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LeNet : LeNet-5, convolutional neural networks. An online project page for LeNet maintained by Yann LeCun, containing animations and bibliography. projects:lush [leon.bottou.org]. Lush, an object-oriented programming language. It contains SN, a neural network simulator. The LeNet series was written in SN. |
Time series : In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of su... |
Time series : Methods for time series analysis may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and wavelet analysis; the latter include auto-correlation and cross-correlation analysis. In the time domain, correlation and analysis can be made in a f... |
Time series : A time series is one type of panel data. Panel data is the general class, a multidimensional data set, whereas a time series data set is a one-dimensional panel (as is a cross-sectional dataset). A data set may exhibit characteristics of both panel data and time series data. One way to tell is to ask what... |
Time series : There are several types of motivation and data analysis available for time series which are appropriate for different purposes. |
Time series : Models for time series data can have many forms and represent different stochastic processes. When modeling variations in the level of a process, three broad classes of practical importance are the autoregressive (AR) models, the integrated (I) models, and the moving-average (MA) models. These three class... |
Time series : Time series can be visualized with two categories of chart: Overlapping Charts and Separated Charts. Overlapping Charts display all-time series on the same layout while Separated Charts presents them on different layouts (but aligned for comparison purpose) |
Time series : De Gooijer, Jan G.; Hyndman, Rob J. (2006). "25 Years of Time Series Forecasting". International Journal of Forecasting. Twenty Five Years of Forecasting. 22 (3): 443–473. CiteSeerX 10.1.1.154.9227. doi:10.1016/j.ijforecast.2006.01.001. S2CID 14996235. Box, George; Jenkins, Gwilym (1976), Time Series Anal... |
Time series : Introduction to Time series Analysis (Engineering Statistics Handbook) — A practical guide to Time series analysis. |
Paraphrasing (computational linguistics) : Paraphrase or paraphrasing in computational linguistics is the natural language processing task of detecting and generating paraphrases. Applications of paraphrasing are varied including information retrieval, question answering, text summarization, and plagiarism detection. P... |
Paraphrasing (computational linguistics) : Multiple methods can be used to evaluate paraphrases. Since paraphrase recognition can be posed as a classification problem, most standard evaluations metrics such as accuracy, f1 score, or an ROC curve do relatively well. However, there is difficulty calculating f1-scores due... |
Paraphrasing (computational linguistics) : Round-trip translation Text simplification – automated processPages displaying wikidata descriptions as a fallback Text normalization – Process of transforming text into a single canonical form |
Paraphrasing (computational linguistics) : Microsoft Research Paraphrase Corpus - a dataset consisting of 5800 pairs of sentences extracted from news articles annotated to note whether a pair captures semantic equivalence Paraphrase Database (PPDB) - A searchable database containing millions of paraphrases in 16 differ... |
CLEVER score : The CLEVER (Cross Lipschitz Extreme Value for nEtwork Robustness) score is a way of measuring the robustness of an artificial neural network towards adversarial attacks. It was developed by a team at the MIT-IBM Watson AI Lab in IBM Research and first presented at the 2018 International Conference on Lea... |
Enterprise cognitive system : Enterprise cognitive systems (ECS) are part of a broader shift in computing, from a programmatic to a probabilistic approach, called cognitive computing. An Enterprise Cognitive System makes a new class of complex decision support problems computable, where the business context is ambiguou... |
Enterprise cognitive system : ECS have to be: Adaptive: They must learn as information changes, and as goals and requirements evolve. They must resolve ambiguity and tolerate unpredictability. They must be engineered to feed on dynamic data in real time, or near real time. In the Enterprise, near-real time learning fro... |
Enterprise cognitive system : Bottlenose – trends and brands monitoring Cybereason – security threat monitoring Dataminr – social media monitoring == Further reading == |
Symbolic artificial intelligence : In artificial intelligence, symbolic artificial intelligence (also known as classical artificial intelligence or logic-based artificial intelligence) is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable... |
Symbolic artificial intelligence : A short history of symbolic AI to the present day follows below. Time periods and titles are drawn from Henry Kautz's 2020 AAAI Robert S. Engelmore Memorial Lecture and the longer Wikipedia article on the History of AI, with dates and titles differing slightly for increased clarity. |
Symbolic artificial intelligence : This section provides an overview of techniques and contributions in an overall context leading to many other, more detailed articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered earlier in the history section. |
