Artificial intelligence
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Neuroscience
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Learning
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System
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Neural network
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Algorithm
Cybernetics
Machine learning
Computational neuroscience
Mathematical model
Biocybernetics
Network
Computational science
Knowledge representation
Connectionism
Computational statistics
Spiking neural network
Classification algorithms
Artificial neural network
In machine learning and cognitive science, artificial neural networks (ANNs) are a family of statistical learning algorithms inspired by biological neural networks (the central nervous systems of anim...
Artificial neural network - Wikipedia
Graphical model
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Mathematical optimization
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Estimation theory
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Machine learning
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Dynamic programming
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Stochastic control
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Neural network software
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Types of artificial neural networks
Machine Learning Can Transform Video Footage Automatically
“Deep fake” videos may make it impossible to tell fact from fiction.
Newly Built Computer Mimics The Human Brain
Newly Built Computer Mimics The Human Brain.
Can you tell that these faces are fake?
New 'Artificial Synapses' Could Let Supercomputers Mimic the Human Brain
Brain-like machines with human-like abilities to solve problems could become a reality, researchers say.
MIT Has Produced Data Science Machine That Is Replacing Human Intution For Big-Data Analysis
System that replaces human intuition with algorithms outperforms 615 of 906 human teams. Big-data analysis consists of searching for buried patterns that have some kind of predictive power. But choosi...
Bubble, Bubble … Boiling On The Double
The boiling of water is at the heart of many industrial processes, from the operation of electric power plants to chemical processing and desalination. But the details of what happens on a hot surface...
Important Step In Artificial Intelligence: Stylized Letters Classified By Their Images
In what marks a significant step forward for artificial intelligence, researchers at UC Santa Barbara have demonstrated the functionality of a simple artificial neural circuit. For the first time, a c...
Probabilistic programming does in 50 lines of code what used to take thousands
To make machine-learning applications easier to build, computer scientists have begun developing so-called probabilistic programming languages, which let researchers mix and match machine-learning tec...
New brain mapping reveals unknown cell types
jens hjerlingleffler and sten linnarsson are principal investigators at the department of medical biochemistry and biophysics at karolinska institutet in sweden using a process known as single cell se...
Brainlike computers, learning from experience
Computers have entered the age when they are able to learn from their own mistakes, a development that is about to turn the digital world on its head.
IBM takes a step towards building artificial semiconductor synapses
Researchers at IBM have discovered a way to change the conductive properties of a metal oxide layer using an ionic fluid rather than a conventional electric charge. Building artificial synapses may ha...
Graphical model
A model or tic graphical model ('PGM) is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory,...
Graphical model - Wikipedia
Mathematical optimization
In mathematics, computer science, economics, or management science, mathematical optimization (alternatively, optimization or mathematical programming) is the selection of a best element (with regard ...
Mathematical optimization - Wikipedia
Estimation theory
Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured/empirical data that has a random component. The parameters describe an underlying phy...
Machine learning
Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using t...
Dynamic programming
In mathematics, computer science, economics, and bioinformatics, dynamic programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems. It is appli...
Dynamic programming - Wikipedia
Stochastic control
Stochastic control or stochastic optimal control is a subfield of control theory that deals with the existence of uncertainty either in observations or in the noise that drives the evolution of the pl...
Neural network software
Neural network software is used to simulate, research, develop, and apply artificial neural networks, software concepts adapted from biological neural networks, and, in some cases, a wider array of ad...
Neural network software - Wikipedia
Types of artificial neural networks
There are many types of artificial neural networks (ANN).Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are gene...
Multiobjective optimization
Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple cri...
Multiple discriminant analysis
Multiple Discriminant Analysis (MDA) is a method for compressing a multivariate signal to yield a lower-dimensional signal amenable to classification.MDA is not directly used to perform classification...
Hungarian algorithm
The Hungarian method is a combinatorial optimization algorithm that solves the assignment problem in polynomial time and which anticipated later primal-dual methods. It was developed and published in ...
Subgradient method
Subgradient methods are iterative methods for solving convex minimization problems. Originally developed by Naum Z. Shor and others in the 1960s and 1970s, subgradient methods are convergent when appl...
PROPT
The PROPT MATLAB Optimal Control Software is a new generation platform for solving applied optimal control (with ODE or DAE formulation) and parameters estimation problems.The platform was developed b...
Quantum neural network
Quantum neural networks (QNNs) are neural network models which are based on the principles of quantum mechanics. There are two different approaches to QNN research, one exploiting quantum information...
Quantum neural network - Wikipedia
Multinomial logit
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it ...
New brain mapping reveals unknown cell types
jens hjerlingleffler and sten linnarsson are principal investigators at the department of medical biochemistry and biophysics at karolinska institutet in sweden using a process known as single cell se...
Activation function
In computational networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard computer chip circuit can be seen as a digital network of activ...
Neocognitron
The neocognitron is a hierarchical multilayered artificial neural network proposed by Professor Kunihiko Fukushima. It has been used for handwritten character recognition and other pattern recognition...