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1,000 | Conditioning (probability) Conditional expectation Conditional probability distribution Regular conditional probability Disintegration theorem Bayes' theorem de Finetti's theorem Exchangeable random variables Rule of succession Conditional independence Conditional event algebra Goodman–Nguyen–van Fraassen algebra | List of probability topics | 0.841859 |
1,001 | This is a list of probability topics. It overlaps with the (alphabetical) list of statistical topics. There are also the outline of probability and catalog of articles in probability theory. For distributions, see List of probability distributions. For journals, see list of probability journals. For contributors to the... | List of probability topics | 0.841859 |
1,002 | Discrete random variable Probability mass function Constant random variable Expected value Jensen's inequality Variance Standard deviation Geometric standard deviation Multivariate random variable Joint probability distribution Marginal distribution Kirkwood approximation Independent identically-distributed random vari... | List of probability topics | 0.841859 |
1,003 | Central limit theorem Illustration of the central limit theorem Concrete illustration of the central limit theorem Berry–Esséen theorem Berry–Esséen theorem De Moivre–Laplace theorem Lyapunov's central limit theorem Martingale central limit theorem Infinite divisibility (probability) Method of moments (probability theo... | List of probability topics | 0.841859 |
1,004 | Buffon's needle Integral geometry Hadwiger's theorem Wendel's theorem | List of probability topics | 0.841859 |
1,005 | Adapted process Basic affine jump diffusion Bernoulli process Bernoulli scheme Branching process Point process Chapman–Kolmogorov equation Chinese restaurant process Coupling (probability) Ergodic theory Maximal ergodic theorem Ergodic (adjective) Galton–Watson process Gauss–Markov process Gaussian process Gaussian ran... | List of probability topics | 0.841859 |
1,006 | Punnett square Hardy–Weinberg principle Ewens's sampling formula Population genetics | List of probability topics | 0.841859 |
1,007 | Probability theory Probability space Sample space Standard probability space Random element Random compact set Dynkin system Probability axioms Normalizing constant Event (probability theory) Complementary event Elementary event Mutually exclusive Boole's inequality Probability density function Cumulative distribution ... | List of probability topics | 0.841859 |
1,008 | Probability distribution Probability distribution function Probability density function Probability mass function Cumulative distribution function Quantile Moment (mathematics) Moment about the mean Standardized moment Skewness Kurtosis Locality Cumulant Factorial moment Expected value Law of the unconscious statistici... | List of probability topics | 0.841859 |
1,009 | Probability Randomness, Pseudorandomness, Quasirandomness Randomization, hardware random number generator Random number generation Random sequence Uncertainty Statistical dispersion Observational error Equiprobable Equipossible Average Probability interpretations Markovian Statistical regularity Central tendency Bean m... | List of probability topics | 0.841859 |
1,010 | Magnetic charges are not seen experimentally in laboratory experiments, but would be present for theories including magnetic monopoles.In supersymmetry: The supercharge refers to the generator that rotates the fermions into bosons, and vice versa, in the supersymmetry.In conformal field theory: The central charge of th... | Charge (physics) | 0.841802 |
1,011 | Various charge quantum numbers have been introduced by theories of particle physics. These include the charges of the Standard Model: The color charge of quarks. The color charge generates the SU(3) color symmetry of quantum chromodynamics. The weak isospin quantum numbers of the electroweak interaction. | Charge (physics) | 0.841802 |
1,012 | The electric charge for electromagnetic interactions. In mathematics texts, this is sometimes referred to as the u 1 {\displaystyle u_{1}} -charge of a Lie algebra module.Note that these charge quantum numbers show up in the Lagrangian via the Gauge covariant derivative#Standard_Model. Charges of approximate symmetries... | Charge (physics) | 0.841802 |
1,013 | So, for example, when the symmetry group is a Lie group, then the charge operators correspond to the simple roots of the root system of the Lie algebra; the discreteness of the root system accounting for the quantization of the charge. The simple roots are used, as all the other roots can be obtained as linear combinat... | Charge (physics) | 0.841802 |
1,014 | Abstractly, a charge is any generator of a continuous symmetry of the physical system under study. When a physical system has a symmetry of some sort, Noether's theorem implies the existence of a conserved current. The thing that "flows" in the current is the "charge", the charge is the generator of the (local) symmetr... | Charge (physics) | 0.841802 |