Symbolic artificial intelligence : Controversies arose from early on in symbolic AI, both within the field—e.g., between logicists (the pro-logic "neats") and non-logicists (the anti-logic "scruffies")—and between those who embraced AI but rejected symbolic approaches—primarily connectionists—and those outside the fiel... |
Symbolic artificial intelligence : Brooks, Rodney A. (1991). "Intelligence without representation". Artificial Intelligence. 47 (1): 139–159. doi:10.1016/0004-3702(91)90053-M. ISSN 0004-3702. S2CID 207507849. Retrieved 2022-09-13. Clancey, William (1987). Knowledge-Based Tutoring: The GUIDON Program (MIT Press Series i... |
Semantic decomposition (natural language processing) : A semantic decomposition is an algorithm that breaks down the meanings of phrases or concepts into less complex concepts. The result of a semantic decomposition is a representation of meaning. This representation can be used for tasks, such as those related to arti... |
Semantic decomposition (natural language processing) : Given that an AI does not inherently have language, it is unable to think about the meanings behind the words of a language. An artificial notion of meaning needs to be created for a strong AI to emerge. Creating an artificial representation of meaning requires the... |
Semantic decomposition (natural language processing) : Representing meaning as a graph is one of the two ways that both an AI cognition and a linguistic researcher think about meaning (connectionist view). Logicians utilize a formal representation of meaning to build upon the idea of symbolic representation, whereas de... |
Semantic decomposition (natural language processing) : Latent Semantic Analysis Lexical semantics Principle of compositionality == References == |
Autoassociative memory : Autoassociative memory, also known as auto-association memory or an autoassociation network, is any type of memory that is able to retrieve a piece of data from only a tiny sample of itself. They are very effective in de-noising or removing interference from the input and can be used to determi... |
Autoassociative memory : Standard memories (data storage) are organized by being indexed by positional memory addresses which are also used for data retrieval. Autoassociative memories are organized in such a way that data is stored in a graph like system with connection weights based on the number of inherent associat... |
Autoassociative memory : For example, the sentence fragments presented below are sufficient for most English-speaking adult humans to recall the missing information. "To be or not to be, that is _____." "I came, I saw, _____." Many readers will realize the missing information is in fact: "To be or not to be, that is th... |
Automated decision-making : Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or interventi... |
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 appl... |
Automated decision-making : Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor... |
Automated decision-making : Automated decision-making technologies (ADMT) are software-coded digital tools that automate the translation of input data to output data, contributing to the function of automated decision-making systems. There are a wide range of technologies in use across ADM applications and systems. ADM... |
Automated decision-making : ADM is being used to replace or augment human decision-making by both public and private-sector organisations for a range of reasons including to help increase consistency, improve efficiency, reduce costs and enable new solutions to complex problems. |
Automated decision-making : There are many social, ethical and legal implications of automated decision-making systems. Concerns raised include lack of transparency and contestability of decisions, incursions on privacy and surveillance, exacerbating systemic bias and inequality due to data and algorithmic bias, intell... |
Automated decision-making : Many academic disciplines and fields are increasingly turning their attention to the development, application and implications of ADM including business, computer sciences, human computer interaction (HCI), law, public administration, and media and communications. The automation of media con... |
Automated decision-making : Automated decision support Algorithmic bias Decision-making software Decision Management Ethics of artificial intelligence Government by algorithm Machine learning Recommender systems == References == |
Sparkles emoji : The Sparkles emoji (✨) is an emoji that has one large star surrounded by smaller stars. Originating from Japan to represent sparkles used in anime and manga, the sparkles are often used as emphasis in text by surrounding words or phrases with it. It is the third most-used emoji in the world on Twitter ... |
Sparkles emoji : According to Emojipedia, the Sparkles emoji was first used by Japanese mobile operators SoftBank, Docomo and au in the late 1990s. The emoji was added to Unicode 6.0 in 2010 and Emoji 1.0 in 2015. On some platforms the Sparkles emoji has been multicoloured whilst on other platforms it has been one colo... |
Long short-term memory : Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem commonly encountered by traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models, and other sequence learning met... |
Long short-term memory : In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning... |
Long short-term memory : In the equations below, the lowercase variables represent vectors. Matrices W q and U q contain, respectively, the weights of the input and recurrent connections, where the subscript q can either be the input gate i , output gate o , the forget gate f or the memory cell c , depending on ... |
