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LanguageWare : Branimir K. Boguraev Annotation-Based Finite State Processing in a Large-Scale NLP Architecture, IBM Research Report, 2004 Alexander Troussov, Mikhail Sogrin, "IBM LanguageWare Ontological Network Miner" Sheila Kinsella, Andreas Harth, Alexander Troussov, Mikhail Sogrin, John Judge, Conor Hayes, John G. ... |
Neural Networks (journal) : Neural Networks is a monthly peer-reviewed scientific journal and an official journal of the International Neural Network Society, European Neural Network Society, and Japanese Neural Network Society. |
Neural Networks (journal) : The journal was established in 1988 and is published by Elsevier. It covers all aspects of research on artificial neural networks. The founding editor-in-chief was Stephen Grossberg (Boston University). The current editors-in-chief are DeLiang Wang (Ohio State University) and Taro Toyoizumi ... |
Neural Networks (journal) : The journal is abstracted and indexed in Scopus and the Science Citation Index Expanded. According to the Journal Citation Reports, the journal has a 2022 impact factor of 7.8. |
Word-sense disambiguation : Word-sense disambiguation is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious. Given that natural language requires reflection of neurological reality, as shaped by the ... |
Word-sense disambiguation : Disambiguation requires two strict inputs: a dictionary to specify the senses which are to be disambiguated and a corpus of language data to be disambiguated (in some methods, a training corpus of language examples is also required). WSD task has two variants: "lexical sample" (disambiguatin... |
Word-sense disambiguation : WSD was first formulated as a distinct computational task during the early days of machine translation in the 1940s, making it one of the oldest problems in computational linguistics. Warren Weaver first introduced the problem in a computational context in his 1949 memorandum on translation.... |
Word-sense disambiguation : There are two main approaches to WSD – deep approaches and shallow approaches. Deep approaches presume access to a comprehensive body of world knowledge. These approaches are generally not considered to be very successful in practice, mainly because such a body of knowledge does not exist in... |
Word-sense disambiguation : Knowledge is a fundamental component of WSD. Knowledge sources provide data which are essential to associate senses with words. They can vary from corpora of texts, either unlabeled or annotated with word senses, to machine-readable dictionaries, thesauri, glossaries, ontologies, etc. They c... |
Word-sense disambiguation : Comparing and evaluating different WSD systems is extremely difficult, because of the different test sets, sense inventories, and knowledge resources adopted. Before the organization of specific evaluation campaigns most systems were assessed on in-house, often small-scale, data sets. In ord... |
Word-sense disambiguation : Babelfy, a unified state-of-the-art system for multilingual Word Sense Disambiguation and Entity Linking BabelNet API, a Java API for knowledge-based multilingual Word Sense Disambiguation in 6 different languages using the BabelNet semantic network WordNet::SenseRelate, a project that inclu... |
Word-sense disambiguation : Controlled natural language Entity linking Judicial interpretation Semantic unification Sentence boundary disambiguation Syntactic ambiguity |
Word-sense disambiguation : Computational Linguistics Special Issue on Word Sense Disambiguation (1998) Word Sense Disambiguation Tutorial by Rada Mihalcea and Ted Pedersen (2005). |
Inductive programming : Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output example... |
Inductive programming : Inductive programming incorporates all approaches which are concerned with learning programs or algorithms from incomplete (formal) specifications. Possible inputs in an IP system are a set of training inputs and corresponding outputs or an output evaluation function, describing the desired beha... |
Inductive programming : Research on the inductive synthesis of recursive functional programs started in the early 1970s and was brought onto firm theoretical foundations with the seminal THESIS system of Summers and work of Biermann. These approaches were split into two phases: first, input-output examples are transfor... |
Inductive programming : The first workshop on Approaches and Applications of Inductive Programming (AAIP) held in conjunction with ICML 2005 identified all applications where "learning of programs or recursive rules are called for, [...] first in the domain of software engineering where structural learning, software as... |
Inductive programming : Evolutionary programming Inductive reasoning Test-driven development |
Inductive programming : Inductive Programming community page, hosted by the University of Bamberg. |
Means–ends analysis : Means–ends analysis (MEA) is a problem solving technique used commonly in artificial intelligence (AI) for limiting search in AI programs. It is also a technique used at least since the 1950s as a creativity tool, most frequently mentioned in engineering books on design methods. MEA is also relate... |
Means–ends analysis : An important aspect of intelligent behavior as studied in AI is goal-based problem solving, a framework in which the solution to a problem can be described by finding a sequence of actions that lead to a desirable goal. A goal-seeking system is supposed to be connected to its outside environment b... |