1,015 | In physics, a charge is any of many different quantities, such as the electric charge in electromagnetism or the color charge in quantum chromodynamics. Charges correspond to the time-invariant generators of a symmetry group, and specifically, to the generators that commute with the Hamiltonian. Charges are often denot... | Charge (physics) | 0.841802 |
1,016 | The decomposition of such products of representations into direct sums of irreducible representations can in general be written as Λ ⊗ Λ ′ = ⨁ i L i Λ i {\displaystyle \Lambda \otimes \Lambda '=\bigoplus _{i}{\mathcal {L}}_{i}\Lambda _{i}} for representations Λ {\displaystyle \Lambda } . The dimensions of the represent... | Charge (physics) | 0.841802 |
1,017 | {\displaystyle 2\otimes {\overline {2}}=3\oplus 1.\ } That is, the product of two (Lorentz) spinors is a (Lorentz) vector and a (Lorentz) scalar. Note that the complex Lie algebra sl(2,C) has a compact real form su(2) (in fact, all Lie algebras have a unique compact real form). The same decomposition holds for the comp... | Charge (physics) | 0.841802 |
1,018 | Nucleic acid secondary structure is the basepairing interactions within a single nucleic acid polymer or between two polymers. It can be represented as a list of bases which are paired in a nucleic acid molecule. The secondary structures of biological DNAs and RNAs tend to be different: biological DNA mostly exists as ... | Nucleic acid secondary structure | 0.841791 |
1,019 | In molecular biology, two nucleotides on opposite complementary DNA or RNA strands that are connected via hydrogen bonds are called a base pair (often abbreviated bp). In the canonical Watson-Crick base pairing, adenine (A) forms a base pair with thymine (T) and guanine (G) forms one with cytosine (C) in DNA. In RNA, t... | Nucleic acid secondary structure | 0.841791 |
1,020 | Scores on this exam are sometimes required for entrance to chemistry Ph.D. programs in the United States. Scores are scaled and then reported as a number between 200 and 990; however, in recent versions of the test, the maximum and minimum reported scores have been 940 (corresponding to the 99 percentile) and 460 (1 pe... | GRE Chemistry Test | 0.841765 |
1,021 | The GRE subject test in chemistry is a standardized test in the United States created by the Educational Testing Service, and is designed to assess a candidate's potential for graduate or post-graduate study in the field of chemistry. It contains questions from many fields of chemistry. 15% of the questions will come f... | GRE Chemistry Test | 0.841765 |
1,022 | 1452–1519 Leonardo da Vinci made many contributions 1638: Galileo Galilei published the book "Two New Sciences" in which he examined the failure of simple structures1660: Hooke's law by Robert Hooke 1687: Isaac Newton published "Philosophiae Naturalis Principia Mathematica" which contains Newton's laws of motion1750: E... | Theory of elasticity | 0.841744 |
1,023 | Solid mechanics (also known as mechanics of solids) is the branch of continuum mechanics that studies the behavior of solid materials, especially their motion and deformation under the action of forces, temperature changes, phase changes, and other external or internal agents. Solid mechanics is fundamental for civil, ... | Theory of elasticity | 0.841744 |
1,024 | The word problem on free lattices and more generally free bounded lattices has a decidable solution. Bounded lattices are algebraic structures with the two binary operations ∨ and ∧ and the two constants (nullary operations) 0 and 1. The set of all well-formed expressions that can be formulated using these operations o... | Word problem (mathematics) | 0.841725 |
1,025 | The most direct solution to a word problem takes the form of a normal form theorem and algorithm which maps every element in an equivalence class of expressions to a single encoding known as the normal form - the word problem is then solved by comparing these normal forms via syntactic equality. For example one might d... | Word problem (mathematics) | 0.841725 |
1,026 | In computer algebra one often wishes to encode mathematical expressions using an expression tree. But there are often multiple equivalent expression trees. The question naturally arises of whether there is an algorithm which, given as input two expressions, decides whether they represent the same element. | Word problem (mathematics) | 0.841725 |
1,027 | One of the most deeply studied cases of the word problem is in the theory of semigroups and groups. A timeline of papers relevant to the Novikov-Boone theorem is as follows: 1910 (1910): Axel Thue poses a general problem of term rewriting on tree-like structures. He states "A solution of this problem in the most genera... | Word problem (mathematics) | 0.841725 |
