SystemsTrang 2LexisNexis/Florida Atlantic University Cooperative ResearchDeveloping ML Algorithms On HPCC/ECL ... distributed data storage and parallel processing capabilities of the HPCC Systems Platform. Trang 10Random Forest Learning OptimizationLoopbody function fully parallelized: • Receives and returns one...

Ngày tải lên: 20/10/2022, 01:49

... Øistein Andersen and the anonymous reviewers for their useful comments References Yigal Attali and Jill Burstein 2006 Automated essay scoring with e-rater v.2 Journal of Technology, Learning, and ... 133–142 ACM T.K Landauer and P.W Foltz 1998 An introduction to latent semantic analysis Discourse processes, pages 259–284 T.K Landauer, D Laham, and P.W Foltz 2003 Automated scoring and annotation ... system, we calculate the average correlation between the CLC and the examiners’ scores, and find an upper bound of 0.796 and 0.792 Pearson’s and Spearman’s correlation respectively In order to evaluate...

Ngày tải lên: 20/02/2014, 04:20