Local log-linear models (LLLMs) for capture-recapture (CRC)Zach Kurtz
The learning rule is formulated as a gradient descent algorithm of a cost function L, whose landscape is depicted as in athfiusnthctrieoen-doifmsyennasipotnicasltprleontg, twhepuasreadmaettweros-(dWim11e,nWsi1o2n).aNl rootteatthioant in order to graphically matrix with angle 6/π ...
We developed a biologically plausible unsupervised learning algorithm, error-gated Hebbian rule (EGHR)-β, that performs principal component analysis (PCA) and independent component analysis (ICA) in a single-layer feedforward neural network. If parameter β = 1, it can extract the subspace ...
These measures are expressed in terms of two fundamental diagnostic statistics in discriminant analysis, proposed independently by Critchley and Vitiello (1991) and Fung (1992). They can be compared using contour plots with other measures such as the Johnson (1987) type measures that are defined ...
The learning rule is formulated as a gradient descent algorithm of a cost function L, whose landscape is depicted as in athfiusnthctrieoen-doifmsyennasipotnicasltprleontg, twhepuasreadmaettweros-(dWim11e,nWsi1o2n).aNl rootteatthioant in order to graphically matrix with angle 6/π ...
The theoretical foundation of L2ECM is the Log-Euclidean framework, which endows the commutative lie group formed by the SPD matrices with a linear space structure. This enables the common Euclidean operations of covariance matrices in the logarithmic domain while preserving their geometric structure. ...
MPSMatrixLogSoftMaxGradient MPSMatrixMultiplication MPSMatrixNeuron MPSMatrixNeuronGradient MPSMatrixSoftMax MPSMatrixSoftMaxGradient MPSMatrixSolveCholesky MPSMatrixSolveLU MPSMatrixSolveTriangular MPSMatrixSum MPSMatrixUnaryKernel MPSMatrixVectorMultiplication MPSNNAdditionGradientNode MPSNNAdditionNode MPSNNArithm...
As the implementation of our algorithm was done in Python, we use the NetworkX package implementation of the Louvain algorithm, our main point of comparison, as they are both of a linear time complexity, to obtain fair comparison for running time. We also compare with a broader range of popu...
In Proc. of the 9th European Conference on Machine Learning, ECML-97, Lecture Notes in AI 1224, Springer, M. van Someren and G. Widmer (Eds), 266–273, Prague. About this Chapter Title Generating Linear Regression Rules from Neural Networks Using Local Least Squares Approximation Book ...
Algorithm 1 Random Batch Method Full size image 3.2 The Reduced Variance Strategy In this section we want to define a suitable strategy to reduce the variance of the RBM method. To this end, we will develop a control variate approach which is based on the construction of simpler linear models...