1,028 | One of the earliest proofs that a word problem is undecidable was for combinatory logic: when are two strings of combinators equivalent? Because combinators encode all possible Turing machines, and the equivalence of two Turing machines is undecidable, it follows that the equivalence of two strings of combinators is un... | Word problem (mathematics) | 0.841725 |
1,029 | : 355 1958 (1958) – 1959 (1959): Boone publishes a simplified version of his construction. 1961 (1961): Graham Higman characterises the subgroups of finitely presented groups with Higman's embedding theorem, connecting recursion theory with group theory in an unexpected way and giving a very different proof of the unso... | Word problem (mathematics) | 0.841725 |
1,030 | In universal algebra one studies algebraic structures consisting of a generating set A, a collection of operations on A of finite arity, and a finite set of identities that these operations must satisfy. The word problem for an algebra is then to determine, given two expressions (words) involving the generators and ope... | Word problem (mathematics) | 0.841725 |
1,031 | For example, x 5 − 3 x + 1 = 0 {\displaystyle x^{5}-3x+1=0} is a univariate algebraic (polynomial) equation with integer coefficients and y 4 + x y 2 = x 3 3 − x y 2 + y 2 − 1 7 {\displaystyle y^{4}+{\frac {xy}{2}}={\frac {x^{3}}{3}}-xy^{2}+y^{2}-{\frac {1}{7}}} is a multivariate polynomial equation over the rational n... | Equation | 0.841651 |
1,032 | In general, an algebraic equation or polynomial equation is an equation of the form P = 0 {\displaystyle P=0} , or P = Q {\displaystyle P=Q} where P and Q are polynomials with coefficients in some field (e.g., rational numbers, real numbers, complex numbers). An algebraic equation is univariate if it involves only one ... | Equation | 0.841651 |
1,033 | The Cartesian coordinate system transforms a geometric problem into an analysis problem, once the figures are transformed into equations; thus the name analytic geometry. This point of view, outlined by Descartes, enriches and modifies the type of geometry conceived of by the ancient Greek mathematicians. Currently, an... | Equation | 0.841651 |
1,034 | In Euclidean geometry, it is possible to associate a set of coordinates to each point in space, for example by an orthogonal grid. This method allows one to characterize geometric figures by equations. A plane in three-dimensional space can be expressed as the solution set of an equation of the form a x + b y + c z + d... | Equation | 0.841651 |
1,035 | For functions of one variable, such an equation differs from a differential equation primarily through a change of variable substituting the function by its derivative, however this is not the case when the integral is taken over an open surface An integro-differential equation is a functional equation involving both t... | Equation | 0.841651 |
1,036 | Equations can be classified according to the types of operations and quantities involved. Important types include: An algebraic equation or polynomial equation is an equation in which both sides are polynomials (see also system of polynomial equations). These are further classified by degree: linear equation for degree... | Equation | 0.841651 |
1,037 | These seemingly distinct physical phenomena can be formalised similarly in terms of PDEs. Just as ordinary differential equations often model one-dimensional dynamical systems, partial differential equations often model multidimensional systems. PDEs find their generalisation in stochastic partial differential equation... | Equation | 0.841651 |
1,038 | A partial differential equation (PDE) is a differential equation that contains unknown multivariable functions and their partial derivatives. (This is in contrast to ordinary differential equations, which deal with functions of a single variable and their derivatives.) PDEs are used to formulate problems involving func... | Equation | 0.841651 |
1,039 | In this operation, sometimes called rotate no carry, the bits are "rotated" as if the left and right ends of the register were joined. The value that is shifted into the right during a left-shift is whatever value was shifted out on the left, and vice versa for a right-shift operation. This is useful if it is necessary... | Binary and | 0.841636 |
1,040 | The molecule approaches this capture region aided by brownian motion and any attraction it might have to the surface of the membrane. Once inside the nanopore, the molecule translocates through via a combination of electro-phoretic, electro-osmotic and sometimes thermo-phoretic forces. Inside the pore the molecule occu... | Nanopore sequencing | 0.841628 |
1,041 | A mathematical chess problem is a mathematical problem which is formulated using a chessboard and chess pieces. These problems belong to recreational mathematics. The most well-known problems of this kind are the eight queens puzzle and the knight's tour problem, which have connection to graph theory and combinatorics.... | Mathematical chess problem | 0.841584 |
