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Computer science | The earliest foundations of what would become computer science predate the invention of the modern digital computer. Machines for calculating fixed numerical tasks such as the abacus have existed since antiquity, aiding in computations such as multiplication and division. Algorithms for performing computations have exi... | Computer_science |
Glossary of computer science | abstract data type (ADT) A mathematical model for data types in which a data type is defined by its behavior (semantics) from the point of view of a user of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations. This contrasts with data struct... | Computer_science |
Agnostic (data) | Many devices or programs need data to be presented in a specific format to process the data. For example, Apple Inc devices generally require applications to be downloaded from their App Store. This is a non data-agnostic method, as it uses a specified file type, downloaded from a specific location, and does not functi... | Computer_science |
Catalytic computing | In 2020 J. Cook and Mertz used catalytic computing to prove to attack the tree evaluation problem (TreeEval) a type of pebble game introduced by Cook, McKenzie, Wehr, Braverman and Santhanam as an example where any algorithm for solving the problem would require too much memory to belong in the L complexity class, prov... | Computer_science |
Computational gastronomy | The field of computational gastronomy aims to enhance understanding and innovation in culinary science through computational tools. By analyzing the relationships between food components, health, and flavor, researchers seek to create innovative culinary experiences and improve food preparation techniques. Despite its ... | Computer_science |
Computer science in sport | Going back in history, computers in sports were used for the first time in the 1960s, when the main purpose was to accumulate sports information. Databases were created and expanded in order to launch documentation and dissemination of publications like articles or books that contain any kind of knowledge related to sp... | Computer_science |
Filter and refine | FRP follows a two-step processing strategy: Filter: an efficient filter function f f i l t e r {\displaystyle f_{filter}} is applied to each object x {\displaystyle x} in the dataset D {\displaystyle {\mathcal {D}}} . The filtered subset D ′ {\displaystyle {\mathcal {D}}'} is defined as D ′ = { x | f f i l t e r ( x ) ... | Computer_science |
Outline of computer science | History of computer science List of pioneers in computer science History of Artificial Intelligence History of Operating Systems Computer Scientist Programmer (Software developer) Teacher/Professor Software engineer Software architect Software tester Hardware engineer Data analyst Interaction designer Network administr... | Computer_science |
Prefetching | Prefetching works by predicting which memory addresses or resources will be accessing and load them into faster access storage, like caches. Prefetching may be used: Hardware-level, such as CPU memory controllers Software-level, strategies in compilers, operating systems, logic in web browsers or file systems Processor... | Computer_science |
Technology transfer in computer science | Notable examples of technology transfer in computer science include: == References == | Computer_science |
Transition (computer science) | The study of new and fundamental design methods, models and techniques that enable automated, coordinated and cross-layer transitions between functionally similar mechanisms within a communication system is the main goal of a collaborative research center funded by the German research foundation (DFG). The DFG collabor... | Computer_science |
Sherifah Tumusiime | Tumusiime attended Mount Saint Mary's College Namagunga , then did a Bachelor of Computer Science at Makerere University from 2008–2011 and Business and Entrepreneurship at Clark Atlanta University in 2015. Tumusiime is the Founder, CEO of Zimba Zimba Group Ltd from December 2014 to date, which currently collaborates w... | Computer_science |
Machine learning | 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 Arthur Samuel inven... | Machine learning |
Outline of machine learning | An academic discipline A branch of science An applied science A subfield of computer science A branch of artificial intelligence A subfield of soft computing Application of statistics Applications of machine learning Bioinformatics Biomedical informatics Computer vision Customer relationship management Data mining Eart... | Machine learning |
80 Million Tiny Images | 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 search engines: Altavi... | Machine learning |
A Logical Calculus of the Ideas Immanent in Nervous Activity | 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 {\displaystyle i} at time t {\d... | Machine learning |
