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Computational semantics : Computational semantics is the study of how to automate the process of constructing and reasoning with meaning representations of natural language expressions. It consequently plays an important role in natural-language processing and computational linguistics. Some traditional topics of interest are: construction of meaning representations, semantic underspecification, anaphora resolution, presupposition projection, and quantifier scope resolution. Methods employed usually draw from formal semantics or statistical semantics. Computational semantics has points of contact with the areas of lexical semantics (word-sense disambiguation and semantic role labeling), discourse semantics, knowledge representation and automated reasoning (in particular, automated theorem proving). Since 1999 there has been an ACL special interest group on computational semantics, SIGSEM.
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Computational semantics : Discourse representation theory Formal semantics (natural language) Minimal recursion semantics Natural-language understanding Semantic compression Semantic parsing Semantic Web SemEval WordNet
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Computational semantics : Blackburn, P., and Bos, J. (2005), Representation and Inference for Natural Language: A First Course in Computational Semantics, CSLI Publications. ISBN 1-57586-496-7. Bunt, H., and Muskens, R. (1999), Computing Meaning, Volume 1, Kluwer Publishing, Dordrecht. ISBN 1-4020-0290-4. Bunt, H., Muskens, R., and Thijsse, E. (2001), Computing Meaning, Volume 2, Kluwer Publishing, Dordrecht. ISBN 1-4020-0175-4. Copestake, A., Flickinger, D. P., Sag, I. A., & Pollard, C. (2005). Minimal Recursion Semantics. An introduction. In Research on Language and Computation. 3:281–332. Eijck, J. van, and C. Unger (2010): Computational Semantics with Functional Programming. Cambridge University Press. ISBN 978-0-521-75760-7 Wilks, Y., and Charniak, E. (1976), Computational Semantics: An Introduction to Artificial Intelligence and Natural Language Understanding, North-Holland, Amsterdam. ISBN 0-444-11110-7.
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Computational semantics : Special Interest Group on Computational Semantics (SIGSEM) of the Association for Computational Linguistics (ACL) IWCS - International Workshop on Computational Semantics (endorsed by SIGSEM) ICoS - Inference in Computational Semantics (endorsed by SIGSEM)
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AI notetaker : An AI notetaker is a tool using artificial intelligence to take notes during meetings. They are created by tech companies such as Microsoft and Google; by AI transcription services such as Otter.ai and Fireflies.ai; and by smaller firms such as Circleback, Fathom, Granola, and Krisp. Some business executives send AI notetakers to attend meetings not only to take notes, but also to answer questions on their behalf. The use of AI notetakers raises ethical questions, including recording meetings without the consent of all participants and the possibility that the notetaker will hallucinate and misrepresent what was said during meetings. == References ==
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Artificial Solutions : Artificial Solutions is a multinational technology company that develops technology for conversational AI systems. It rebranded in August 2024 to Teneo.ai. The company's products have been deployed in a wide range of industries including automotive, finance, energy, entertainment, telecoms, the public sector, retail and travel. It’s Customers include telecom providers such as AT&T, as well as other organisations such as Circle K and Folksam, The company was founded in 2001 and became a public company in 2019 after the reverse takeover of Indentive AB. Artificial Solutions is listed on the Nasdaq First North stock exchange under the ticker ASAI.
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Artificial Solutions : Founded in Stockholm in 2001 by Johan Åhlund, Johan Gustavsson and Michael Söderström the company created interactive web assistants using a combination of artificial intelligence and natural language processing. Artificial Solutions expanded with the development of online customer service optimization products and by 2005 it had several offices throughout Europe supporting the development and sales of its online virtual assistants. In 2008 Elbot, Artificial Solutions won the Loebner Prize. In 2010, the company’s management changed, causing the company to focus the basis of its technology on Natural Language Interaction and launched the Teneo Platform, which allows people to hold humanlike, conversations with applications running on electronic devices. In 2013 Artificial Solutions launched Lyra, a mobile personal assistant that is able to operate and remember the context of the conversation across different platforms and operating systems. A new round of funding was announced in June 2013 to support expansion in the US market. Since then the company has continued to develop the Teneo Platform and patent technology in the conversational AI sector including a framework for enabling chatbots to interact easily with each other. In 2018, the company raised a total of 13.7 Euros in equity capital to help support global growth and to accelerate the expansion of Teneo. In 2019, Artificial Solutions Holding completed the Reverse Takeover of Indentive, enabling the business of Artificial Solutions Holding to be traded on Nasdaq First North. Lawrence Flynn remains the CEO. In November 2020, Per Ottosson took over as CEO. He is based at the company's headquarter in Stockholm together with the newly appointed CFO, Fredrik Törgren. In August of 2024, the company rebranded as Teneo.ai. == References ==
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You.com : You.com is an AI assistant that began as a personalization-focused search engine. While still offering web search capabilities, You.com has evolved to prioritize a chat-first AI assistant. The company was founded in 2020 by Richard Socher, the former Chief Scientist at Salesforce and third most-cited researcher in Natural Language Processing with over 175,000 citations, and Bryan McCann, a former Lead Research Scientist in NLP at Salesforce. Socher is CEO and McCann CTO. In December 2022, You.com was the first search engine to integrate a consumer-facing Large Language Model (LLM) with real-time internet access for up-to-date responses with citations. In February 2023, it was the first to introduce multimodal AI chat capabilities, providing users with various types of responses, including visual elements like stock charts. In 2023, Time named Socher to the "TIME100 AI", recognizing "the most influential people in AI". In an interview with Time, Socher expressed You.com's goal of enhancing user productivity and access to information, stating, "to give people answers more quickly, make them more productive, efficient, more well-informed, with better privacy."