1,042 | For rooks, eight are required; the solution is to place them all on one file or rank. The solutions for other pieces are given below. Domination by queens on the main diagonal of a chessboard of any size can be shown equivalent to a problem in number theory of finding a Salem–Spencer set, a set of numbers in which none... | Mathematical chess problem | 0.841584 |
1,043 | Although enzymes can consist of hundreds of amino acids, it is usually only a small fraction of the residues that come in contact with the substrate, and an even smaller fraction—three to four residues on average—that are directly involved in catalysis. The region of the enzyme that binds the substrate and contains the... | Protein | 0.841574 |
1,044 | Because DNA nanoballs remain confined their spots on the patterned array there are no optical duplicates to contend with during bioinformatics analysis of sequencing reads. It is suggested to run Picard MarkDuplicates as follows: java -jar picard.jar MarkDuplicates I=input.bam O=marked_duplicates.bam M=marked_dup_metri... | DNA nanoball sequencing | 0.841562 |
1,045 | Massively parallel next generation sequencing platforms like DNA nanoball sequencing may contribute to the diagnosis and treatment of many genetic diseases. The cost of sequencing an entire human genome has fallen from about one million dollars in 2008, to $4400 in 2010 with the DNA nanoball technology. Sequencing the ... | DNA nanoball sequencing | 0.841562 |
1,046 | DNA nanoball sequencing has been used in recent studies. Lee et al. used this technology to find mutations that were present in a lung cancer and compared them to normal lung tissue. They were able to identify over 50,000 single nucleotide variants. Roach et al. used DNA nanoball sequencing to sequence the genomes of a... | DNA nanoball sequencing | 0.841562 |
1,047 | The nanoballs are then adsorbed onto a sequencing flow cell. The color of the fluorescence at each interrogated position is recorded through a high-resolution camera. Bioinformatics are used to analyze the fluorescence data and make a base call, and for mapping or quantifying the 50bp, 100bp, or 150bp single- or paired... | DNA nanoball sequencing | 0.841562 |
1,048 | Ry receptors are homotetrameric complexes with each subunit exhibiting a molecular size of over 500,000 daltons (about 5,000 amino acyl residues). They possess C-terminal domains with six putative transmembrane α-helical spanners (TMSs). Putative pore-forming sequences occur between the fifth and sixth TMSs as suggeste... | Ryanodine-Inositol 1,4,5-triphosphate receptor calcium channels | 0.841549 |
1,049 | The Protein Information Resource (PIR), located at Georgetown University Medical Center, is an integrated public bioinformatics resource to support genomic and proteomic research, and scientific studies. It contains protein sequences databases | Protein ontology | 0.841547 |
1,050 | PIR was established in 1984 by the National Biomedical Research Foundation as a resource to assist researchers and customers in the identification and interpretation of protein sequence information. Prior to that, the foundation compiled the first comprehensive collection of macromolecular sequences in the Atlas of Pro... | Protein ontology | 0.841547 |
1,051 | In physics and electrical engineering, the universal dielectric response, or UDR, refers to the observed emergent behaviour of the dielectric properties exhibited by diverse solid state systems. In particular this widely observed response involves power law scaling of dielectric properties with frequency under conditio... | Universal dielectric response | 0.841542 |
1,052 | Intuitively, an index e {\displaystyle e} falls into this set if and only if for every m {\displaystyle m} "there is an s {\displaystyle s} such that the Turing machine with index e {\displaystyle e} halts on input m {\displaystyle m} after s {\displaystyle s} steps”. A complete proof would show that the property displ... | Arithmetical hierarchy | 0.841536 |
1,053 | The halting problem for a Δ n 0 , Y {\displaystyle \Delta _{n}^{0,Y}} oracle in fact sits in Σ n + 1 0 , Y {\displaystyle \Sigma _{n+1}^{0,Y}} . Post's theorem establishes a close connection between the arithmetical hierarchy of sets of natural numbers and the Turing degrees. In particular, it establishes the following... | Arithmetical hierarchy | 0.841536 |
1,054 | Since the only Hausdorff topology on a finite set is the discrete one, a finite Hausdorff topological group must necessarily be discrete. It follows that every finite subgroup of a Hausdorff group is discrete. A discrete subgroup H of G is cocompact if there is a compact subset K of G such that HK = G. Discrete normal ... | Discrete group theory | 0.841495 |
1,055 | which is algebraically identical to the formula derived in the previous section. The quantity T E = ε / k {\displaystyle T_{\rm {E}}=\varepsilon /k} has the dimensions of temperature and is a characteristic property of a crystal. It is known as the Einstein temperature. Hence, the Einstein crystal model predicts that t... | Einstein temperature | 0.841488 |