Accelerated Linear Algebra | x86-64 ARM64 NVIDIA GPU AMD GPU Intel GPU Apple GPU Google TPU AWS Trainium, Inferentia Cerebras Graphcore IPU | Machine learning |
Action model learning | 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 observed in time step t {\displ... | Machine learning |
Active learning (machine learning) | 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 up into three subsets... | Machine learning |
Adversarial machine learning | 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 noted that linear... | Machine learning |
AI/ML Development Platform | AI/ML development platforms serve as comprehensive environments for building AI systems, ranging from simple predictive models to complex large language models (LLMs). They abstract technical complexities (e.g., distributed computing, hyperparameter tuning) while offering modular components for customization. Key users... | Machine learning |
AIOps | AIOPs was first defined by Gartner in 2016, combining "artificial intelligence" and "IT operations" to describe the application of AI and machine learning to enhance IT operations. This concept was introduced to address the increasing complexity and data volume in IT environments, aiming to automate processes such as e... | Machine learning |
AIXI | According to Hutter, the word "AIXI" can have several interpretations. AIXI can stand for AI based on Solomonoff's distribution, denoted by ξ {\displaystyle \xi } (which is the Greek letter xi), or e.g. it can stand for AI "crossed" (X) with induction (I). There are other interpretations. AIXI is a reinforcement learni... | Machine learning |
Algorithm selection | Given a portfolio P {\displaystyle {\mathcal {P}}} of algorithms A ∈ P {\displaystyle {\mathcal {A}}\in {\mathcal {P}}} , a set of instances i ∈ I {\displaystyle i\in {\mathcal {I}}} and a cost metric m : P × I → R {\displaystyle m:{\mathcal {P}}\times {\mathcal {I}}\to \mathbb {R} } , the algorithm selection problem c... | Machine learning |
Algorithmic bias | Algorithms are difficult to define, but may be generally understood as lists of instructions that determine how programs read, collect, process, and analyze data to generate output.: 13 For a rigorous technical introduction, see Algorithms. Advances in computer hardware have led to an increased ability to process, stor... | Machine learning |
Algorithmic inference | Concerning the identification of the parameters of a distribution law, the mature reader may recall lengthy disputes in the mid 20th century about the interpretation of their variability in terms of fiducial distribution (Fisher 1956), structural probabilities (Fraser 1966), priors/posteriors (Ramsey 1925), and so on. ... | Machine learning |
Algorithmic party platforms in the United States | The integration of artificial intelligence (AI) into political campaigns has introduced a significant shift in how party platforms are shaped and communicated. Traditionally, platforms were drafted months before elections and remained static throughout the campaign. However, algorithmic platforms now rely on continuous... | Machine learning |
Anomaly detection | Many attempts have been made in the statistical and computer science communities to define an anomaly. The most prevalent ones include the following, and can be categorised into three groups: those that are ambiguous, those that are specific to a method with pre-defined thresholds usually chosen empirically, and those ... | Machine learning |
Aporia (company) | Aporia was founded in 2019 by Liran Hason and Alon Gubkin. In April 2021, the company raised a $5 million seed round for its monitoring platform for ML models. In February 2022, the company closed a Series A round of $25 million for its ML observability platform. Aporia was named by Forbes as the Next Billion-Dollar Co... | Machine learning |
Apprenticeship learning | Mapping methods try to mimic the expert by forming a direct mapping either from states to actions, or from states to reward values. For example, in 2002 researchers used such an approach to teach an AIBO robot basic soccer skills. The system learns rules to associate preconditions and postconditions with each action. I... | Machine learning |
Artificial intelligence in hiring | Artificial intelligence has fascinated researchers since the term was coined in the mid-1950s. Researchers have identified four main forms of intelligence that AI would need to possess to truly replace humans in the workplace: mechanical, analytical, intuitive, and empathetic. Automation follows a predictable progressi... | Machine learning |
Attention (machine learning) | Academic reviews of the history of the attention mechanism are provided in Niu et al. and Soydaner. The modern era of machine attention was revitalized by grafting an attention mechanism (Fig 1. orange) to an Encoder-Decoder. Figure 2 shows the internal step-by-step operation of the attention block (A) in Fig 1. This a... | Machine learning |
