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Outline of natural language processing : Information extraction (IE) β field concerned in general with the extraction of semantic information from text. This covers tasks such as named-entity recognition, coreference resolution, relationship extraction, etc. Ontology engineering β field that studies the methods and met... |
Outline of natural language processing : Natural-language processing contributes to, and makes use of (the theories, tools, and methodologies from), the following fields: Automated reasoning β area of computer science and mathematical logic dedicated to understanding various aspects of reasoning, and producing software... |
Outline of natural language processing : Anaphora β type of expression whose reference depends upon another referential element. E.g., in the sentence 'Sally preferred the company of herself', 'herself' is an anaphoric expression in that it is coreferential with 'Sally', the sentence's subject. Context-free language β ... |
Outline of natural language processing : History of natural-language processing History of machine translation History of automated essay scoring History of natural-language user interface History of natural-language understanding History of optical character recognition History of question answering History of speech ... |
Outline of natural language processing : Sukhotin's algorithm β statistical classification algorithm for classifying characters in a text as vowels or consonants. It was initially created by Boris V. Sukhotin. T9 (predictive text) β stands for "Text on 9 keys", is a USA-patented predictive text technology for mobile ph... |
Outline of natural language processing : Google Ngram Viewer β graphs n-gram usage from a corpus of more than 5.2 million books |
Outline of natural language processing : AFNLP (Asian Federation of Natural Language Processing Associations) β the organization for coordinating the natural-language processing related activities and events in the Asia-Pacific region. Australasian Language Technology Association β Association for Computational Linguis... |
Outline of natural language processing : Daniel Bobrow β Rollo Carpenter β creator of Jabberwacky and Cleverbot. Noam Chomsky β author of the seminal work Syntactic Structures, which revolutionized Linguistics with 'universal grammar', a rule based system of syntactic structures. Kenneth Colby β David Ferrucci β princi... |
Outline of natural language processing : Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3. McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, MA: A. K. Peters, Ltd., ISBN 978-1-56881-205-2, OCLC 52197627. Russell, Stuart J.; Norvig,... |
Predictive state representation : In computer science, a predictive state representation (PSR) is a way to model a state of controlled dynamical system from a history of actions taken and resulting observations. PSR captures the state of a system as a vector of predictions for future tests (experiments) that can be don... |
Predictive state representation : Consider a dynamic system based on a discrete set A of actions and a discrete set O of observations. A history h is a sequence a 1 o 1 β¦ a β o β o_\dots a_o_ where a 1 , β¦ , a β ,\dots ,a_ are the actions taken by the agent in that order and o 1 , β¦ , o β ,\dots ,o_ are the observat... |
Predictive state representation : Littman, Michael L.; Richard S. Sutton; Satinder Singh (2002). "Predictive Representations of State" (PDF). Advances in Neural Information Processing Systems 14 (NIPS). pp. 1555β1561. Singh, Satinder; Michael R. James; Matthew R. Rudary (2004). "Predictive State Representations: A New ... |
LRE Map : The LRE Map (Language Resources and Evaluation) is a freely accessible large database on resources dedicated to Natural language processing. The original feature of LRE Map is that the records are collected during the submission of different major Natural language processing conferences. The records are then ... |
LRE Map : Several institutions worldwide maintain catalogues of language resources (ELRA, LDC, NICT Universal Catalogue, ACL Data and Code Repository, OLAC, LT World, etc.) However, it has been estimated that only 10% of existing resources are known, either through distribution catalogues or via direct publicity by pro... |
LRE Map : The LRE Map originated under the name "LREC Map" during the preparation of LREC 2010 conference. More specifically, the idea was discussed within the FlaReNet project, and in collaboration with ELRA and the Institute of Computational Linguistics of CNR in Pisa, the Map was put in place at LREC 2010. The LREC ... |
LRE Map : The size of the database increases over time. The data collected amount to 4776 entries. Each resource is described according to the following attributes: Resource type, e.g. lexicon, annotation tool, tagger/parser. Resource production status, e.g. newly created finished, existing-updated. Resource availabili... |
LRE Map : The LRE map is a very important tool to chart the NLP field. Compared to other studied based on subjective scorings, the LRE map is made of real facts. The map has a great potential for many uses, in addition to being an information gathering tool: It is a great instrument for monitoring the evolution of the ... |
