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Section: Software > Cores. Specialized molecular dynamics programs, referred to as "FahCores" and often abbreviated "cores", perform the calculations on the work unit as a background process. A large majority of Folding@home's cores are based on GROMACS, one of the fastest and most popular molecular dynamics software p... | Wikipedia - Folding@home - Software > Cores | 254 | 1,303 | null |
Section: Software > Client. A Folding@home participant installs a client program on their personal computer. The user interacts with the client, which manages the other software components in the background. Through the client, the user may pause the folding process, open an event log, check the work progress, or view ... | Wikipedia - Folding@home - Software > Client | 316 | 1,680 | null |
Clients can be downloaded only from the official Folding@home website or its commercial partners, and will only interact with Folding@home computer files. They will upload and download data with Folding@home's data servers (over port 8080, with 80 as an alternate), and the communication is verified using 2048-bit digit... | Wikipedia - Folding@home - Software > Client | 333 | 1,680 | null |
Section: Software > Client > Graphics processing units. The specialized hardware of graphics processing units (GPU) is designed to accelerate rendering of 3-D graphics applications such as video games and can significantly outperform CPUs for some types of calculations. GPUs are one of the most powerful and rapidly gro... | Wikipedia - Folding@home - Software > Client > Graphics processing units | 338 | 1,829 | null |
These errors strongly correlated to board architecture, though the study concluded that reliable GPU computing was very feasible as long as attention is paid to the hardware traits, such as software-side error detection. The first generation of Folding@home's GPU client (GPU1) was released to the public on October 2, 2... | Wikipedia - Folding@home - Software > Client > Graphics processing units | 349 | 1,589 | null |
Section: Software > Client > PlayStation 3. From March 2007 until November 2012, Folding@home took advantage of the computing power of PlayStation 3s. At the time of its inception, its main streaming Cell processor delivered a 20 times speed increase over PCs for some calculations, processing power which could not be f... | Wikipedia - Folding@home - Software > Client > PlayStation 3 | 330 | 1,665 | null |
Section: Software > Client > Multi-core processing client. Folding@home can use the parallel computing abilities of modern multi-core processors. The ability to use several CPU cores simultaneously allows completing the full simulation far faster. Working together, these CPU cores complete single work units proportiona... | Wikipedia - Folding@home - Software > Client > Multi-core processing client | 344 | 1,795 | null |
SMP2 supports a trial of a special category of bigadv work units, designed to simulate proteins that are unusually large and computationally intensive and have a great scientific priority. These units originally required a minimum of eight CPU cores, which was raised to sixteen later, on February 7, 2012. Along with th... | Wikipedia - Folding@home - Software > Client > Multi-core processing client | 254 | 1,266 | null |
Section: Software > Client > V7. The V7 client is the seventh generation of the Folding@home client software, and is a full rewrite and unification of the prior clients for Windows, macOS, and Linux operating systems. It was released on March 22, 2012. Like its predecessors, V7 can run Folding@home in the background at... | Wikipedia - Folding@home - Software > Client > V7 | 312 | 1,392 | null |
Section: Comparison to other molecular simulators. Rosetta@home is a distributed computing project aimed at protein structure prediction and is one of the most accurate tertiary structure predictors. The conformational states from Rosetta's software can be used to initialize a Markov state model as starting points for ... | Wikipedia - Folding@home - Comparison to other molecular simulators | 325 | 1,655 | null |
Article: General Architecture for Text Engineering. General Architecture for Text Engineering (GATE) is a Java suite of natural language processing (NLP) tools for man tasks, including information extraction in many languages. It is now used worldwide by a wide community of scientists, companies, teachers and students.... | Wikipedia - General Architecture for Text Engineering - Summary | 255 | 1,223 | null |