Means–ends analysis : The MEA technique is a strategy to control search in problem-solving. Given a current state and a goal state, an action is chosen which will reduce the difference between the two. The action is performed on the current state to produce a new state, and the process is recursively applied to this ne... |
Means–ends analysis : The MEA technique as a problem-solving strategy was first introduced in 1961 by Allen Newell and Herbert A. Simon in their computer problem-solving program General Problem Solver (GPS). In that implementation, the correspondence between differences and actions, also called operators, is provided a... |
Means–ends analysis : Causal layered analysis Knowledge representation Automated reasoning Intelligent control Cognitive load Mathematical proof Futures techniques Polytely Gap analysis Hill climbing == References == |
You Only Look Once : You Only Look Once (YOLO) is a series of real-time object detection systems based on convolutional neural networks. First introduced by Joseph Redmon et al. in 2015, YOLO has undergone several iterations and improvements, becoming one of the most popular object detection frameworks. The name "You O... |
You Only Look Once : Compared to previous methods like R-CNN and OverFeat, instead of applying the model to an image at multiple locations and scales, YOLO applies a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. These ... |
You Only Look Once : There are two parts to the YOLO series. The original part contained YOLOv1, v2, and v3, all released on a website maintained by Joseph Redmon. |
You Only Look Once : Computer vision Object detection Convolutional neural network R-CNN SqueezeNet MobileNet EfficientNet |
You Only Look Once : Official YOLO website YOLO implementation in Darknet |
Offline learning : Offline learning is a machine learning training approach in which a model is trained on a fixed dataset that is not updated during the learning process. This dataset is collected beforehand, and the learning typically occurs in a batch mode (i.e., the model is updated using batches of data, rather th... |
Offline learning : Online machine learning Incremental learning == References == |
Bias–variance tradeoff : In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as we increase the number of... |
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-v... |
Bias–variance tradeoff : Suppose that we have a training set consisting of a set of points x 1 , … , x n ,\dots ,x_ and real-valued labels y i associated with the points x i . We assume that the data is generated by a function f ( x ) such as y = f ( x ) + ε , where the noise, ε , has zero mean and variance σ 2 .... |
Bias–variance tradeoff : Dimensionality reduction and feature selection can decrease variance by simplifying models. Similarly, a larger training set tends to decrease variance. Adding features (predictors) tends to decrease bias, at the expense of introducing additional variance. Learning algorithms typically have som... |
Bias–variance tradeoff : MLU-Explain: The Bias Variance Tradeoff — An interactive visualization of the bias–variance tradeoff in LOESS Regression and K-Nearest Neighbors. |
Spiking neural network : Spiking neural networks (SNNs) are artificial neural networks (ANN) that mimic natural neural networks. These models leverage timing of discrete spikes as the main information carrier. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. T... |
Spiking neural network : Many multi-layer artificial neural networks are fully connected, receiving input from every neuron in the previous layer and signalling every neuron in the subsequent layer. Although these networks have achieved breakthroughs in many fields, they are biologically inaccurate and do not mimic the... |
Spiking neural network : Information in the brain is represented as action potentials (neuron spikes), which may be grouped into spike trains or even coordinated waves of brain activity. A fundamental question of neuroscience is to determine whether neurons communicate by a rate or temporal code. Temporal coding sugges... |
Spiking neural network : SNNs can in principle be applied to the same applications as traditional ANNs. In addition, SNNs can model the central nervous system of biological organisms, such as an insect seeking food without prior knowledge of the environment. Due to their relative realism, they can be used to study the ... |
Spiking neural network : A diverse range of application software can simulate SNNs. This software can be classified according to its uses: |
Spiking neural network : Sutton and Barton propose that future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Experimental systems based on ferroelectric tunnel junctions have been used to show that STDP ... |
Spiking neural network : Classification capabilities of spiking networks trained according to unsupervised learning methods have been tested on the common benchmark datasets, such as, Iris, Wisconsin Breast Cancer or Statlog Landsat dataset. Various approaches to information encoding and network design have been used. ... |
Sentence extraction : Sentence extraction is a technique used for automatic summarization of a text. In this shallow approach, statistical heuristics are used to identify the most salient sentences of a text. Sentence extraction is a low-cost approach compared to more knowledge-intensive deeper approaches which require... |