Audio inpainting | Consider a digital audio signal x {\displaystyle \mathbf {x} } . A corrupted version of x {\displaystyle \mathbf {x} } , which is the audio signal presenting missing gaps to be reconstructed, can be defined as x ~ = m ∘ x {\displaystyle \mathbf {\tilde {x}} =\mathbf {m} \circ \mathbf {x} } , where m {\displaystyle \mat... | Machine learning |
Automated decision-making | There are different definitions of ADM based on the level of automation involved. Some definitions suggests ADM involves decisions made through purely technological means without human input, such as the EU's General Data Protection Regulation (Article 22). However, ADM technologies and applications can take many forms... | Machine learning |
Automated machine learning | In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to. To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, fea... | Machine learning |
Automation in construction | Kratos Defense & Security Solutions fielded the world’s first Autonomous Truck-Mounted Attenuator (ATMA) in 2017, in conjunction with Royal Truck & Equipment. Equipment control and management: Automation can be used to control and monitor construction equipment, such as cranes, excavators, and bulldozers. Material hand... | Machine learning |
Bag-of-words model | The following models a text document using bag-of-words. Here are two simple text documents: Based on these two text documents, a list is constructed as follows for each document: Representing each bag-of-words as a JSON object, and attributing to the respective JavaScript variable: Each key is the word, and each value... | Machine learning |
Ball tree | A ball tree is a binary tree in which every node defines a D-dimensional ball containing a subset of the points to be searched. Each internal node of the tree partitions the data points into two disjoint sets which are associated with different balls. While the balls themselves may intersect, each point is assigned to ... | Machine learning |
Base rate | Many psychological studies have examined a phenomenon called base-rate neglect or base rate fallacy, in which category base rates are not integrated with presented evidence in a normative manner, although not all evidence is consistent regarding how common this fallacy is. Mathematician Keith Devlin illustrates the ris... | Machine learning |
Bayesian interpretation of kernel regularization | The classical supervised learning problem requires estimating the output for some new input point x ′ {\displaystyle \mathbf {x} '} by learning a scalar-valued estimator f ^ ( x ′ ) {\displaystyle {\hat {f}}(\mathbf {x} ')} on the basis of a training set S {\displaystyle S} consisting of n {\displaystyle n} input-outpu... | Machine learning |
Bayesian optimization | The term is generally attributed to Jonas Mockus and is coined in his work from a series of publications on global optimization in the 1970s and 1980s. Bayesian optimization is used on problems of the form max x ∈ X f ( x ) {\textstyle \max _{x\in X}f(x)} , with X {\textstyle X} being the set of all possible parameters... | Machine learning |
Bayesian regret | The term Bayesian refers to Thomas Bayes (1702–1761), who proved a special case of what is now called Bayes' theorem, who provided the first mathematical treatment of a non-trivial problem of statistical data analysis using what is now known as Bayesian inference. This term has been used to compare a random buy-and-hol... | Machine learning |
Bayesian structural time series | The model consists of three main components: Kalman filter. The technique for time series decomposition. In this step, a researcher can add different state variables: trend, seasonality, regression, and others. Spike-and-slab method. In this step, the most important regression predictors are selected. Bayesian model av... | Machine learning |
Bias–variance tradeoff | The bias–variance tradeoff is a central problem in supervised learning. Ideally, one wants to choose a model that both accurately captures the regularities in its training data, but also generalizes well to unseen data. Unfortunately, it is typically impossible to do both simultaneously. High-variance learning methods ... | Machine learning |
Binary classification | Given a classification of a specific data set, there are four basic combinations of actual data category and assigned category: true positives TP (correct positive assignments), true negatives TN (correct negative assignments), false positives FP (incorrect positive assignments), and false negatives FN (incorrect negat... | Machine learning |
