This publication represents the largest LTC commitmentto an application note to date. No other application noteabsorbed as much effort, took so long or cost so much.This level of activity is justified by our Belief that high speedmonolithic amplifiers greatly interest users.
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a Belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
The package includes 3 Matlab-interfaces to the c-code:
1. inference.m
An interface to the full inference package, includes several methods for
approximate inference: Loopy Belief Propagation, Generalized Belief
Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods
(Metropolis, Gibbs, Wolff, Swendsen-Wang).
Use "help inference" from Matlab to see all options for usage.
2. gbp_preprocess.m and gbp.m
These 2 interfaces split Generalized Belief Propagation into the pre-process
stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use
only one of them, or changing some parameters in between.
Use "help gbp_preprocess" and "help gbp" from Matlab.
3. simulatedAnnealing.m
An interface to the simulated-annealing c-code. This code uses Metropolis
sampling method, the same one used for inference.
Use "help simulatedAnnealing" from Matlab.
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, Belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
ieee754的標準，原英文版的！Twenty years ago anarchy threatened floating-point arithmetic. Over a dozen commercially significant arithmetics
boasted diverse wordsizes, precisions, rounding procedures and over/underflow behaviors, and more were in the
works. “Portable” software intended to reconcile that numerical diversity had become unbearably costly to
develop.
Thirteen years ago, when IEEE 754 became official, major microprocessor manufacturers had already adopted it
despite the challenge it posed to implementors. With unprecedented altruism, hardware designers had risen to its
challenge in the Belief that they would ease and encourage a vast burgeoning of numerical software. They did
succeed to a considerable extent. Anyway, rounding anomalies that preoccupied all of us in the 1970s afflict only
CRAY X-MPs — J90s now.
2.5 Neural Turing Machine
-
2.1 Model
-
.DS_Store
10KB
2.4 RNN Sequence-to-Sequence Model
-
2.8 One Shot Deep Learning
-
2.7 Deep Transfer Learning Lifelong Learning especially for RL
-
2.2 Optimization
-
1.4 Speech Recognition Evolution
-
1.2 Deep Belief Network(DBN)(Milestone of Deep Learning Eve)
-
1.3 ImageNet Evolution（Deep Learning broke out from here）
-
2.3 Unsupervised Learning Deep Generative Model
-
2.6 Deep Reinforcement Learning