1,056 | The Einstein solid is a model of a crystalline solid that contains a large number of independent three-dimensional quantum harmonic oscillators of the same frequency. The independence assumption is relaxed in the Debye model. While the model provides qualitative agreement with experimental data, especially for the high... | Einstein temperature | 0.841488 |
1,057 | An epigenetic clock is a biochemical test that can be used to measure age. Galkin et al. used deep neural networks to train an epigenetic aging clock of unprecedented accuracy using >6,000 blood samples. The clock uses information from 1000 CpG sites and predicts people with certain conditions older than healthy contro... | Deep Learning | 0.841442 |
1,058 | For this purpose Facebook introduced the feature that once a user is automatically recognized in an image, they receive a notification. They can choose whether of not they like to be publicly labeled on the image, or tell Facebook that it is not them in the picture. This user interface is a mechanism to generate "a con... | Deep Learning | 0.841442 |
1,059 | Most Deep Learning systems rely on training and verification data that is generated and/or annotated by humans. It has been argued in media philosophy that not only low-paid clickwork (e.g. on Amazon Mechanical Turk) is regularly deployed for this purpose, but also implicit forms of human microwork that are often not r... | Deep Learning | 0.841442 |
1,060 | In 2006, Alex Graves, Santiago Fernández, Faustino Gomez, and Schmidhuber combined it with connectionist temporal classification (CTC) in stacks of LSTM RNNs. In 2015, Google's speech recognition reportedly experienced a dramatic performance jump of 49% through CTC-trained LSTM, which they made available through Google... | Deep Learning | 0.841442 |
1,061 | The principle of elevating "raw" features over hand-crafted optimization was first explored successfully in the architecture of deep autoencoder on the "raw" spectrogram or linear filter-bank features in the late 1990s, showing its superiority over the Mel-Cepstral features that contain stages of fixed transformation f... | Deep Learning | 0.841442 |
1,062 | Funded by the US government's NSA and DARPA, SRI studied deep neural networks in speech and speaker recognition. The speaker recognition team led by Larry Heck reported significant success with deep neural networks in speech processing in the 1998 National Institute of Standards and Technology Speaker Recognition evalu... | Deep Learning | 0.841442 |
1,063 | Each layer in the feature extraction module extracted features with growing complexity regarding the previous layer.In 1995, Brendan Frey demonstrated that it was possible to train (over two days) a network containing six fully connected layers and several hundred hidden units using the wake-sleep algorithm, co-develop... | Deep Learning | 0.841442 |
1,064 | In 2015, Rupesh Kumar Srivastava, Klaus Greff, and Schmidhuber used LSTM principles to create the Highway network, a feedforward neural network with hundreds of layers, much deeper than previous networks. 7 months later, Kaiming He, Xiangyu Zhang; Shaoqing Ren, and Jian Sun won the ImageNet 2015 competition with an ope... | Deep Learning | 0.841442 |
1,065 | It combines this with a softmax operator and a projection matrix. Transformers have increasingly become the model of choice for natural language processing. Many modern large language models such as ChatGPT, GPT-4, and BERT use it. | Deep Learning | 0.841442 |
1,066 | In 1993, a chunker solved a deep learning task whose depth exceeded 1000.In 1992, Jürgen Schmidhuber also published an alternative to RNNs which is now called a linear Transformer or a Transformer with linearized self-attention (save for a normalization operator). It learns internal spotlights of attention: a slow feed... | Deep Learning | 0.841442 |
1,067 | It uses predictive coding to learn internal representations at multiple self-organizing time scales. This can substantially facilitate downstream deep learning. The RNN hierarchy can be collapsed into a single RNN, by distilling a higher level chunker network into a lower level automatizer network. | Deep Learning | 0.841442 |
1,068 | LeNet-5 (1998), a 7-level CNN by Yann LeCun et al., that classifies digits, was applied by several banks to recognize hand-written numbers on checks digitized in 32x32 pixel images. In the 1980s, backpropagation did not work well for deep learning with long credit assignment paths. To overcome this problem, Jürgen Schm... | Deep Learning | 0.841442 |
1,069 | The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, and to artificial neural networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons.In 1988, Wei Zhang et al. applied the backpropagation algorithm to a convolutional neural network (a si... | Deep Learning | 0.841442 |
1,070 | If so, how fast? What is it approximating?) Deep learning methods are often looked at as a black box, with most confirmations done empirically, rather than theoretically.Others point out that deep learning should be looked at as a step towards realizing strong AI, not as an all-encompassing solution. | Deep Learning | 0.841442 |