Long short-term memory : An RNN using LSTM units can be trained in a supervised fashion on a set of training sequences, using an optimization algorithm like gradient descent combined with backpropagation through time to compute the gradients needed during the optimization process, in order to change each weight of the ... |
Long short-term memory : Applications of LSTM include: 2015: Google started using an LSTM trained by CTC for speech recognition on Google Voice. According to the official blog post, the new model cut transcription errors by 49%. 2016: Google started using an LSTM to suggest messages in the Allo conversation app. In the... |
Long short-term memory : Monner, Derek D.; Reggia, James A. (2010). "A generalized LSTM-like training algorithm for second-order recurrent neural networks" (PDF). Neural Networks. 25 (1): 70–83. doi:10.1016/j.neunet.2011.07.003. PMC 3217173. PMID 21803542. High-performing extension of LSTM that has been simplified to a... |
Long short-term memory : Recurrent Neural Networks with over 30 LSTM papers by Jürgen Schmidhuber's group at IDSIA Zhang, Aston; Lipton, Zachary; Li, Mu; Smola, Alexander J. (2024). "10.1. Long Short-Term Memory (LSTM)". Dive into deep learning. Cambridge New York Port Melbourne New Delhi Singapore: Cambridge Universit... |
Transduction (machine learning) : In logic, statistical inference, and supervised learning, transduction or transductive inference is reasoning from observed, specific (training) cases to specific (test) cases. In contrast, induction is reasoning from observed training cases to general rules, which are then applied to ... |
Transduction (machine learning) : The mode of inference from particulars to particulars, which Vapnik came to call transduction, was already distinguished from the mode of inference from particulars to generalizations in part III of the Cambridge philosopher and logician W.E. Johnson's 1924 textbook, Logic. In Johnson'... |
Transduction (machine learning) : The following example problem contrasts some of the unique properties of transduction against induction. A collection of points is given, such that some of the points are labeled (A, B, or C), but most of the points are unlabeled (?). The goal is to predict appropriate labels for all o... |
Transduction (machine learning) : Transduction algorithms can be broadly divided into two categories: those that seek to assign discrete labels to unlabeled points, and those that seek to regress continuous labels for unlabeled points. Algorithms that seek to predict discrete labels tend to be derived by adding partial... |
Transduction (machine learning) : De Finetti, Bruno. "La prévision: ses lois logiques, ses sources subjectives." Annales de l'institut Henri Poincaré. Vol. 7. No. 1. 1937. de Finetti, Bruno (1970). Theory of Probability: A Critical Introductory Treatment. New York: John Wiley. W.E. Johnson Logic part III, CUP Archive, ... |
Transduction (machine learning) : A Gammerman, V. Vovk, V. Vapnik (1998). "Learning by Transduction." An early explanation of transductive learning. "A Discussion of Semi-Supervised Learning and Transduction," Chapter 25 of Semi-Supervised Learning, Olivier Chapelle, Bernhard Schölkopf and Alexander Zien, eds. (2006). ... |
Text-to-video model : A text-to-video model is a machine learning model that uses a natural language description as input to produce a video relevant to the input text. Advancements during the 2020s in the generation of high-quality, text-conditioned videos have largely been driven by the development of video diffusion... |
Text-to-video model : There are different models, including open source models. Chinese-language input CogVideo is the earliest text-to-video model "of 9.4 billion parameters" to be developed, with its demo version of open source codes first presented on GitHub in 2022. That year, Meta Platforms released a partial text... |
Text-to-video model : There are several architectures that have been used to create Text-to-Video models. Similar to Text-to-Image models, these models can be trained using Recurrent Neural Networks (RNNs) such as long short-term memory (LSTM) networks, which has been used for Pixel Transformation Models and Stochastic... |
Text-to-video model : Despite the rapid evolution of Text-to-Video models in their performance, a primary limitation is that they are very computationally heavy which limits its capacity to provide high quality and lengthy outputs. Additionally, these models require a large amount of specific training data to be able t... |
Text-to-video model : The deployment of Text-to-Video models raises ethical considerations related to content generation. These models have the potential to create inappropriate or unauthorized content, including explicit material, graphic violence, misinformation, and likenesses of real individuals without consent. En... |
Text-to-video model : Text-to-Video models offer a broad range of applications that may benefit various fields, from educational and promotional to creative industries. These models can streamline content creation for training videos, movie previews, gaming assets, and visualizations, making it easier to generate high-... |
Text-to-video model : Text-to-image model VideoPoet, unreleased Google's model, precursor of Lumiere Deepfake Human image synthesis ChatGPT == References == |
Capsule neural network : A capsule neural network (CapsNet) is a machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization. The idea is to add structures called "c... |