Sentence extraction : Usually, a combination of heuristics is used to determine the most important sentences within the document. Each heuristic assigns a (positive or negative) score to the sentence. After all heuristics have been applied, the highest-scoring sentences are included in the summary. The individual heuri... |
Sentence extraction : Sentence boundary disambiguation Text segmentation == References == |
Yarowsky algorithm : In computational linguistics the Yarowsky algorithm is an unsupervised learning algorithm for word sense disambiguation that uses the "one sense per collocation" and the "one sense per discourse" properties of human languages for word sense disambiguation. From observation, words tend to exhibit on... |
Yarowsky algorithm : The algorithm starts with a large, untagged corpus, in which it identifies examples of the given polysemous word, and stores all the relevant sentences as lines. For instance, Yarowsky uses the word "plant" in his 1995 paper to demonstrate the algorithm. If it is assumed that there are two possible... |
Yarowsky algorithm : Semantic net Word sense disambiguation |
Yarowsky algorithm : Yarowsky, David (1995). "Unsupervised Word Sense Disambiguation Rivaling Supervised Methods". Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics. Cambridge, MA: Association for Computational Linguistics: 189–196. doi:10.3115/981658.981684. Retrieved 1 November 2... |
Linear separability : In Euclidean geometry, linear separability is a property of two sets of points. This is most easily visualized in two dimensions (the Euclidean plane) by thinking of one set of points as being colored blue and the other set of points as being colored red. These two sets are linearly separable if t... |
Linear separability : Let X 0 and X 1 be two sets of points in an n-dimensional Euclidean space. Then X 0 and X 1 are linearly separable if there exist n + 1 real numbers w 1 , w 2 , . . , w n , k ,w_,..,w_,k , such that every point x ∈ X 0 satisfies ∑ i = 1 n w i x i > k ^w_x_>k and every point x ∈ X 1 satisfies... |
Linear separability : Three non-collinear points in two classes ('+' and '-') are always linearly separable in two dimensions. This is illustrated by the three examples in the following figure (the all '+' case is not shown, but is similar to the all '-' case): However, not all sets of four points, no three collinear, ... |
Linear separability : Let T ( N , K ) be the number of ways to linearly separate N points (in general position) in K dimensions, then T ( N , K ) = 2^&K\geq N\\2\sum _^\left(N-1\\k\end\right)&K<N\end\right. When K is large, T ( N , K ) / 2 N is very close to one when N ≤ 2 K , but very close to zero when N > 2 K . ... |
Linear separability : A Boolean function in n variables can be thought of as an assignment of 0 or 1 to each vertex of a Boolean hypercube in n dimensions. This gives a natural division of the vertices into two sets. The Boolean function is said to be linearly separable provided these two sets of points are linearly se... |
Linear separability : A linear threshold logic gate is a Boolean function defined by n weights w 1 , … , w n ,\dots ,w_ and a threshold θ . It takes n binary inputs x 1 , … , x n ,\dots ,x_ , and outputs 1 if ∑ i w i x i > θ w_x_>\theta , and otherwise outputs 0. For any fixed n , because there are only finitely m... |
Linear separability : Classifying data is a common task in machine learning. Suppose some data points, each belonging to one of two sets, are given and we wish to create a model that will decide which set a new data point will be in. In the case of support vector machines, a data point is viewed as a p-dimensional vect... |
Linear separability : Clustering (statistics) Hyperplane separation theorem Kirchberger's theorem Perceptron Vapnik–Chervonenkis dimension == References == |
Table extraction : Table extraction is the process of recognizing and separating a table from a large document, possibly also recognizing individual rows, columns or elements. It may be regarded as a special form of information extraction. Table extractions from webpages can take advantage of the special HTML elements ... |
Modular neural network : A modular neural network is an artificial neural network characterized by a series of independent neural networks moderated by some intermediary. Each independent neural network serves as a module and operates on separate inputs to accomplish some subtask of the task the network hopes to perfor... |
Modular neural network : As artificial neural network research progresses, it is appropriate that artificial neural networks continue to draw on their biological inspiration and emulate the segmentation and modularization found in the brain. The brain, for example, divides the complex task of visual perception into man... |
Modular neural network : Unlike a single large network that can be assigned to arbitrary tasks, each module in a modular network must be assigned a specific task and connected to other modules in specific ways by a designer. In the vision example, the brain evolved (rather than learned) to create the LGN. In some cases... |
Modular neural network : Modular neural networks reduce a single large, unwieldy neural network to smaller, potentially more manageable components. Some tasks are intractably large for a single neural network. The benefits of modular neural networks include: |