LRE Map : The data were then cleaned and sorted by Joseph Mariani (CNRS-LIMSI IMMI) and Gil Francopoulo (CNRS-LIMSI IMMI + Tagmatica) in order to compute the various matrices of the final FLaReNet reports. One of them, the matrix for written data at LREC 2010 is as follows: English is the most studied language. Secondl... |
LRE Map : The LRE Map has been extended to Language Resources and Evaluation Journal and other conferences. |
LRE Map : LREC Map research page |
Algorithmic probability : In algorithmic information theory, algorithmic probability, also known as Solomonoff probability, is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithm... |
Algorithmic probability : Algorithmic probability is the main ingredient of Solomonoff's theory of inductive inference, the theory of prediction based on observations; it was invented with the goal of using it for machine learning; given a sequence of symbols, which one will come next? Solomonoff's theory provides an a... |
Algorithmic probability : Solomonoff invented the concept of algorithmic probability with its associated invariance theorem around 1960, publishing a report on it: "A Preliminary Report on a General Theory of Inductive Inference." He clarified these ideas more fully in 1964 with "A Formal Theory of Inductive Inference,... |
Algorithmic probability : Sequential Decisions Based on Algorithmic Probability is a theoretical framework proposed by Marcus Hutter to unify algorithmic probability with decision theory. The framework provides a foundation for creating universally intelligent agents capable of optimal performance in any computable env... |
Algorithmic probability : Ray Solomonoff Andrey Kolmogorov Leonid Levin |
Algorithmic probability : Solomonoff's theory of inductive inference Algorithmic information theory Bayesian inference Inductive inference Inductive probability Kolmogorov complexity Universal Turing machine Information-based complexity |
Algorithmic probability : Li, M. and Vitanyi, P., An Introduction to Kolmogorov Complexity and Its Applications, 3rd Edition, Springer Science and Business Media, N.Y., 2008 Hutter, Marcus (2005). Universal artificial intelligence: sequential decisions based on algorithmic probability. Texts in theoretical computer sci... |
Algorithmic probability : Rathmanner, S and Hutter, M., "A Philosophical Treatise of Universal Induction" in Entropy 2011, 13, 1076-1136: A very clear philosophical and mathematical analysis of Solomonoff's Theory of Inductive Inference |
Algorithmic probability : Algorithmic Probability at Scholarpedia Solomonoff's publications |
SqueezeNet : SqueezeNet is a deep neural network for image classification released in 2016. SqueezeNet was developed by researchers at DeepScale, University of California, Berkeley, and Stanford University. In designing SqueezeNet, the authors' goal was to create a smaller neural network with fewer parameters while ach... |
SqueezeNet : SqueezeNet was originally released on February 22, 2016. This original version of SqueezeNet was implemented on top of the Caffe deep learning software framework. Shortly thereafter, the open-source research community ported SqueezeNet to a number of other deep learning frameworks. On February 26, 2016, Ed... |
SqueezeNet : Some of the members of the original SqueezeNet team have continued to develop resource-efficient deep neural networks for a variety of applications. A few of these works are noted in the following table. As with the original SqueezeNet model, the open-source research community has ported and adapted these ... |
SqueezeNet : Convolutional neural network MobileNet EfficientNet You Only Look Once Edge computing == References == |
MindsDB : MindsDB is an artificial intelligence company headquartered in California, an innovator bringing AI and Data together and is focused on enabling developers to build AI capabilities that can Reason, Plan and Orchestrate over enterprise data. |
MindsDB : MindsDB was founded in 2017 by Jorge Torres and Adam Carrigan. The idea was incubated in early 2018 at Skydeck from UC Berkeley during the first funded batch, this led to the MindsDB Open Source project which started in August 2018. On April 16, 2020, MindsDB raised $3 million. Among the investors were OpenOc... |
MindsDB : Minds - an intelligent turnkey private AI system able to understand complex queries, retrieve relevant data from enterprise systems, deliver insights and make actions. It goes beyond traditional models by providing transparency in its decision-making process, respecting data privacy allowing organizations to ... |
MindsDB : MindsDB has formed strategic partnerships with leading companies such as Snowflake, SingleStore, DataStax, and NVIDIA. As of September 2024, the platform supports over 200 integrations, including popular large language models (LLMs) like OpenAI, Anthropic, and Mistral, as well as data platforms such as MySQL,... |
MindsDB : Named to Forbes' AI 50 list in 2021 Recognized as a 2022 Gartner Cool Vendor in Data-Centric AI Ranked 10th fastest-growing open-source startup globally and 3rd in the US by ROSS Index in 2022 Recognized by Fast Company as one of the Most Innovative AI companies in 2024 Reached over 26k GitHub stars, 4.8k for... |
MindsDB : Gurevich, Natalia (2023-08-24). "AI companies flocking to Mission worry neighbors". San Francisco Examiner. Retrieved 2024-03-23. |
MindsDB : Official website mindsdb on GitHub |