Section: Features. GATE includes an information extraction system called ANNIE (A Nearly-New Information Extraction System) which is a set of modules comprising a tokenizer, a gazetteer, a sentence splitter, a part of speech tagger, a named entities transducer and a coreference tagger. ANNIE can be used as-is to provid... | Wikipedia - General Architecture for Text Engineering - Features | 297 | 1,304 | null |
Section: Octave, the language. The Octave language is an interpreted programming language. It is a structured programming language (similar to C) and supports many common C standard library functions, and also certain UNIX system calls and functions. However, it does not support passing arguments by reference although ... | Wikipedia - GNU Octave - Octave, the language | 183 | 970 | null |
Section: Notable features > Unwind-protect. Octave supports a limited form of exception handling modelled after the unwind_protect of Lisp. The general form of an unwind_protect block looks like this: As a general rule, GNU Octave recognizes as termination of a given block either the keyword end (which is compatible wi... | Wikipedia - GNU Octave - Notable features > Unwind-protect | 258 | 1,215 | null |
Section: MATLAB compatibility. Octave has been built with MATLAB compatibility in mind, and shares many features with MATLAB: Matrices as fundamental data type. Built-in support for complex numbers. Powerful built-in math functions and extensive function libraries. Extensibility in the form of user-defined functions. O... | Wikipedia - GNU Octave - MATLAB compatibility | 165 | 812 | null |
Section: MATLAB compatibility > Syntax compatibility. There are a few purposeful, albeit minor, syntax additions Archived 2012-04-26 at the Wayback Machine: Comment lines can be prefixed with the # character as well as the % character; Various C-based operators ++, --, +=, *=, /= are supported; Elements can be referenc... | Wikipedia - GNU Octave - MATLAB compatibility > Syntax compatibility | 226 | 928 | null |
Article: Gremlin (query language). Gremlin is a graph traversal language and virtual machine developed by Apache TinkerPop of the Apache Software Foundation. Gremlin works for both OLTP-based graph databases as well as OLAP-based graph processors. Gremlin's automata and functional language foundation enable Gremlin to ... | Wikipedia - Gremlin (query language) - Summary | 184 | 847 | null |
Section: History. 2009-10-30 the project is born, and immediately named "TinkerPop" 2009-12-25 v0.1 is the first release 2011-05-21 v1.0 is released 2012-05-24 v2.0 is released 2015-01-16 TinkerPop becomes an Apache Incubator project 2015-07-09 v3.0.0-incubating is released 2016-05-23 Apache TinkerPop becomes a top-lev... | Wikipedia - Gremlin (query language) - History | 182 | 518 | null |
Section: Gremlin graph traversal machine > Gremlin steps (instruction set). The following traversal is a Gremlin traversal in the Gremlin-Java8 dialect. The Gremlin language (i.e. the fluent-style of expressing a graph traversal) can be represented in any host language that supports function composition and function ne... | Wikipedia - Gremlin (query language) - Gremlin graph traversal machine > Gremlin steps (instruction set) | 344 | 1,498 | null |
Section: Features. ilastik allows user to annotate an arbitrary number of classes in images with a mouse interface. Using these user annotations and the generic (nonlinear) image features, the user can train a random forest classifier. Trained ilastik classifiers can be applied new data not included in the training set... | Wikipedia - Ilastik - Features | 187 | 843 | null |
Article: Machine learning. Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, adv... | Wikipedia - Machine learning - Summary | 191 | 1,081 | null |
Section: History. The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used in this time period. Although the earliest machine learning model was introduced in the 1950s when A... | Wikipedia - Machine learning - History | 348 | 1,914 | null |
Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using teaching strategies so that an artificial neural network learns to recognise 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. Tom M. Mitchell... | Wikipedia - Machine learning - History | 280 | 1,487 | null |
Section: Relationships to other fields > Artificial intelligence. As a scientific endeavour, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the pro... | Wikipedia - Machine learning - Relationships to other fields > Artificial intelligence | 339 | 1,669 | null |