Capsule neural network : In 2000, Geoffrey Hinton et al. described an imaging system that combined segmentation and recognition into a single inference process using parse trees. So-called credibility networks described the joint distribution over the latent variables and over the possible parse trees. That system prov... |
Capsule neural network : An invariant is an object property that does not change as a result of some transformation. For example, the area of a circle does not change if the circle is shifted to the left. Informally, an equivariant is a property that changes predictably under transformation. For example, the center of ... |
Capsule neural network : Capsnets reject the pooling layer strategy of conventional CNNs that reduces the amount of detail to be processed at the next higher layer. Pooling allows a degree of translational invariance (it can recognize the same object in a somewhat different location) and allows a larger number of featu... |
Capsule neural network : A capsule is a set of neurons that individually activate for various properties of a type of object, such as position, size and hue. Formally, a capsule is a set of neurons that collectively produce an activity vector with one element for each neuron to hold that neuron's instantiation value (e... |
Capsule neural network : The outputs from one capsule (child) are routed to capsules in the next layer (parent) according to the child's ability to predict the parents' outputs. Over the course of a few iterations, each parents' outputs may converge with the predictions of some children and diverge from those of others... |
Capsule neural network : Learning is supervised. The network is trained by minimizing the euclidean distance between the image and the output of a CNN that reconstructs the input from the output of the terminal capsules. The network is discriminatively trained, using iterative routing-by-agreement. The activity vectors... |
Capsule neural network : The first convolutional layers perform feature extraction. For the 28x28 pixel MNIST image test an initial 256 9x9 pixel convolutional kernels (using stride 1 and rectified linear unit (ReLU) activation, defining 20x20 receptive fields) convert the pixel input into 1D feature activations and in... |
Capsule neural network : Human vision examines a sequence of focal points (directed by saccades), processing only a fraction of the scene at its highest resolution. Capsnets build on inspirations from cortical minicolumns (also called cortical microcolumns) in the cerebral cortex. A minicolumn is a structure containing... |
Capsule neural network : CapsNets are claimed to have four major conceptual advantages over convolutional neural networks (CNN): Viewpoint invariance: the use of pose matrices allows capsule networks to recognize objects regardless of the perspective from which they are viewed. Fewer parameters: Because capsules group ... |
Capsule neural network : Convolutional neural network Geoffrey Hinton MNIST database |
Capsule neural network : Capsules Network Implementation in PyTorch, fixing several bugs in previous implementations, 2018-04-16, retrieved 2018-04-16 Pytorch code: Capsule Routing via Variational Bayes, February 2020, retrieved 2020-10-23 A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsule... |
Rademacher complexity : In computational learning theory (machine learning and theory of computation), Rademacher complexity, named after Hans Rademacher, measures richness of a class of sets with respect to a probability distribution. The concept can also be extended to real valued functions. |
Rademacher complexity : The Rademacher complexity is typically applied on a function class of models that are used for classification, with the goal of measuring their ability to classify points drawn from a probability space under arbitrary labellings. When the function class is rich enough, it contains functions that... |
Rademacher complexity : 1. A contains a single vector, e.g., A = ⊂ R 2 \subset \mathbb ^ . Then: Rad ( A ) = 1 2 ⋅ ( 1 4 ⋅ ( a + b ) + 1 4 ⋅ ( a − b ) + 1 4 ⋅ ( − a + b ) + 1 4 ⋅ ( − a − b ) ) = 0 (A)=\cdot \left(\cdot (a+b)+\cdot (a-b)+\cdot (-a+b)+\cdot (-a-b)\right)=0 The same is true for every singleton hypot... |
Rademacher complexity : The Rademacher complexity can be used to derive data-dependent upper-bounds on the learnability of function classes. Intuitively, a function-class with smaller Rademacher complexity is easier to learn. |
Rademacher complexity : Since smaller Rademacher complexity is better, it is useful to have upper bounds on the Rademacher complexity of various function sets. The following rules can be used to upper-bound the Rademacher complexity of a set A ⊂ R m ^ .: 329–330 1. If all vectors in A are translated by a constant vec... |
Rademacher complexity : Gaussian complexity is a similar complexity with similar physical meanings, and can be obtained from the Rademacher complexity using the random variables g i instead of σ i , where g i are Gaussian i.i.d. random variables with zero-mean and variance 1, i.e. g i ∼ N ( 0 , 1 ) \sim (0,1) . Gaus... |
Rademacher complexity : Peter L. Bartlett, Shahar Mendelson (2002) Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. Journal of Machine Learning Research 3 463–482 Giorgio Gnecco, Marcello Sanguineti (2008) Approximation Error Bounds via Rademacher's Complexity. Applied Mathematical Sciences, Vo... |
Information retrieval : Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be ... |