Bioserenity | BioSerenity was founded in 2014, by Pierre-Yves Frouin. The company was initially hosted at the ICM Institute (Institute du Cerveau et de la Moëlle épinière), in Paris, France. Fund Raising June 8, 2015 : The company raises a $4 million seed round with Kurma Partners and IdInvest Partners September 20, 2017 : The compa... | Machine learning |
Bradley–Terry model | The model is named after Ralph A. Bradley and Milton E. Terry, who presented it in 1952, although it had already been studied by Ernst Zermelo in the 1920s. Applications of the model include the ranking of competitors in sports, chess, and other competitions, the ranking of products in paired comparison surveys of cons... | Machine learning |
Category utility | The probability-theoretic definition of category utility given in Fisher (1987) and Witten & Frank (2005) is as follows: C U ( C , F ) = 1 p ∑ c j ∈ C p ( c j ) [ ∑ f i ∈ F ∑ k = 1 m p ( f i k | c j ) 2 − ∑ f i ∈ F ∑ k = 1 m p ( f i k ) 2 ] {\displaystyle CU(C,F)={\tfrac {1}{p}}\sum _{c_{j}\in C}p(c_{j})\left[\sum _{f_... | Machine learning |
CIML community portal | The CIML community portal was created to facilitate an online virtual scientific community wherein anyone interested in CIML can share research, obtain resources, or simply learn more. The effort is currently led by Jacek Zurada (principal investigator), with Rammohan Ragade and Janusz Wojtusiak, aided by a team of 25 ... | Machine learning |
Claude (language model) | Claude models are generative pre-trained transformers. They have been pre-trained to predict the next word in large amounts of text. Then, they have been fine-tuned, notably using constitutional AI and reinforcement learning from human feedback (RLHF). Claude is named after Claude Shannon, a pioneer in AI research. In ... | Machine learning |
Cognitive robotics | While traditional cognitive modeling approaches have assumed symbolic coding schemes as a means for depicting the world, translating the world into these kinds of symbolic representations has proven to be problematic if not untenable. Perception and action and the notion of symbolic representation are therefore core is... | Machine learning |
Concept drift | In machine learning and predictive analytics this drift phenomenon is called concept drift. In machine learning, a common element of a data model are the statistical properties, such as probability distribution of the actual data. If they deviate from the statistical properties of the training data set, then the learne... | Machine learning |
Conditional random field | CRFs are a type of discriminative undirected probabilistic graphical model. Lafferty, McCallum and Pereira define a CRF on observations X {\displaystyle {\boldsymbol {X}}} and random variables Y {\displaystyle {\boldsymbol {Y}}} as follows: Let G = ( V , E ) {\displaystyle G=(V,E)} be a graph such that Y = ( Y v ) v ∈ ... | Machine learning |
Confusion matrix | Given a sample of 12 individuals, 8 that have been diagnosed with cancer and 4 that are cancer-free, where individuals with cancer belong to class 1 (positive) and non-cancer individuals belong to class 0 (negative), we can display that data as follows: Assume that we have a classifier that distinguishes between indivi... | Machine learning |
Contrastive Language-Image Pre-training | The CLIP method trains a pair of models contrastively. One model takes in a piece of text as input and outputs a single vector representing its semantic content. The other model takes in an image and similarly outputs a single vector representing its visual content. The models are trained so that the vectors correspond... | Machine learning |
Cost-sensitive machine learning | Cost-sensitive machine learning optimizes models based on the specific consequences of misclassifications, making it a valuable tool in various applications. It is especially useful in problems with a high imbalance in class distribution and a high imbalance in associated costs Cost-sensitive machine learning introduce... | Machine learning |
Coupled pattern learner | Semi-supervised learning approaches using a small number of labeled examples with many unlabeled examples are usually unreliable as they produce an internally consistent, but incorrect set of extractions. CPL solves this problem by simultaneously learning classifiers for many different categories and relations in the p... | Machine learning |
Cross-entropy method | Consider the general problem of estimating the quantity ℓ = E u [ H ( X ) ] = ∫ H ( x ) f ( x ; u ) d x {\displaystyle \ell =\mathbb {E} _{\mathbf {u} }[H(\mathbf {X} )]=\int H(\mathbf {x} )\,f(\mathbf {x} ;\mathbf {u} )\,{\textrm {d}}\mathbf {x} } , where H {\displaystyle H} is some performance function and f ( x ; u ... | Machine learning |
Cross-validation (statistics) | Assume a model with one or more unknown parameters, and a data set to which the model can be fit (the training data set). The fitting process optimizes the model parameters to make the model fit the training data as well as possible. If an independent sample of validation data is taken from the same population as the t... | Machine learning |
