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Pruning (artificial Neural Network)
Pruning is the practice of removing parameters (which may entail removing individual parameters, or parameters in groups such as by neurons) from an existing artificial neural networks. The goal of this process is to maintain accuracy of the network while increasing its efficiency. This can be done to reduce the computational resources required to run the neural network. A biological process of synaptic pruning takes place in the brain of mammals during development (see also Neural Darwinism Neural Darwinism is a biological, and more specifically Darwinian and selectionist, approach to understanding global brain function, originally proposed by American biologist, researcher and Nobel-Prize recipient Gerald Maurice Edelman (July 1, ...). Node (neuron) pruning A basic algorithm for pruning is as follows: #Evaluate the importance of each neuron. #Rank the neurons according to their importance (assuming there is a clearly defined measure for "importance"). #Remove the least i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Parameter
A parameter (), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.). That is, a parameter is an element of a system that is useful, or critical, when identifying the system, or when evaluating its performance, status, condition, etc. ''Parameter'' has more specific meanings within various disciplines, including mathematics, computer programming, engineering, statistics, logic, linguistics, and electronic musical composition. In addition to its technical uses, there are also extended uses, especially in non-scientific contexts, where it is used to mean defining characteristics or boundaries, as in the phrases 'test parameters' or 'game play parameters'. Modelization When a system is modeled by equations, the values that describe the system are called ''parameters''. For example, in mechanics, the masses, the dimensions and shapes (for solid bodies), the densities and the viscosit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Neurons
An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in an artificial neural network. The artificial neuron receives one or more inputs (representing excitatory postsynaptic potentials and inhibitory postsynaptic potentials at neural dendrites) and sums them to produce an output (or , representing a neuron's action potential which is transmitted along its axon). Usually each input is separately weighted, and the sum is passed through a non-linear function known as an activation function or transfer function. The transfer functions usually have a sigmoid shape, but they may also take the form of other non-linear functions, piecewise linear functions, or step functions. They are also often monotonically increasing, continuous, differentiable and bounded. Non-monotonic, unbounded and oscillating activation functions with multiple zeros that outperform sigmoidal and ReLU like activat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Neural Network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called ''edges''. Neurons and edges typically have a ''weight'' that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Efficiency
Efficiency is the often measurable ability to avoid wasting materials, energy, efforts, money, and time in doing something or in producing a desired result. In a more general sense, it is the ability to do things well, successfully, and without waste. In more mathematical or scientific terms, it signifies the level of performance that uses the least amount of inputs to achieve the highest amount of output. It often specifically comprises the capability of a specific application of effort to produce a specific outcome with a minimum amount or quantity of waste, expense, or unnecessary effort. Efficiency refers to very different inputs and outputs in different fields and industries. In 2019, the European Commission said: "Resource efficiency means using the Earth's limited resources in a sustainable manner while minimising impacts on the environment. It allows us to create more with less and to deliver greater value with less input." Writer Deborah Stone notes that efficiency is " ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computational Resource
In computational complexity theory, a computational resource is a resource used by some computational models in the solution of computational problems. The simplest computational resources are computation time, the number of steps necessary to solve a problem, and memory space, the amount of storage needed while solving the problem, but many more complicated resources have been defined. A computational problem is generally defined in terms of its action on any valid input. Examples of problems might be "given an integer ''n'', determine whether ''n'' is prime", or "given two numbers ''x'' and ''y'', calculate the product ''x''*''y''". As the inputs get bigger, the amount of computational resources needed to solve a problem will increase. Thus, the resources needed to solve a problem are described in terms of asymptotic analysis, by identifying the resources as a function of the length or size of the input. Resource usage is often partially quantified using Big O notation. Co ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Synaptic Pruning
Synaptic pruning, a phase in the development of the nervous system, is the process of synapse elimination that occurs between early childhood and the onset of puberty in many mammals, including humans. Pruning starts near the time of birth and continues into the late-20s. During pruning, both the axon and dendrite decay and die off. It was traditionally considered to be complete by the time of sexual maturation, but this was discounted by MRI studies. The infant brain will increase in size by a factor of up to 5 by adulthood, reaching a final size of approximately 86 (± 8) billion neurons. Two factors contribute to this growth: the growth of synaptic connections between neurons and the myelination of nerve fibers; the total number of neurons, however, remains the same. After adolescence, the volume of the synaptic connections decreases again due to synaptic pruning. Pruning is influenced by environmental factors and is widely thought to represent learning. Variations Regu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neural Darwinism
Neural Darwinism is a biological, and more specifically Darwinian and selectionist, approach to understanding global brain function, originally proposed by American biologist, researcher and Nobel-Prize recipient Gerald Maurice Edelman (July 1, 1929 – May 17, 2014). Edelman's 1987 book ''Neural Darwinism'' introduced the public to the ''theory of neuronal group selection'' (TNGS) – which is the core theory underlying Edelman's explanation of global brain function. Owing to the book title, TNGS is most commonly referred to as the ''theory of neural Darwinism'', although TNGS has roots going back to Edelman and Mountcastle's 1978 book, ''The Mindful Brain – Cortical Organization and the Group-selective Theory of Higher Brain Function'' – where Edelman's colleague, the American neurophysiologist and anatomist Vernon B. Mountcastle (July 15, 1918 – January 11, 2015), describes the columnar structure of the cortical groups within the neocortex, while Edelman develops his ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