1,071 | In particular, GPUs are well-suited for the matrix/vector computations involved in machine learning. GPUs speed up training algorithms by orders of magnitude, reducing running times from weeks to days. Further, specialized hardware and algorithm optimizations can be used for efficient processing of deep learning models... | Deep Learning | 0.841442 |
1,072 | Advances in hardware have driven renewed interest in deep learning. In 2009, Nvidia was involved in what was called the "big bang" of deep learning, "as deep-learning neural networks were trained with Nvidia graphics processing units (GPUs)". That year, Andrew Ng determined that GPUs could increase the speed of deep-le... | Deep Learning | 0.841442 |
1,073 | Results on commonly used evaluation sets such as TIMIT (ASR) and MNIST (image classification), as well as a range of large-vocabulary speech recognition tasks have steadily improved. Convolutional neural networks (CNNs) were superseded for ASR by CTC for LSTM. but are more successful in computer vision. | Deep Learning | 0.841442 |
1,074 | Analysis around 2009–2010, contrasting the GMM (and other generative speech models) vs. DNN models, stimulated early industrial investment in deep learning for speech recognition. That analysis was done with comparable performance (less than 1.5% in error rate) between discriminative DNNs and generative models. In 2010... | Deep Learning | 0.841442 |
1,075 | It was believed that pre-training DNNs using generative models of deep belief nets (DBN) would overcome the main difficulties of neural nets. However, it was discovered that replacing pre-training with large amounts of training data for straightforward backpropagation when using DNNs with large, context-dependent outpu... | Deep Learning | 0.841442 |
1,076 | In 2006, publications by Geoff Hinton, Ruslan Salakhutdinov, Osindero and Teh showed how a many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then fine-tuning it using supervised backpropagation. The ... | Deep Learning | 0.841442 |
1,077 | LSTM recurrent neural networks can learn "very deep learning" tasks with long credit assignment paths that require memories of events that happened thousands of discrete time steps before. The "vanilla LSTM" with forget gate was introduced in 1999 by Felix Gers, Schmidhuber and Fred Cummins. LSTM has become the most ci... | Deep Learning | 0.841442 |
1,078 | It not only tested the neural history compressor, but also identified and analyzed the vanishing gradient problem. Hochreiter proposed recurrent residual connections to solve this problem. This led to the deep learning method called long short-term memory (LSTM), published in 1997. | Deep Learning | 0.841442 |
1,079 | Excellent image quality is achieved by Nvidia's StyleGAN (2018) based on the Progressive GAN by Tero Karras et. al. Here the GAN generator is grown from small to large scale in a pyramidal fashion. Sepp Hochreiter's diploma thesis (1991) was called "one of the most important documents in the history of machine learning... | Deep Learning | 0.841442 |
1,080 | Transformers are also increasingly being used in computer vision.In 1991, Jürgen Schmidhuber also published adversarial neural networks that contest with each other in the form of a zero-sum game, where one network's gain is the other network's loss. The first network is a generative model that models a probability dis... | Deep Learning | 0.841442 |
1,081 | In 1969, he also introduced the ReLU (rectified linear unit) activation function. The rectifier has become the most popular activation function for CNNs and deep learning in general. CNNs have become an essential tool for computer vision. | Deep Learning | 0.841442 |
1,082 | The terminology "back-propagating errors" was actually introduced in 1962 by Rosenblatt, but he did not know how to implement this, although Henry J. Kelley had a continuous precursor of backpropagation already in 1960 in the context of control theory. In 1982, Paul Werbos applied backpropagation to MLPs in the way tha... | Deep Learning | 0.841442 |
1,083 | However, since only the output layer had learning connections, this was not yet deep learning. It was what later was called an extreme learning machine.The first general, working learning algorithm for supervised, deep, feedforward, multilayer perceptrons was published by Alexey Ivakhnenko and Lapa in 1967. A 1971 pape... | Deep Learning | 0.841442 |
1,084 | His learning RNN was popularised by John Hopfield in 1982. RNNs have become central for speech recognition and language processing. Charles Tappert writes that Frank Rosenblatt developed and explored all of the basic ingredients of the deep learning systems of today, referring to Rosenblatt's 1962 book which introduced... | Deep Learning | 0.841442 |