Modular neural network : Azam, Farooq (2000). "Biologically Inspired Modular Neural Networks. PhD Dissertation". Virginia Tech. hdl:10919/27998. Happel, Bart; Murre, Jacob (1994). "The Design and Evolution of Modular Neural Network Architectures" (PDF). Neural Networks. 7 (6–7): 985–1004. doi:10.1016/s0893-6080(05)8015... |
Artificial brain : An artificial brain (or artificial mind) is software and hardware with cognitive abilities similar to those of the animal or human brain. Research investigating "artificial brains" and brain emulation plays three important roles in science: An ongoing attempt by neuroscientists to understand how the ... |
Artificial brain : Although direct human brain emulation using artificial neural networks on a high-performance computing engine is a commonly discussed approach, there are other approaches. An alternative artificial brain implementation could be based on Holographic Neural Technology (HNeT) non linear phase coherence/... |
Artificial brain : Some critics of brain simulation believe that it is simpler to create general intelligent action directly without imitating nature. Some commentators have used the analogy that early attempts to construct flying machines modeled them after birds, but that modern aircraft do not look like birds. |
Artificial brain : Neukart, Florian (23 November 2016). Reverse Engineering the Mind - Consciously Acting Machines and Accelerated Evolution. Wolfsburg, Germany: Springer. ISBN 978-3-658-16176-7. Retrieved 30 October 2016. Bandyopadhyay, Anirban (4 April 2020). Nanobrain : The Making of an Artificial Brain from a Time ... |
Competitive learning : Competitive learning is a form of unsupervised learning in artificial neural networks, in which nodes compete for the right to respond to a subset of the input data. A variant of Hebbian learning, competitive learning works by increasing the specialization of each node in the network. It is well ... |
Competitive learning : There are three basic elements to a competitive learning rule: A set of neurons that are all the same except for some randomly distributed synaptic weights, and which therefore respond differently to a given set of input patterns A limit imposed on the "strength" of each neuron A mechanism that p... |
Competitive learning : Competitive Learning is usually implemented with Neural Networks that contain a hidden layer which is commonly known as “competitive layer”. Every competitive neuron is described by a vector of weights w i = ( w i 1 , . . , w i d ) T , i = 1 , . . , M _=\left(,..,w_\right)^,i=1,..,M and calculat... |
Competitive learning : Here is a simple competitive learning algorithm to find three clusters within some input data. 1. (Set-up.) Let a set of sensors all feed into three different nodes, so that every node is connected to every sensor. Let the weights that each node gives to its sensors be set randomly between 0.0 an... |
Competitive learning : Ensemble learning Neural gas Pandemonium architecture |
Competitive learning : Draft Report "Some Competitive Learning Methods" pdf(contains descriptions of several related algos) DemoGNG - Java simulator for competitive learning methods |
History of artificial neural networks : Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by biological neural circuitry. While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first imple... |
History of artificial neural networks : The simplest feedforward network consists of a single weight layer without activation functions. It would be just a linear map, and training it would be linear regression. Linear regression by least squares method was used by Adrien-Marie Legendre (1805) and Carl Friedrich Gauss ... |
History of artificial neural networks : Backpropagation is an efficient application of the chain rule derived by Gottfried Wilhelm Leibniz in 1673 to networks of differentiable nodes. The terminology "back-propagating errors" was actually introduced in 1962 by Rosenblatt, but he did not know how to implement this, alth... |
History of artificial neural networks : One origin of the recurrent neural network (RNN) was statistical mechanics. The Ising model was developed by Wilhelm Lenz and Ernst Ising in the 1920s as a simple statistical mechanical model of magnets at equilibrium. Glauber in 1963 studied the Ising model evolving in time, as ... |
History of artificial neural networks : The origin of the CNN architecture is the "neocognitron" introduced by Kunihiko Fukushima in 1980. It was inspired by work of Hubel and Wiesel in the 1950s and 1960s which showed that cat visual cortices contain neurons that individually respond to small regions of the visual fie... |
History of artificial neural networks : The deep learning revolution started around CNN- and GPU-based computer vision. Although CNNs trained by backpropagation had been around for decades and GPU implementations of NNs for years, including CNNs, faster implementations of CNNs on GPUs were needed to progress on compute... |
History of artificial neural networks : In 1991, Juergen Schmidhuber published "artificial curiosity", neural networks in a zero-sum game. The first network is a generative model that models a probability distribution over output patterns. The second network learns by gradient descent to predict the reactions of the en... |