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You.com : Following its 2020 founding, You.com opened its public beta on November 9, 2021, and received $20 million in funding led by Salesforce founder and CEO Marc Benioff. Other investors include Breyer Capital, Sound Ventures, and Day One Ventures. The domain You.com was initially purchased in 1996 by Benioff. Benioff invested in You.com and transferred ownership of the You.com domain name to the company. Benioff called You.com "the future of search" in a statement during its public beta launch and said, "We're at a critical inflection point in the internet's history, and we must take steps to restore trust online." In July 2022, You.com announced its $25 million Series A funding round led by Radical Ventures with participation from Marc Benioff's Time Ventures, Breyer Capital, Norwest Venture Partners and Day One Ventures. By mid-December 2022, You.com shared that it had surpassed one million active users, and the number of searches grew by over 400% in six months.
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You.com : On December 23, 2022, You.com was the first search engine to launch a ChatGPT-style chatbot with live web results alongside its responses. Initially known as YouChat, the chatbot was primarily based on the GPT-3.5 large language model and could answer questions, suggest ideas, translate text, summarize articles, compose emails, and write code snippets, while staying up-to-date with current events and citing sources. Several further versions of YouChat were released. The second version, called YouChat 2.0, was released on February 7, 2023, incorporated improved conversational AI and community-built applications by blending a large language model named C-A-L (Chat, Apps, and Links). This update enabled YouChat to provide results in various formats, such as charts, photos, videos, tables, graphs, text or code, so users can find answers without leaving the search results page. YouChat 3.0, unveiled on May 4, 2023, combined chat functionality with results from Reddit, TikTok, Stack Overflow and Wikipedia.
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You.com : In its review of You.com's YouPro service, ZDNet highlighted its cost-effectiveness for accessing diverse large language models from leading tech companies. It praised YouPro for offering unique features such as comprehensive internet access and a Custom Model Selector, enhancing the AI chat experience. ZDNet recommended YouPro for individuals looking to explore advanced AI capabilities affordably, though it noted experiences might vary compared to using models on their native platforms. You.com was named one of Time's "The Best Inventions of 2022."
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You.com : Official website
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Binary classification : Binary classification is the task of classifying the elements of a set into one of two groups (each called class). Typical binary classification problems include: Medical testing to determine if a patient has a certain disease or not; Quality control in industry, deciding whether a specification has been met; In information retrieval, deciding whether a page should be in the result set of a search or not In administration, deciding whether someone should be issued with a driving licence or not In cognition, deciding whether an object is food or not food. When measuring the accuracy of a binary classifier, the simplest way is to count the errors. But in the real world often one of the two classes is more important, so that the number of both of the different types of errors is of interest. For example, in medical testing, detecting a disease when it is not present (a false positive) is considered differently from not detecting a disease when it is present (a false negative).
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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 negative assignments). These can be arranged into a 2×2 contingency table, with rows corresponding to actual value – condition positive or condition negative – and columns corresponding to classification value – test outcome positive or test outcome negative.
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Binary classification : From tallies of the four basic outcomes, there are many approaches that can be used to measure the accuracy of a classifier or predictor. Different fields have different preferences.
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Binary classification : Statistical classification is a problem studied in machine learning in which the classification is performed on the basis of a classification rule. It is a type of supervised learning, a method of machine learning where the categories are predefined, and is used to categorize new probabilistic observations into said categories. When there are only two categories the problem is known as statistical binary classification. Some of the methods commonly used for binary classification are: Decision trees Random forests Bayesian networks Support vector machines Neural networks Logistic regression Probit model Genetic Programming Multi expression programming Linear genetic programming Each classifier is best in only a select domain based upon the number of observations, the dimensionality of the feature vector, the noise in the data and many other factors. For example, random forests perform better than SVM classifiers for 3D point clouds.