1,085 | There are two types of neural networks: feedforward neural networks (FNNs) and recurrent neural networks (RNNs). RNNs have cycles in their connectivity structure, FNNs don't. In the 1920s, Wilhelm Lenz and Ernst Ising created and analyzed the Ising model which is essentially a non-learning RNN architecture consisting o... | Deep Learning | 0.841442 |
1,086 | Deep learning is being successfully applied to financial fraud detection, tax evasion detection, and anti-money laundering. | Deep Learning | 0.841442 |
1,087 | Deep learning is a class of machine learning algorithms that: 199–200 uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or ... | Deep Learning | 0.841442 |
1,088 | Deep learning has been successfully applied to inverse problems such as denoising, super-resolution, inpainting, and film colorization. These applications include learning methods such as "Shrinkage Fields for Effective Image Restoration" which trains on an image dataset, and Deep Image Prior, which trains on the image... | Deep Learning | 0.841442 |
1,089 | Large processing capabilities of many-core architectures (such as GPUs or the Intel Xeon Phi) have produced significant speedups in training, because of the suitability of such processing architectures for the matrix and vector computations.Alternatively, engineers may look for other types of neural networks with more ... | Deep Learning | 0.841442 |
1,090 | The most powerful A.I. systems, like Watson (...) use techniques like deep learning as just one element in a very complicated ensemble of techniques, ranging from the statistical technique of Bayesian inference to deductive reasoning. In further reference to the idea that artistic sensitivity might be inherent in relat... | Deep Learning | 0.841442 |
1,091 | Despite the power of deep learning methods, they still lack much of the functionality needed for realizing this goal entirely. Research psychologist Gary Marcus noted: Realistically, deep learning is only part of the larger challenge of building intelligent machines. Such techniques lack ways of representing causal rel... | Deep Learning | 0.841442 |
1,092 | A large percentage of candidate drugs fail to win regulatory approval. These failures are caused by insufficient efficacy (on-target effect), undesired interactions (off-target effects), or unanticipated toxic effects. Research has explored use of deep learning to predict the biomolecular targets, off-targets, and toxi... | Deep Learning | 0.841442 |
1,093 | Recommendation systems have used deep learning to extract meaningful features for a latent factor model for content-based music and journal recommendations. Multi-view deep learning has been applied for learning user preferences from multiple domains. The model uses a hybrid collaborative and content-based approach and... | Deep Learning | 0.841442 |
1,094 | Physics informed neural networks have been used to solve partial differential equations in both forward and inverse problems in a data driven manner. One example is the reconstructing fluid flow governed by the Navier-Stokes equations. Using physics informed neural networks does not require the often expensive mesh gen... | Deep Learning | 0.841442 |
1,095 | This lets the strength of the acoustic modeling aspects of speech recognition be more easily analyzed. The error rates listed below, including these early results and measured as percent phone error rates (PER), have been summarized since 1991. The debut of DNNs for speaker recognition in the late 1990s and speech reco... | Deep Learning | 0.841442 |
1,096 | Large-scale automatic speech recognition is the first and most convincing successful case of deep learning. LSTM RNNs can learn "Very Deep Learning" tasks that involve multi-second intervals containing speech events separated by thousands of discrete time steps, where one time step corresponds to about 10 ms. LSTM with... | Deep Learning | 0.841442 |
1,097 | Program comprehension (also program understanding or code comprehension) is a domain of computer science concerned with the ways software engineers maintain existing source code. The cognitive and other processes involved are identified and studied. The results are used to develop tools and training. Software maintena... | Program comprehension | 0.841421 |
1,098 | Titles of works on program comprehension include Using a behavioral theory of program comprehension in software engineering The concept assignment problem in program understanding, and Program Comprehension During Software Maintenance and Evolution.Computer scientists pioneering program comprehension include Ruven Broo... | Program comprehension | 0.841421 |
1,099 | Assess the quality of the clustering by adding up the variation within each cluster. Repeat the processes with different values of k. Pick the best value for 'k' by finding the "elbow" in the plot of which k value has the lowest variance.One example of this in biology is used in the 3D mapping of a genome. Information ... | Computational biologist | 0.841404 |
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