History of artificial neural networks : The human selective attention had been studied in neuroscience and cognitive psychology. Selective attention of audition was studied in the cocktail party effect (Colin Cherry, 1953). (Donald Broadbent, 1958) proposed the filter model of attention. Selective attention of vision w... |
History of artificial neural networks : The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), combining millions or billions of MOS transistors onto a single chip in the form of complementary MOS (CMOS) technology, enabled the development of practical artificial neural networks in the ... |
History of artificial neural networks : "Lecun 2019-7-11 ACM Tech Talk". Google Docs. Retrieved 2020-02-13. |
Semantic analysis (computational) : Semantic analysis (computational) within applied linguistics and computer science, is a composite of semantic analysis and computational components. Semantic analysis refers to a formal analysis of meaning, and computational refers to approaches that in principle support effective im... |
Semantic analysis (computational) : Computational semantics Natural language processing Semantic analytics Semantic analysis (machine learning) Semantic Web SemEval |
Semantic analysis (computational) : Chris Fox (2010), "Computational Semantics", In Alexander Clark, Chris Fox, and Shalom Lappin, editors. The Handbook of Computational Linguistics and Natural Language Processing. Malden, MA: Wiley-Blackwell, 394–428. Agirre, Eneko, Lluis Marquez & Richard Wincentowski (2009), "Comput... |
Text simplification : Text simplification is an operation used in natural language processing to change, enhance, classify, or otherwise process an existing body of human-readable text so its grammar and structure is greatly simplified while the underlying meaning and information remain the same. Text simplification is... |
Text simplification : Text simplification is illustrated with an example used by Siddharthan (2006). The first sentence contains two relative clauses and one conjoined verb phrase. A text simplification system aims to change the first sentence into a group of simpler sentences, as seen just below the first sentence. Al... |
Text simplification : Automated paraphrasing Controlled natural language Language reform Lexical simplification Lexical substitution Semantic compression Text normalization Simplified English Basic English |
Text simplification : Wei Xu, Chris Callison-Burch and Courtney Napoles. "Problems in Current Text Simplification Research". In Transactions of the Association for Computational Linguistics (TACL), Volume 3, 2015, Pages 283–297. Advaith Siddharthan. "Syntactic Simplification and Text Cohesion". In Research on Language ... |
Text simplification : Automatic Induction of Rules for Text Simplification 1996 Text Simplification for Information-Seeking Applications 2004 |
Local case-control sampling : In machine learning, local case-control sampling is an algorithm used to reduce the complexity of training a logistic regression classifier. The algorithm reduces the training complexity by selecting a small subsample of the original dataset for training. It assumes the availability of a (... |
Local case-control sampling : In classification, a dataset is a set of N data points ( x i , y i ) i = 1 N ,y_)_^ , where x i ∈ R d \in \mathbb ^ is a feature vector, y i ∈ \in \ is a label. Intuitively, a dataset is imbalanced when certain important statistical patterns are rare. The lack of observations of certain ... |
Local case-control sampling : In logistic regression, given the model θ = ( α , β ) , the prediction is made according to P ( Y = 1 ∣ X ; θ ) = p ~ θ ( x ) = exp ( α + β T x ) 1 + exp ( α + β T x ) (Y=1\mid X;\theta )=_(x)=x)x) . The local-case control sampling algorithm assumes the availability of a pilot model ... |
Local case-control sampling : The algorithm has the following properties. When the pilot is consistent, the estimates using the samples from local case-control sampling is consistent even under model misspecification. If the model is correct then the algorithm has exactly twice the asymptotic variance of logistic regre... |
GPT-4 : Generative Pre-trained Transformer 4 (GPT-4) is a multimodal large language model trained and created by OpenAI and the fourth in its series of GPT foundation models. It was launched on March 14, 2023, and made publicly available via the paid chatbot product ChatGPT Plus, via OpenAI's API, and via the free chat... |
GPT-4 : OpenAI introduced the first GPT model (GPT-1) in 2018, publishing a paper called "Improving Language Understanding by Generative Pre-Training.", which was based on the transformer architecture and trained on a large corpus of books. The next year, they introduced GPT-2, a larger model that could generate cohere... |
GPT-4 : OpenAI stated that GPT-4 is "more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5." They produced two versions of GPT-4, with context windows of 8,192 and 32,768 tokens, a significant improvement over GPT-3.5 and GPT-3, which were limited to 4,096 and 2,048 tokens respectively... |
GPT-4 : Like its predecessors, GPT-4 has been known to hallucinate, meaning that the outputs may include information not in the training data or that contradicts the user's prompt. GPT-4 also lacks transparency in its decision-making processes. If requested, the model is able to provide an explanation as to how and why... |
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