Data augmentation | Synthetic Minority Over-sampling Technique (SMOTE) is a method used to address imbalanced datasets in machine learning. In such datasets, the number of samples in different classes varies significantly, leading to biased model performance. For example, in a medical diagnosis dataset with 90 samples representing healthy... | Machine learning |
Data exploration | This area of data exploration has become an area of interest in the field of machine learning. This is a relatively new field and is still evolving. As its most basic level, a machine-learning algorithm can be fed a data set and can be used to identify whether a hypothesis is true based on the dataset. Common machine l... | Machine learning |
Astroinformatics | Astroinformatics is primarily focused on developing the tools, methods, and applications of computational science, data science, machine learning, and statistics for research and education in data-oriented astronomy. Early efforts in this direction included data discovery, metadata standards development, data modeling,... | Machine learning |
Data-driven model | These models have evolved from earlier statistical models, which were based on certain assumptions about probability distributions that often proved to be overly restrictive. The emergence of data-driven models in the 1950s and 1960s coincided with the development of digital computers, advancements in artificial intell... | Machine learning |
Decision list | A decision list (DL) of length r is of the form: if f1 then output b1 else if f2 then output b2 ... else if fr then output br where fi is the ith formula and bi is the ith boolean for i ∈ { 1... r } {\displaystyle i\in \{1...r\}} . The last if-then-else is the default case, which means formula fr is always equal to tru... | Machine learning |
Decision tree pruning | Pruning processes can be divided into two types (pre- and post-pruning). Pre-pruning procedures prevent a complete induction of the training set by replacing a stop () criterion in the induction algorithm (e.g. max. Tree depth or information gain (Attr)> minGain). Pre-pruning methods are considered to be more efficient... | Machine learning |
Deep Tomographic Reconstruction | Traditional tomographic reconstruction relies on analytic methods such as filtered back-projection, or iterative methods which incrementally compute inverse transformations from measurement data (e.g., Radon or Fourier transform data). However, these approaches are not sufficient for certain imaging techniques such as ... | Machine learning |
Developmental robotics | Can a robot learn like a child? Can it learn a variety of new skills and new knowledge unspecified at design time and in a partially unknown and changing environment? How can it discover its body and its relationships with the physical and social environment? How can its cognitive capacities continuously develop withou... | Machine learning |
Discovery system (artificial intelligence) | Autoclass was a Bayesian Classification System written in 1986 Automated Mathematician was one of the earliest successful discovery systems. It was written in 1977 and worked by generating a modifying small Lisp programs Eurisko was a Sequel to Automated Mathematician written in 1984 Dalton is a still maintained progra... | Machine learning |
Document classification | Content-based classification is classification in which the weight given to particular subjects in a document determines the class to which the document is assigned. It is, for example, a common rule for classification in libraries, that at least 20% of the content of a book should be about the class to which the book ... | Machine learning |
Domain adaptation | Domain adaptation setups are classified in two different ways; according to the distribution shift between the domains, and according to the available data from the target domain. Let X {\displaystyle X} be the input space (or description space) and let Y {\displaystyle Y} be the output space (or label space). The obje... | Machine learning |
Double descent | Early observations of what would later be called double descent in specific models date back to 1989. The term "double descent" was coined by Belkin et. al. in 2019, when the phenomenon gained popularity as a broader concept exhibited by many models. The latter development was prompted by a perceived contradiction betw... | Machine learning |
EfficientNet | EfficientNet introduces compound scaling, which, instead of scaling one dimension of the network at a time, such as depth (number of layers), width (number of channels), or resolution (input image size), uses a compound coefficient ϕ {\displaystyle \phi } to scale all three dimensions simultaneously. Specifically, give... | Machine learning |