Section: Relationships to other fields > Data mining. Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown propert... | Wikipedia - Machine learning - Relationships to other fields > Data mining | 339 | 1,757 | null |
Section: Relationships to other fields > Statistics. Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalisable predictive patterns. According to Michael I. Jordan,... | Wikipedia - Machine learning - Relationships to other fields > Statistics | 241 | 1,326 | null |
Section: Theory. A core objective of a learner is to generalise from its experience. Generalisation in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability di... | Wikipedia - Machine learning - Theory | 337 | 1,927 | null |
Section: Approaches. Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system: Supervised learning: The computer is presented with example inputs and their desired out... | Wikipedia - Machine learning - Approaches | 214 | 1,082 | null |
Section: Approaches > Supervised learning. Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data, known as training data, consists of a set of training examples. Each training example has one or more inputs and the desired output, also... | Wikipedia - Machine learning - Approaches > Supervised learning | 342 | 1,932 | null |
Section: Approaches > Unsupervised learning. Unsupervised learning algorithms find structures in data that has not been labelled, classified or categorised. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonali... | Wikipedia - Machine learning - Approaches > Unsupervised learning | 236 | 1,250 | null |
Section: Approaches > Reinforcement learning. Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximise some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, c... | Wikipedia - Machine learning - Approaches > Reinforcement learning | 164 | 930 | null |
Section: Approaches > Dimensionality reduction. Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables. In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the ... | Wikipedia - Machine learning - Approaches > Dimensionality reduction | 161 | 839 | null |
Section: Approaches > Other types > Self-learning. Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array (CAA). It gives a solution to the problem learning without any external reward, by introducing emotion as an interna... | Wikipedia - Machine learning - Approaches > Other types > Self-learning | 345 | 1,662 | null |
Section: Approaches > Other types > Feature learning. Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal component analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, oft... | Wikipedia - Machine learning - Approaches > Other types > Feature learning | 340 | 1,988 | null |
Section: Approaches > Other types > Sparse dictionary learning. Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions and assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. A popular heu... | Wikipedia - Machine learning - Approaches > Other types > Sparse dictionary learning | 167 | 927 | null |
Section: Approaches > Other types > Anomaly detection. In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such a... | Wikipedia - Machine learning - Approaches > Other types > Anomaly detection | 337 | 1,774 | null |
Section: Approaches > Other types > Association rules. Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness". Rule-based machine learnin... | Wikipedia - Machine learning - Approaches > Other types > Association rules | 331 | 1,663 | null |
Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatic... | Wikipedia - Machine learning - Approaches > Other types > Association rules | 344 | 1,882 | null |
Section: Models > Artificial neural networks. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any ta... | Wikipedia - Machine learning - Models > Artificial neural networks | 334 | 1,796 | null |
Section: Models > Decision trees. Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modelling approaches used in statistics, data mini... | Wikipedia - Machine learning - Models > Decision trees | 174 | 951 | null |
Section: Models > Support-vector machines. Support-vector machines (SVMs), also known as support-vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds... | Wikipedia - Machine learning - Models > Support-vector machines | 154 | 780 | null |
Section: Models > Regression analysis. Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical... | Wikipedia - Machine learning - Models > Regression analysis | 231 | 1,334 | null |