Information retrieval : An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval, a query does not uniquely identify a single object in the collection. Instead, sever... |
Information retrieval : there is ... a machine called the Univac ... whereby letters and figures are coded as a pattern of magnetic spots on a long steel tape. By this means the text of a document, preceded by its subject code symbol, can be recorded ... the machine ... automatically selects and types out those referen... |
Information retrieval : Areas where information retrieval techniques are employed include (the entries are in alphabetical order within each category): |
Information retrieval : In order to effectively retrieve relevant documents by IR strategies, the documents are typically transformed into a suitable representation. Each retrieval strategy incorporates a specific model for its document representation purposes. The picture on the right illustrates the relationship of s... |
Information retrieval : The evaluation of an information retrieval system' is the process of assessing how well a system meets the information needs of its users. In general, measurement considers a collection of documents to be searched and a search query. Traditional evaluation metrics, designed for Boolean retrieval... |
Information retrieval : Lemur Lucene Solr Elasticsearch Manatee Manticore search Sphinx Terrier Search Engine Xapian |
Information retrieval : Before the 1900s 1801: Joseph Marie Jacquard invents the Jacquard loom, the first machine to use punched cards to control a sequence of operations. 1880s: Herman Hollerith invents an electro-mechanical data tabulator using punch cards as a machine readable medium. 1890 Hollerith cards, keypunche... |
Information retrieval : SIGIR: Special Interest Group on Information Retrieval ECIR: European Conference on Information Retrieval CIKM: Conference on Information and Knowledge Management WWW: International World Wide Web Conference |
Information retrieval : Tony Kent Strix award Gerard Salton Award Karen Spärck Jones Award |
Information retrieval : Ricardo Baeza-Yates, Berthier Ribeiro-Neto. Modern Information Retrieval: The Concepts and Technology behind Search (second edition) Archived 2017-09-18 at the Wayback Machine. Addison-Wesley, UK, 2011. Stefan Büttcher, Charles L. A. Clarke, and Gordon V. Cormack. Information Retrieval: Implemen... |
Information retrieval : ACM SIGIR: Information Retrieval Special Interest Group BCS IRSG: British Computer Society – Information Retrieval Specialist Group Text Retrieval Conference (TREC) Forum for Information Retrieval Evaluation (FIRE) Information Retrieval (online book) by C. J. van Rijsbergen Information Retrieval... |
GPTs : GPTs are customizable applications based on the Artificial Intelligence, abbreviated as AI, language model of ChatGPT that can perform a wide variety of tasks tailored to specific needs. GPTs can be used and created from the GPT Store. Any user can easily create them without any programming knowledge. By January... |
GPTs : GPTs can be configured to answer complex questions in specific fields, solve problems, provide image-based information, or create digital content. They can be programmed as educational tools, purchasing guides, or technical advisors, as well as for many others applications. GPTs are accessed from the GPT Store s... |
GPTs : Installing GPTs is a simple task that does not require prior programming knowledge. Free users can use existing GPTs but cannot customize their own. Paying subscribers, can use a “Create a GPT” link at the page chat.openai.com/gpts/editor where they can specify the title and description and upload an image or as... |
GPTs : The implementation and use of GPTs has not been without criticism. The main concerns are ethics and the accuracy of the information generated. The GPT Store has been criticized for the proliferation of low-quality GPTs and spam due to a lack of effective moderation. There are also concerns about data privacy and... |
GPTs : Generative pre-trained transformer |
MeaningCloud : MeaningCloud is a Software as a Service product that enables users to embed text analytics and semantic processing in any application or system. It was previously branded as Textalytics. MeaningCloud extends the concept of semantic API with a cloud-based framework that makes the integration of semantic p... |
MeaningCloud : MeaningCloud is a brand by MeaningCloud LLC, a wholly owned subsidiary of MeaningCloud Europe S.L., previously known as Daedalus. Daedalus was founded in 1998 by Jose C Gonzalez and other colleagues as a spin-off from their Artificial Intelligence research lab at the Technical University of Madrid. In Ju... |
MeaningCloud : Topic Extraction: identifies appearances of named entities and abstract concepts in the text. Text Classification: assigns a text to one or several categories in a predefined taxonomy. Sentiment Analysis: assigns a polarity (positive, negative, neutral) to a document or to the individual topics or attrib... |
MeaningCloud : Advanced APIs provide a functionality optimized for diverse applications and ease of use. In addition, customization and integration capabilities offer a fast learning curve and a short time to obtain results. Customized resource management tools allow users to easily incorporate their own semantic resou... |
MeaningCloud : Meaningcloud has been applied to compare French and English Tweets sentiment. |
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