Base rate : In probability and statistics, the base rate (also known as prior probabilities) is the class of probabilities unconditional on "featural evidence" (likelihoods). It is the proportion of individuals in a population who have a certain characteristic or trait. For example, if 1% of the population were medical... |
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 illustr... |
Base rate : Bayes' rule Prior probability Prevalence == References == |
Transfer learning : Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.... |
Transfer learning : In 1976, Bozinovski and Fulgosi published a paper addressing transfer learning in neural network training. The paper gives a mathematical and geometrical model of the topic. In 1981, a report considered the application of transfer learning to a dataset of images representing letters of computer term... |
Transfer learning : The definition of transfer learning is given in terms of domains and tasks. A domain D consists of: a feature space X and a marginal probability distribution P ( X ) , where X = β X ,...,x_\\in . Given a specific domain, D = =\,P(X)\ , a task consists of two components: a label space Y and an... |
Transfer learning : Algorithms are available for transfer learning in Markov logic networks and Bayesian networks. Transfer learning has been applied to cancer subtype discovery, building utilization, general game playing, text classification, digit recognition, medical imaging and spam filtering. In 2020, it was disco... |
Transfer learning : Crossover (genetic algorithm) Domain adaptation General game playing Multi-task learning Multitask optimization Transfer of learning in educational psychology Zero-shot learning Feature learning external validity |
Transfer learning : Thrun, Sebastian; Pratt, Lorien (6 December 2012). Learning to Learn. Springer Science & Business Media. ISBN 978-1-4615-5529-2. |
GPT-1 : Generative Pre-trained Transformer 1 (GPT-1) was the first of OpenAI's large language models following Google's invention of the transformer architecture in 2017. In June 2018, OpenAI released a paper entitled "Improving Language Understanding by Generative Pre-Training", in which they introduced that initial m... |
GPT-1 : BookCorpus was chosen as a training dataset partly because the long passages of continuous text helped the model learn to handle long-range information. It contained over 7,000 unpublished fiction books from various genres. The rest of the datasets available at the time, while being larger, lacked this long-ran... |
GPT-1 : The GPT-1 architecture was a twelve-layer decoder-only transformer, using twelve masked self-attention heads, with 64-dimensional states each (for a total of 768). Rather than simple stochastic gradient descent, the Adam optimization algorithm was used; the learning rate was increased linearly from zero over th... |
GPT-1 : GPT-1 achieved a 5.8% and 1.5% improvement over previous best results on natural language inference (also known as textual entailment) tasks, evaluating the ability to interpret pairs of sentences from various datasets and classify the relationship between them as "entailment", "contradiction" or "neutral". Exa... |
Quantum natural language processing : Quantum natural language processing (QNLP) is the application of quantum computing to natural language processing (NLP). It computes word embeddings as parameterised quantum circuits that can solve NLP tasks faster than any classical computer. It is inspired by categorical quantum ... |
Quantum natural language processing : The first quantum algorithm for natural language processing used the DisCoCat framework and Grover's algorithm to show a quadratic quantum speedup for a text classification task. It was later shown that quantum language processing is BQP-Complete, i.e. quantum language models are m... |
Quantum natural language processing : The algorithm of Zeng and Coecke was adapted to the constraints of NISQ computers and implemented on IBM quantum computers to solve binary classification tasks. Instead of loading classical word vectors onto a quantum memory, the word vectors are computed directly as the parameters... |
Quantum natural language processing : Categorical quantum mechanics Natural language processing Quantum machine learning Applied category theory String diagram |
Quantum natural language processing : DisCoPy, a Python toolkit for computing with string diagrams lambeq, a Python library for quantum natural language processing |
General regression neural network : Generalized regression neural network (GRNN) is a variation to radial basis neural networks. GRNN was suggested by D.F. Specht in 1991. GRNN can be used for regression, prediction, and classification. GRNN can also be a good solution for online dynamical systems. GRNN represents an i... |
General regression neural network : Y ( x ) = β k = 1 N y k K ( x , x k ) β k = 1 N K ( x , x k ) ^y_K(x,x_)^K(x,x_) where: Y ( x ) is the prediction value of input x y k is the activation weight for the pattern layer neuron at k K ( x , x k ) ) is the Radial basis function kernel (Gaussian kernel) as formulated be... |
General regression neural network : GRNN has been implemented in many computer languages including MATLAB, R- programming language, Python (programming language) and Node.js. Neural networks (specifically Multi-layer Perceptron) can delineate non-linear patterns in data by combining with generalized linear models by co... |