Section: Models > Bayesian networks. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilis... | Wikipedia - Machine learning - Models > Bayesian networks | 150 | 804 | null |
Section: Models > Gaussian processes. A Gaussian process is a stochastic process in which every finite collection of the random variables in the process has a multivariate normal distribution, and it relies on a pre-defined covariance function, or kernel, that models how pairs of points relate to each other depending o... | Wikipedia - Machine learning - Models > Gaussian processes | 157 | 731 | null |
Section: Models > Belief functions. The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and imprecise probability theories. These theoretical... | Wikipedia - Machine learning - Models > Belief functions | 229 | 1,242 | null |
Section: Models > Training models. Typically, machine learning models require a high quantity of reliable data to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as va... | Wikipedia - Machine learning - Models > Training models | 173 | 954 | null |
Section: Applications. There are many applications for machine learning, including: In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 1... | Wikipedia - Machine learning - Applications | 330 | 1,759 | null |
Recently, machine learning technology was also applied to optimise smartphone's performance and thermal behaviour based on the user's interaction with the phone. When applied correctly, machine learning algorithms (MLAs) can utilise a wide range of company characteristics to predict stock returns without overfitting. B... | Wikipedia - Machine learning - Applications | 263 | 1,514 | null |
Section: Limitations. Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and pe... | Wikipedia - Machine learning - Limitations | 305 | 1,658 | null |
Section: Limitations > Other limitations and vulnerabilities. Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike... | Wikipedia - Machine learning - Limitations > Other limitations and vulnerabilities | 293 | 1,432 | null |
Section: Model assessments. Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the tes... | Wikipedia - Machine learning - Model assessments | 260 | 1,318 | null |
Section: Ethics > Bias. Different machine learning approaches can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on human-made data, machine lea... | Wikipedia - Machine learning - Ethics > Bias | 344 | 1,825 | null |
In fact, according to research carried out by the Computing Research Association (CRA) in 2021, "female faculty merely make up 16.1%" of all faculty members who focus on AI among several universities around the world. Furthermore, among the group of "new U.S. resident AI PhD graduates," 45% identified as white, 22.4% a... | Wikipedia - Machine learning - Ethics > Bias | 348 | 1,670 | null |
Section: Machine learning tools > Machine learning algorithms. Almeida–Pineda recurrent backpropagation ALOPEX Backpropagation Bootstrap aggregating CN2 algorithm Constructing skill trees Dehaene–Changeux model Diffusion map Dominance-based rough set approach Dynamic time warping Error-driven learning Evolutionary mult... | Wikipedia - Outline of machine learning - Machine learning tools > Machine learning algorithms | 283 | 1,491 | null |
Section: Machine learning methods > Supervised learning. Supervised learning Averaged one-dependence estimators (AODE) Artificial neural network Case-based reasoning Gaussian process regression Gene expression programming Group method of data handling (GMDH) Inductive logic programming Instance-based learning Lazy lear... | Wikipedia - Outline of machine learning - Machine learning methods > Supervised learning | 169 | 965 | null |
Section: Persons influential in machine learning. Alberto Broggi Andrei Knyazev Andrew McCallum Andrew Ng Anuraag Jain Armin B. Cremers Ayanna Howard Barney Pell Ben Goertzel Ben Taskar Bernhard Schölkopf Brian D. Ripley Christopher G. Atkeson Corinna Cortes Demis Hassabis Douglas Lenat Eric Xing Ernst Dickmanns Geoffr... | Wikipedia - Outline of machine learning - Persons influential in machine learning | 289 | 1,059 | null |
Article: 80 Million Tiny Images. 80 Million Tiny Images is a dataset intended for training machine learning systems constructed by Antonio Torralba, Rob Fergus, and William T. Freeman in a collaboration between MIT and New York University. It was published in 2008. The dataset has size 760 GB. It contains 79,302,017 32... | Wikipedia - 80 Million Tiny Images - Summary | 209 | 948 | null |