ELMo | ELMo is a multilayered bidirectional LSTM on top of a token embedding layer. The output of all LSTMs concatenated together consists of the token embedding. The input text sequence is first mapped by an embedding layer into a sequence of vectors. Then two parts are run in parallel over it. The forward part is a 2-layere... | Machine learning |
EM algorithm and GMM model | In the picture below, are shown the red blood cell hemoglobin concentration and the red blood cell volume data of two groups of people, the Anemia group and the Control Group (i.e. the group of people without Anemia). As expected, people with Anemia have lower red blood cell volume and lower red blood cell hemoglobin c... | Machine learning |
Empirical dynamic modeling | Mathematical models have tremendous power to describe observations of real-world systems. They are routinely used to test hypothesis, explain mechanisms and predict future outcomes. However, real-world systems are often nonlinear and multidimensional, in some instances rendering explicit equation-based modeling problem... | Machine learning |
Empirical risk minimization | The following situation is a general setting of many supervised learning problems. There are two spaces of objects X {\displaystyle X} and Y {\displaystyle Y} and we would like to learn a function h : X → Y {\displaystyle \ h:X\to Y} (often called hypothesis) which outputs an object y ∈ Y {\displaystyle y\in Y} , given... | Machine learning |
Energy-based model | For a given input x {\displaystyle x} , the model describes an energy E θ ( x ) {\displaystyle E_{\theta }(x)} such that the Boltzmann distribution P θ ( x ) = exp ( − β E θ ( x ) ) / Z ( θ ) {\displaystyle P_{\theta }(x)=\exp(-\beta E_{\theta }(x))/Z(\theta )} is a probability (density), and typically β = 1 {\displa... | Machine learning |
Evaluation of binary classifiers | Given a data set, a classification (the output of a classifier on that set) gives two numbers: the number of positives and the number of negatives, which add up to the total size of the set. To evaluate a classifier, one compares its output to another reference classification – ideally a perfect classification, but in ... | Machine learning |
Evolvability (computer science) | Let F n {\displaystyle F_{n}\,} and R n {\displaystyle R_{n}\,} be collections of functions on n {\displaystyle n\,} variables. Given an ideal function f ∈ F n {\displaystyle f\in F_{n}} , the goal is to find by local search a representation r ∈ R n {\displaystyle r\in R_{n}} that closely approximates f {\displaystyle ... | Machine learning |
Expectation propagation | Expectation propagation via moment matching plays a vital role in approximation for indicator functions that appear when deriving the message passing equations for TrueSkill. | Machine learning |
Explanation-based learning | An example of EBL using a perfect domain theory is a program that learns to play chess through example. A specific chess position that contains an important feature such as "Forced loss of black queen in two moves" includes many irrelevant features, such as the specific scattering of pawns on the board. EBL can take a ... | Machine learning |
Exploration–exploitation dilemma | In the context of machine learning, the exploration–exploitation tradeoff is fundamental in reinforcement learning (RL), a type of machine learning that involves training agents to make decisions based on feedback from the environment. Crucially, this feedback may be incomplete or delayed. The agent must decide whether... | Machine learning |
Fairness (machine learning) | Discussion about fairness in machine learning is a relatively recent topic. Since 2016 there has been a sharp increase in research into the topic. This increase could be partly attributed to an influential report by ProPublica that claimed that the COMPAS software, widely used in US courts to predict recidivism, was ra... | Machine learning |
Feature (machine learning) | In feature engineering, two types of features are commonly used: numerical and categorical. Numerical features are continuous values that can be measured on a scale. Examples of numerical features include age, height, weight, and income. Numerical features can be used in machine learning algorithms directly. Categorica... | Machine learning |
Feature engineering | One of the applications of feature engineering has been clustering of feature-objects or sample-objects in a dataset. Especially, feature engineering based on matrix decomposition has been extensively used for data clustering under non-negativity constraints on the feature coefficients. These include Non-Negative Matri... | Machine learning |
Feature hashing | Ganchev and Dredze showed that in text classification applications with random hash functions and several tens of thousands of columns in the output vectors, feature hashing need not have an adverse effect on classification performance, even without the signed hash function. Weinberger et al. (2009) applied their versi... | Machine learning |