General regression neural network : Similar to RBFNN, GRNN has the following advantages: Single-pass learning so no backpropagation is required. High accuracy in the estimation since it uses Gaussian functions. It can handle noises in the inputs. It requires relatively few data to train. The main disadvantages of GRNN ... |
Quantification (machine learning) : In machine learning and data mining, quantification (variously called learning to quantify, or supervised prevalence estimation, or class prior estimation) is the task of using supervised learning in order to train models (quantifiers) that estimate the relative frequencies (also kno... |
Quantification (machine learning) : The main variants of quantification, according to the characteristics of the set of classes used, are: Binary quantification, corresponding to the case in which there are only n = 2 classes and each data item belongs to exactly one of them; Single-label multiclass quantification, co... |
Quantification (machine learning) : Several evaluation measures can be used for evaluating the error of a quantification method. Since quantification consists of generating a predicted probability distribution that estimates a true probability distribution, these evaluation measures are ones that compare two probabilit... |
Quantification (machine learning) : Quantification is of special interest in fields such as the social sciences, epidemiology, market research, and ecological modelling, since these fields are inherently concerned with aggregate data. However, quantification is also useful as a building block for solving other downstre... |
Quantification (machine learning) : LQ 2021: the 1st International Workshop on Learning to Quantify LQ 2022: the 2nd International Workshop on Learning to Quantify LQ 2023: the 3rd International Workshop on Learning to Quantify LQ 2024: the 4th International Workshop on Learning to Quantify LeQua 2022: the 1st Data Cha... |
Lexalytics : Lexalytics, Inc. provides sentiment and intent analysis to an array of companies using SaaS and cloud based technology. Salience 6, the engine behind Lexalytics, was built as an on-premises, multi-lingual text analysis engine. It is leased to other companies who use it to power filtering and reputation man... |
Lexalytics : Lexalytics spun into existence in January 2003 out of a content management startup called Lightspeed. Lightspeed consolidated on America's West Coast. Jeff Catlin, a Lightspeed General Manager, and Mike Marshall, a Lighstpeed Principal Engineer, convinced investors to give them the East Coast company so as... |
Lexalytics : Official website |
LipNet : LipNet is a deep neural network for audio-visual speech recognition (ASVR). It was created by University of Oxford researchers Yannis Assael, Brendan Shillingford, Shimon Whiteson, and Nando de Freitas. The technique, outlined in a paper in November 2016, is able to decode text from the movement of a speaker's... |
Bayesian regret : In stochastic game theory, Bayesian regret is the expected difference ("regret") between the utility of a Bayesian strategy and that of the optimal strategy (the one with the highest expected payoff). The term Bayesian refers to Thomas Bayes (1702β1761), who proved a special case of what is now called... |
Bayesian regret : This term has been used to compare a random buy-and-hold strategy to professional traders' records. This same concept has received numerous different names, as the New York Times notes: "In 1957, for example, a statistician named James Hanna called his theorem Bayesian Regret. He had been preceded by ... |
Version space learning : Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis space is a disjunction H 1 β¨ H 2 β¨ . . . β¨ H n \lor H_\... |
Version space learning : In settings where there is a generality-ordering on hypotheses, it is possible to represent the version space by two sets of hypotheses: (1) the most specific consistent hypotheses, and (2) the most general consistent hypotheses, where "consistent" indicates agreement with observed data. The mo... |
Version space learning : The notion of version spaces was introduced by Mitchell in the early 1980s as a framework for understanding the basic problem of supervised learning within the context of solution search. Although the basic "candidate elimination" search method that accompanies the version space framework is no... |
Version space learning : Formal concept analysis Inductive logic programming Rough set. [The rough set framework focuses on the case where ambiguity is introduced by an impoverished feature set. That is, the target concept cannot be decisively described because the available feature set fails to disambiguate objects be... |
Version space learning : Hong, Tzung-Pai; Shian-Shyong Tsang (1997). "A generalized version space learning algorithm for noisy and uncertain data". IEEE Transactions on Knowledge and Data Engineering. 9 (2): 336β340. doi:10.1109/69.591457. S2CID 29926783. Mitchell, Tom M. (1997). Machine Learning. Boston: McGraw-Hill. ... |
Knowledge graph embedding : In representation learning, knowledge graph embedding (KGE), also called knowledge representation learning (KRL), or multi-relation learning, is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic ... |