Section: Construction. It was first reported in a technical report in April 2007, during the middle of the construction process, when there were only 73 million images. The full dataset was published in 2008. They began with all 75,846 nonabstract nouns in WordNet, and then for each of these nouns, they scraped 7 Image... | Wikipedia - 80 Million Tiny Images - Construction | 239 | 1,009 | null |
Article: A Logical Calculus of the Ideas Immanent in Nervous Activity. "A Logical Calculus of the Ideas Immanent to Nervous Activity" is a 1943 article written by Warren McCulloch and Walter Pitts. The paper, published in the journal The Bulletin of Mathematical Biophysics, proposed a mathematical model of the nervous ... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Summary | 174 | 899 | null |
Section: Mathematics. The artificial neuron used in the original paper is slightly different from the modern version. They considered neural networks that operate in discrete steps of time t = 0 , 1 , … {\displaystyle t=0,1,\dots } . The neural network contains a number of neurons. Let the state of a neuron i {\display... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Mathematics | 282 | 1,137 | null |
A neural network has a circle or a circuit if there exists a directed circle in the graph. Let w i j ( t ) {\displaystyle w_{ij}(t)} be the connection weight from neuron j {\displaystyle j} to neuron i {\displaystyle i} at time t {\displaystyle t} , then its next state is N i ( t + 1 ) = H ( ∑ j = 1 n w i j ( t ) N j (... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Mathematics | 211 | 563 | null |
Section: Mathematics > Symbolic logic. The paper used, as a logical language for describing neural networks, "Language II" from The Logical Syntax of Language by Rudolf Carnap with some notations taken from Principia Mathematica by Alfred North Whitehead and Bertrand Russell. Language II covers substantial parts of cla... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Mathematics > Symbolic logic | 311 | 1,016 | null |
To describe a neural network with peripheral afferents N 1 , N 2 , … , N p {\displaystyle N_{1},N_{2},\dots ,N_{p}} and non-peripheral afferents N p + 1 , N p + 2 , … , N n {\displaystyle N_{p+1},N_{p+2},\dots ,N_{n}} they considered logical predicate of form P r ( N 1 , N 2 , … , N p , t ) {\displaystyle Pr(N_{1},N_{2... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Mathematics > Symbolic logic | 440 | 1,109 | null |
Intuitively speaking, N 1 , … , N p {\displaystyle N_{1},\dots ,N_{p}} specifies the binary input patterns going into the neural network over all time, and P r ( N 1 , N 2 , … , N n , t ) {\displaystyle Pr(N_{1},N_{2},\dots ,N_{n},t)} is a function that takes some binary input patterns, and constructs an output binary ... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Mathematics > Symbolic logic | 398 | 1,025 | null |
A logical sentence P r ( N 1 , N 2 , … , N n , t ) {\displaystyle Pr(N_{1},N_{2},\dots ,N_{n},t)} is realized by a neural network iff there exists a time-delay T ≥ 0 {\displaystyle T\geq 0} , a neuron i {\displaystyle i} in the network, and an initial state for the non-peripheral neurons N p + 1 ( 0 ) , … , N n ( 0 ) {... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Mathematics > Symbolic logic | 290 | 706 | null |
Section: Mathematics > Equivalence. In the paper, they considered some alternative definitions of artificial neural networks, and have shown them to be equivalent, that is, neural networks under one definition realizes precisely the same logical sentences as neural networks under another definition. They considered thr... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Mathematics > Equivalence | 324 | 1,292 | null |
The definition above is spatial summation (which they pictured as having multiple synapses placed close together, so that the effect of their firing sums up). By "temporal summation" they meant that the total incoming signal is ∑ τ = 0 T ∑ j = 1 n w i j ( t ) N j ( t − τ ) {\displaystyle \sum _{\tau =0}^{T}\sum _{j=1}^... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Mathematics > Equivalence | 315 | 976 | null |
Section: Mathematics > Logical expressivity. They considered "temporal propositional expressions" (TPE), which are propositional formulas with one free variable t {\displaystyle t} . For example, N 1 ( t ) ∨ N 2 ( t ) ∧ ¬ N 3 ( t ) {\displaystyle N_{1}(t)\vee N_{2}(t)\wedge \neg N_{3}(t)} is such an expression. Theorem... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Mathematics > Logical expressivity | 331 | 1,312 | null |