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Binary classification : Binary classification may be a form of dichotomization in which a continuous function is transformed into a binary variable. Tests whose results are of continuous values, such as most blood values, can artificially be made binary by defining a cutoff value, with test results being designated as positive or negative depending on whether the resultant value is higher or lower than the cutoff. However, such conversion causes a loss of information, as the resultant binary classification does not tell how much above or below the cutoff a value is. As a result, when converting a continuous value that is close to the cutoff to a binary one, the resultant positive or negative predictive value is generally higher than the predictive value given directly from the continuous value. In such cases, the designation of the test of being either positive or negative gives the appearance of an inappropriately high certainty, while the value is in fact in an interval of uncertainty. For example, with the urine concentration of hCG as a continuous value, a urine pregnancy test that measured 52 mIU/ml of hCG may show as "positive" with 50 mIU/ml as cutoff, but is in fact in an interval of uncertainty, which may be apparent only by knowing the original continuous value. On the other hand, a test result very far from the cutoff generally has a resultant positive or negative predictive value that is lower than the predictive value given from the continuous value. For example, a urine hCG value of 200,000 mIU/ml confers a very high probability of pregnancy, but conversion to binary values results in that it shows just as "positive" as the one of 52 mIU/ml.
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Binary classification : Approximate membership query filter Examples of Bayesian inference Classification rule Confusion matrix Detection theory Kernel methods Multiclass classification Multi-label classification One-class classification Prosecutor's fallacy Receiver operating characteristic Thresholding (image processing) Uncertainty coefficient, aka proficiency Qualitative property Precision and recall (equivalent classification schema)
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Binary classification : Nello Cristianini and John Shawe-Taylor. An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, 2000. ISBN 0-521-78019-5 ([1] SVM Book) John Shawe-Taylor and Nello Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004. ISBN 0-521-81397-2 (Website for the book) Bernhard Schölkopf and A. J. Smola: Learning with Kernels. MIT Press, Cambridge, Massachusetts, 2002. ISBN 0-262-19475-9
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Data science : Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms and systems to extract or extrapolate knowledge from potentially noisy, structured, or unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge. A data scientist is a professional who creates programming code and combines it with statistical knowledge to summarize data.
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Data science : Data science is an interdisciplinary field focused on extracting knowledge from typically large data sets and applying the knowledge from that data to solve problems in other application domains. The field encompasses preparing data for analysis, formulating data science problems, analyzing data, and summarizing these findings. As such, it incorporates skills from computer science, mathematics, data visualization, graphic design, communication, and business. Vasant Dhar writes that statistics emphasizes quantitative data and description. In contrast, data science deals with quantitative and qualitative data (e.g., from images, text, sensors, transactions, customer information, etc.) and emphasizes prediction and action. Andrew Gelman of Columbia University has described statistics as a non-essential part of data science. Stanford professor David Donoho writes that data science is not distinguished from statistics by the size of datasets or use of computing and that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data-science program. He describes data science as an applied field growing out of traditional statistics.
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Data science : Data analysis typically involves working with structured datasets to answer specific questions or solve specific problems. This can involve tasks such as data cleaning and data visualization to summarize data and develop hypotheses about relationships between variables. Data analysts typically use statistical methods to test these hypotheses and draw conclusions from the data. Data science involves working with larger datasets that often require advanced computational and statistical methods to analyze. Data scientists often work with unstructured data such as text or images and use machine learning algorithms to build predictive models. Data science often uses statistical analysis, data preprocessing, and supervised learning.
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Data science : Cloud computing can offer access to large amounts of computational power and storage. In big data, where volumes of information are continually generated and processed, these platforms can be used to handle complex and resource-intensive analytical tasks. Some distributed computing frameworks are designed to handle big data workloads. These frameworks can enable data scientists to process and analyze large datasets in parallel, which can reduce processing times.
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Data science : Data science involves collecting, processing, and analyzing data which often includes personal and sensitive information. Ethical concerns include potential privacy violations, bias perpetuation, and negative societal impacts. Machine learning models can amplify existing biases present in training data, leading to discriminatory or unfair outcomes.