Knowledge graph embedding : A knowledge graph G = =\ is a collection of entities E , relations R , and facts F . A fact is a triple ( h , r , t ) β F that denotes a link r β R between the head h β E and the tail t β E of the triple. Another notation that is often used in the literature to represent a triple (or... |
Knowledge graph embedding : All algorithms for creating a knowledge graph embedding follow the same approach. First, the embedding vectors are initialized to random values. Then, they are iteratively optimized using a training set of triples. In each iteration, a batch of size b triples is sampled from the training se... |
Knowledge graph embedding : These indexes are often used to measure the embedding quality of a model. The simplicity of the indexes makes them very suitable for evaluating the performance of an embedding algorithm even on a large scale. Given Q as the set of all ranked predictions of a model, it is possible to define ... |
Knowledge graph embedding : Given a collection of triples (or facts) F = =\ , the knowledge graph embedding model produces, for each entity and relation present in the knowledge graph a continuous vector representation. ( h , r , t ) is the corresponding embedding of a triple with h , t β I R d ^ and r β I R k ^ , wh... |
Knowledge graph embedding : The machine learning task for knowledge graph embedding that is more often used to evaluate the embedding accuracy of the models is the link prediction. Rossi et al. produced an extensive benchmark of the models, but also other surveys produces similar results. The benchmark involves five da... |
Knowledge graph embedding : KGE on GitHub MEI-KGE on GitHub Pykg2vec on GitHub DGL-KE on GitHub PyKEEN on GitHub TorchKGE on GitHub AmpliGraph on GitHub OpenKE on GitHub scikit-kge on GitHub Fast-TransX on GitHub MEIM-KGE on GitHub DICEE on GitHub |
Knowledge graph embedding : Knowledge graph Embedding Machine learning Knowledge base Knowledge extraction Statistical relational learning Representation learning Graph embedding |
Knowledge graph embedding : Open Graph Benchmark - Stanford WordNet - Princeton |
Neural modeling fields : Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS).... |
Neural modeling fields : In the general case, NMF system consists of multiple processing levels. At each level, output signals are the concepts recognized in (or formed from) input, bottom-up signals. Input signals are associated with (or recognized, or grouped into) concepts according to the models and at this level. ... |
Neural modeling fields : The learning process consists of estimating model parameters S and associating signals with concepts by maximizing the similarity L. Note that all possible combinations of signals and models are accounted for in expression (2) for L. This can be seen by expanding a sum and multiplying all the t... |
Neural modeling fields : Finding patterns below noise can be an exceedingly complex problem. If an exact pattern shape is not known and depends on unknown parameters, these parameters should be found by fitting the pattern model to the data. However, when the locations and orientations of patterns are not known, it is ... |
Neural modeling fields : Above, a single processing level in a hierarchical NMF system was described. At each level of hierarchy there are input signals from lower levels, models, similarity measures (L), emotions, which are defined as changes in similarity, and actions; actions include adaptation, behavior satisfying ... |
EM algorithm and GMM model : In statistics, EM (expectation maximization) algorithm handles latent variables, while GMM is the Gaussian mixture model. |
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 lowe... |
EM algorithm and GMM model : The EM algorithm consists of two steps: the E-step and the M-step. Firstly, the model parameters and the z ( i ) can be randomly initialized. In the E-step, the algorithm tries to guess the value of z ( i ) based on the parameters, while in the M-step, the algorithm updates the value of t... |
Journal of Machine Learning Research : The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning. It was established in 2000 and the first editor-in-chief was Leslie Kaelbling. The current editors-in-chief are Francis Bach (Inria) and David Blei (Columbia Unive... |
Journal of Machine Learning Research : The journal was established as an open-access alternative to the journal Machine Learning. In 2001, forty editorial board members of Machine Learning resigned, saying that in the era of the Internet, it was detrimental for researchers to continue publishing their papers in expensi... |
Journal of Machine Learning Research : "Top journals in computer science". Times Higher Education. 14 May 2009. Retrieved 22 August 2009. |
Multilingual notation : A multilingual notation is a representation in a lexical resource that allows translation between two or more words. |
Multilingual notation : For instance, within LMF, a multilingual notation could be as presented in the following diagram, for English / French translation. In this diagram, two intermediate SenseAxis instances are used in order to represent a near match between fleuve in French and river in English. The SenseAxis insta... |
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