Section: Context > Previous work. The paper built upon several previous strands of work. In the symbolic logic side, it built on the previous work by Carnap, Whitehead, and Russell. This was contributed by Walter Pitts, who had a strong proficiency with symbolic logic. Pitts provided mathematical and logical rigor to M... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Context > Previous work | 348 | 1,735 | null |
He started studying under Carnap and attending classes during 1938--1943. He wrote several early papers on neuronal network modelling and regularly attended Rashevsky's seminars in theoretical biology. The seminar attendants included Gerhard von Bonin and Householder. In 1940, von Bonin introduced Lettvin to McCulloch.... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Context > Previous work | 215 | 1,030 | null |
Section: Context > Subsequent work. It was a foundational result in automata theory. John von Neumann cited it as a significant result. This work led to work on neural networks and their link to finite automata. Kleene introduced the term "regular" for "regular language" in a 1951 technical report, where Kleene proved ... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Context > Subsequent work | 316 | 1,513 | null |
Norbert Wiener found this a significant evidence for a general method for how animals recognizing objects, by scanning a scene from multiple transformations and finding a canonical representation. He hypothesized that this "scanning" activity is clocked by the alpha wave, which he mistakenly thought was tightly regulat... | Wikipedia - A Logical Calculus of the Ideas Immanent in Nervous Activity - Context > Subsequent work | 160 | 796 | null |
Article: Accelerated Linear Algebra. XLA (Accelerated Linear Algebra) is an open-source compiler for machine learning developed by the OpenXLA project. XLA is designed to improve the performance of machine learning models by optimizing the computation graphs at a lower level, making it particularly useful for large-sca... | Wikipedia - Accelerated Linear Algebra - Summary | 191 | 1,054 | null |
Article: Action model learning. Action model learning (sometimes abbreviated action learning) is an area of machine learning concerned with the creation and modification of a software agent's knowledge about the effects and preconditions of the actions that can be executed within its environment. This knowledge is usua... | Wikipedia - Action model learning - Summary | 193 | 1,080 | null |
Section: Action models. Given a training set E {\displaystyle E} consisting of examples e = ( s , a , s ′ ) {\displaystyle e=(s,a,s')} , where s , s ′ {\displaystyle s,s'} are observations of a world state from two consecutive time steps t , t ′ {\displaystyle t,t'} and a {\displaystyle a} is an action instance observe... | Wikipedia - Action model learning - Action models | 254 | 966 | null |
Section: Action learning methods > State of the art. Recent action learning methods take various approaches and employ a wide variety of tools from different areas of artificial intelligence and computational logic. As an example of a method based on propositional logic, we can mention SLAF (Simultaneous Learning and F... | Wikipedia - Action model learning - Action learning methods > State of the art | 242 | 1,254 | null |
Article: Active learning (machine learning). Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem doma... | Wikipedia - Active learning (machine learning) - Summary | 308 | 1,683 | null |
Section: Definitions. Let T be the total set of all data under consideration. For example, in a protein engineering problem, T would include all proteins that are known to have a certain interesting activity and all additional proteins that one might want to test for that activity. During each iteration, i, T is broken... | Wikipedia - Active learning (machine learning) - Definitions | 184 | 711 | null |
Section: Scenarios. Pool-based sampling: In this approach, which is the most well known scenario, the learning algorithm attempts to evaluate the entire dataset before selecting data points (instances) for labeling. It is often initially trained on a fully labeled subset of the data using a machine-learning method such... | Wikipedia - Active learning (machine learning) - Scenarios | 331 | 1,676 | null |