Feature learning | Supervised feature learning is learning features from labeled data. The data label allows the system to compute an error term, the degree to which the system fails to produce the label, which can then be used as feedback to correct the learning process (reduce/minimize the error). Approaches include: Unsupervised featu... | Machine learning |
Feature scaling | Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization. For example, many classifiers calculate the distance between two points by the Euclidean distance. If one of the features has a broad range of values, the distance ... | Machine learning |
Feature store | Feature stores can be built in-house by engineering teams or obtained from companies offering Feature Store solutions as Platform-as-a-Service (PaaS). These solutions can be cloud-based (online) or offered as on-premises (offline) deployments. The first feature stores, Michelangelo Palette by Uber and Zipline by Airbnb... | Machine learning |
Federated learning | Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets contained in local nodes without explicitly exchanging data samples. The general principle consists in training local models on local data samples and exchanging parameters (e.g. the weights a... | Machine learning |
Fine-tuning (deep learning) | Fine-tuning can degrade a model's robustness to distribution shifts. One mitigation is to linearly interpolate a fine-tuned model's weights with the weights of the original model, which can greatly increase out-of-distribution performance while largely retaining the in-distribution performance of the fine-tuned model. ... | Machine learning |
Flow-based generative model | Let z 0 {\displaystyle z_{0}} be a (possibly multivariate) random variable with distribution p 0 ( z 0 ) {\displaystyle p_{0}(z_{0})} . For i = 1 , . . . , K {\displaystyle i=1,...,K} , let z i = f i ( z i − 1 ) {\displaystyle z_{i}=f_{i}(z_{i-1})} be a sequence of random variables transformed from z 0 {\displaystyle z... | Machine learning |
Force control | Controlling the contact force between a manipulator and its environment is an increasingly important task in the environment of mechanical manufacturing, as well as industrial and service robot. One motivation for the use of force control is safety for man and machine. For various reasons, movements of the robot or mac... | Machine learning |
Formal concept analysis | The original motivation of formal concept analysis was the search for real-world meaning of mathematical order theory. One such possibility of very general nature is that data tables can be transformed into algebraic structures called complete lattices, and that these can be utilized for data visualization and interpre... | Machine learning |
Generative artificial intelligence | A generative AI system is constructed by applying unsupervised machine learning (invoking for instance neural network architectures such as generative adversarial networks (GANs), variation autoencoders (VAEs), transformers, or self-supervised machine learning trained on a dataset. The capabilities of a generative AI s... | Machine learning |
Generative model | An alternative division defines these symmetrically as: a generative model is a model of the conditional probability of the observable X, given a target y, symbolically, P ( X ∣ Y = y ) {\displaystyle P(X\mid Y=y)} a discriminative model is a model of the conditional probability of the target Y, given an observation x,... | Machine learning |
Geometric feature learning | Geometric feature learning methods extract distinctive geometric features from images. Geometric features are features of objects constructed by a set of geometric elements like points, lines, curves or surfaces. These features can be corner features, edge features, Blobs, Ridges, salient points image texture and so on... | Machine learning |
Glossary of artificial intelligence | A* search Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively base... | Machine learning |
Granular computing | As mentioned above, granular computing is not an algorithm or process; there is no particular method that is called "granular computing". It is rather an approach to looking at data that recognizes how different and interesting regularities in the data can appear at different levels of granularity, much as different fe... | Machine learning |
Grokking (machine learning) | Grokking was introduced in January 2022 by OpenAI researchers investigating how neural network perform calculations. It is derived from the word grok coined by Robert Heinlein in his novel Stranger in a Strange Land. Grokking can be understood as a phase transition during the training process. While grokking has been t... | Machine learning |
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