As contrasted with Pool-based sampling, the obvious drawback of stream-based methods is that the learning algorithm does not have sufficient information, early in the process, to make a sound assign-label-vs ask-teacher decision, and it does not capitalize as efficiently on the presence of already labeled data. Therefo... | Wikipedia - Active learning (machine learning) - Scenarios | 270 | 1,388 | null |
Section: Query strategies. Algorithms for determining which data points should be labeled can be organized into a number of different categories, based upon their purpose: Balance exploration and exploitation: the choice of examples to label is seen as a dilemma between the exploration and the exploitation over the dat... | Wikipedia - Active learning (machine learning) - Query strategies | 350 | 1,873 | null |
Variance reduction: label those points that would minimize output variance, which is one of the components of error. Conformal prediction: predicts that a new data point will have a label similar to old data points in some specified way and degree of the similarity within the old examples is used to estimate the confid... | Wikipedia - Active learning (machine learning) - Query strategies | 297 | 1,616 | null |
Section: Literature. Improving Generalization with Active Learning, David Cohn, Les Atlas & Richard Ladner, Machine Learning 15, 201–221 (1994). https://doi.org/10.1007/BF00993277 Balcan, Maria-Florina & Hanneke, Steve & Wortman, Jennifer. (2008). The True Sample Complexity of Active Learning.. 45-56. https://link.spri... | Wikipedia - Active learning (machine learning) - Literature | 219 | 692 | null |
Section: History. At the MIT Spam Conference in January 2004, John Graham-Cumming showed that a machine-learning spam filter could be used to defeat another machine-learning spam filter by automatically learning which words to add to a spam email to get the email classified as not spam. In 2004, Nilesh Dalvi and others... | Wikipedia - Adversarial machine learning - History | 334 | 1,557 | null |
For example, any small rotation or slight illumination on an adversarial image can destroy the adversariality. In addition, researchers such as Google Brain's Nicholas Frosst point out that it is much easier to make self-driving cars miss stop signs by physically removing the sign itself, rather than creating adversari... | Wikipedia - Adversarial machine learning - History | 205 | 1,072 | null |
Section: History > Examples. Examples include attacks in spam filtering, where spam messages are obfuscated through the misspelling of "bad" words or the insertion of "good" words; attacks in computer security, such as obfuscating malware code within network packets or modifying the characteristics of a network flow to... | Wikipedia - Adversarial machine learning - History > Examples | 349 | 1,701 | null |
Section: Attack modalities > Taxonomy. Attacks against (supervised) machine learning algorithms have been categorized along three primary axes: influence on the classifier, the security violation and their specificity. Classifier influence: An attack can influence the classifier by disrupting the classification phase. ... | Wikipedia - Adversarial machine learning - Attack modalities > Taxonomy | 208 | 1,191 | null |
Section: Attack modalities > Strategies > Data poisoning. Poisoning consists of contaminating the training dataset with data designed to increase errors in the output. Given that learning algorithms are shaped by their training datasets, poisoning can effectively reprogram algorithms with potentially malicious intent. ... | Wikipedia - Adversarial machine learning - Attack modalities > Strategies > Data poisoning | 279 | 1,508 | null |
Section: Attack modalities > Strategies > Byzantine attacks. As machine learning is scaled, it often relies on multiple computing machines. In federated learning, for instance, edge devices collaborate with a central server, typically by sending gradients or model parameters. However, some of these devices may deviate ... | Wikipedia - Adversarial machine learning - Attack modalities > Strategies > Byzantine attacks | 276 | 1,385 | null |
Section: Attack modalities > Strategies > Evasion. Evasion attacks consist of exploiting the imperfection of a trained model. For instance, spammers and hackers often attempt to evade detection by obfuscating the content of spam emails and malware. Samples are modified to evade detection; that is, to be classified as l... | Wikipedia - Adversarial machine learning - Attack modalities > Strategies > Evasion | 154 | 751 | null |
Section: Attack modalities > Strategies > Model extraction. Model extraction involves an adversary probing a black box machine learning system in order to extract the data it was trained on. This can cause issues when either the training data or the model itself is sensitive and confidential. For example, model extract... | Wikipedia - Adversarial machine learning - Attack modalities > Strategies > Model extraction | 248 | 1,393 | null |
Section: Categories > Adversarial attacks and training in linear models. There is a growing literature about adversarial attacks in linear models. Indeed, since the seminal work from Goodfellow at al. studying these models in linear models has been an important tool to understand how adversarial attacks affect machine ... | Wikipedia - Adversarial machine learning - Categories > Adversarial attacks and training in linear models | 283 | 1,436 | null |
Section: Specific attack types > Adversarial examples > Black box attacks > Simple Black-box Adversarial Attacks. Simple Black-box Adversarial Attacks is a query-efficient way to attack black-box image classifiers. Take a random orthonormal basis v 1 , v 2 , … , v d {\displaystyle v_{1},v_{2},\dots ,v_{d}} in R d {\dis... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > Black box attacks > Simple Black-box Adversarial Attacks | 326 | 1,100 | null |
Section: Specific attack types > Adversarial examples > Black box attacks > Square Attack. The Square Attack was introduced in 2020 as a black box evasion adversarial attack based on querying classification scores without the need of gradient information. As a score based black box attack, this adversarial approach is ... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > Black box attacks > Square Attack | 350 | 1,258 | null |
The goal of this attack is as follows: argmax k = 1 , . . . , K f k ( x ^ ) ≠ y , | | x ^ − x | | p ≤ ϵ and x ^ ∈ [ 0 , 1 ] d {\displaystyle {\text{argmax}}_{k=1,...,K}f_{k}({\hat {x}})\neq y,||{\hat {x}}-x||_{p}\leq \epsilon {\text{ and }}{\hat {x}}\in [0,1]^{d}} In other words, finding some perturbed adversarial exam... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > Black box attacks > Square Attack | 213 | 510 | null |
, K f k ( x ^ ) ≠ y , | | x ^ − x | | p ≤ ϵ and x ^ ∈ [ 0 , 1 ] d {\displaystyle {\text{argmax}}_{k=1,...,K}f_{k}({\hat {x}})\neq y,||{\hat {x}}-x||_{p}\leq \epsilon {\text{ and }}{\hat {x}}\in [0,1]^{d}} In other words, finding some perturbed adversarial example x ^ {\textstyle {\hat {x}}} such that the classifier inc... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > Black box attacks > Square Attack | 353 | 828 | null |
The paper then defines loss L {\textstyle L} as L ( f ( x ^ ) , y ) = f y ( x ^ ) − max k ≠ y f k ( x ^ ) {\textstyle L(f({\hat {x}}),y)=f_{y}({\hat {x}})-\max _{k\neq y}f_{k}({\hat {x}})} and proposes the solution to finding adversarial example x ^ {\textstyle {\hat {x}}} as solving the below constrained optimization ... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > Black box attacks > Square Attack | 347 | 1,021 | null |
Section: Specific attack types > Adversarial examples > Black box attacks > HopSkipJump Attack. This black box attack was also proposed as a query efficient attack, but one that relies solely on access to any input's predicted output class. In other words, the HopSkipJump attack does not require the ability to calculat... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > Black box attacks > HopSkipJump Attack | 209 | 948 | null |
The attack objectives for both are as follows where x {\textstyle x} is the original image, x ′ {\textstyle x^{\prime }} is the adversarial image, d {\textstyle d} is a distance function between images, c ∗ {\textstyle c^{*}} is the target label, and C {\textstyle C} is the model's classification class label function: ... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > Black box attacks > HopSkipJump Attack | 350 | 934 | null |
(Targeted) {\displaystyle S(x^{\prime }):={\begin{cases}\max _{c\neq C(x)}{F(x^{\prime })_{c}}-F(x^{\prime })_{C(x)},&{\text{(Untargeted)}}\\F(x^{\prime })_{c^{*}}-\max _{c\neq c^{*}}{F(x^{\prime })_{c}},&{\text{(Targeted)}}\end{cases}}} This can be further simplified to better visualize the boundary between different ... | Wikipedia - Adversarial machine learning - Specific attack types > Adversarial examples > Black box attacks > HopSkipJump Attack | 347 | 743 | null |
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