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Data science : Python (programming language) R (programming language) Data engineering Big data Machine learning Bioinformatics Astroinformatics Topological data analysis List of open-source data science software == References ==
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Intelligent automation : Intelligent automation (IA), or intelligent process automation, is a software term that refers to a combination of artificial intelligence (AI) and robotic process automation (RPA). Companies use intelligent automation to cut costs and streamline tasks by using artificial-intelligence-powered robotic software to mitigate repetitive tasks. As it accumulates data, the system learns in an effort to improve its efficiency. Intelligent automation applications consist of but are not limited to, pattern analysis, data assembly, and classification. The term is similar to hyperautomation, a concept identified by research group Gartner as being one of the top technology trends of 2020.
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Intelligent automation : Intelligent automation applies the assembly line concept of breaking tasks into repetitive steps to improve business processes. Rather than having humans do each step, intelligent automation can replace steps with an intelligent software robot or bot, improving efficiency.
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Intelligent automation : The technology is used to process unstructured content. Common real-world applications include self-driving cars, self-checkouts at grocery stores, smart home assistants, and appliances. Businesses can apply data and machine learning to build predictive analytics that react to consumer behavior changes, or to implement RPA to improve manufacturing floor operations. For example, the technology has also been used to automate the workflow behind distributing Covid-19 vaccines. Data provided by hospital systems’ electronic health records can be processed to identify and educate patients, and schedule vaccinations. Intelligent Automation can provide real-time insights on profitability and efficiency. However in an April 2022 survey by Alchemmy, despite three quarters of businesses acknowledging the importance of Artificial Intelligence to their future development, just a quarter of business leaders (25%) considered Intelligent Automation a “game changer” in understanding current performance. 42% of CTOs see “shortage of talent” as the main obstacle to implementing Intelligent Automation in their business, while 36% of CEOs see ‘upskilling and professional development of existing workforce’ as the most significant adoption barrier. IA is becoming increasingly accessible for firms of all sizes. With this in mind, it is expected to continue to grow rapidly in all industries. This technology has the potential to change the workforce. As it advances, it will be able to perform increasingly complex and difficult tasks. In addition, this may expose certain workforce issues as well as change how tasks are allocated.
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Intelligent automation : Streamline Processes Repetitive manual tasks can put a strain on the workforce, these tasks can be automated to allow the workforce to work on more important matters that require human cognition. Intelligent automation can also be used to mitigate tasks with human error which in turn increases proficiency. This allows the opportunity for firms to scale production without the traditional negative consequences such as reduced quality or increased risk. Customer Service Improvement Customers service can be improved drastically, this allows for a competitive advantage for the firm. IA utilizing chat features allows for instant curated responses to customers. In addition, it can give updates to customers, make appointments, manage calls, and personalize campaigns. Flexibility Due to the wide range of applications, IA is useful across a variety of fields, technologies, projects and industries. In addition, IA can be integrated with current automated systems in place. This allows for optimized systems unique to each firm to best fit their individual needs.
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Intelligent automation : Cognitive automation: Employs AI techniques to assist humans in decision-making and task completion Natural language processing: Allows computers to automate knowledge work Business process management: Enhances the consistency and agility of corporate operations Process mining: Applies data mining methods to discover, analyze, and improve business processes Intelligent document processing: Utilizes OCR and other advanced technologies to extract data from documents and convert it into structured, usable data Computer vision: Allows computers to extract information from digital images, videos, and other visual inputs Integration automation: Establishes a unified platform with automated workflows that integrate data, applications, and devices.
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Intelligent automation : Robotic process automation Artificial intelligence Automation == References ==
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Textual entailment : In natural language processing, textual entailment (TE), also known as natural language inference (NLI), is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text.
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Textual entailment : In the TE framework, the entailing and entailed texts are termed text (t) and hypothesis (h), respectively. Textual entailment is not the same as pure logical entailment – it has a more relaxed definition: "t entails h" (t ⇒ h) if, typically, a human reading t would infer that h is most likely true. (Alternatively: t ⇒ h if and only if, typically, a human reading t would be justified in inferring the proposition expressed by h from the proposition expressed by t.) The relation is directional because even if "t entails h", the reverse "h entails t" is much less certain. Determining whether this relationship holds is an informal task, one which sometimes overlaps with the formal tasks of formal semantics (satisfying a strict condition will usually imply satisfaction of a less strict conditioned); additionally, textual entailment partially subsumes word entailment.
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Textual entailment : Textual entailment can be illustrated with examples of three different relations: An example of a positive TE (text entails hypothesis) is: text: If you help the needy, God will reward you. hypothesis: Giving money to a poor man has good consequences. An example of a negative TE (text contradicts hypothesis) is: text: If you help the needy, God will reward you. hypothesis: Giving money to a poor man has no consequences. An example of a non-TE (text does not entail nor contradict) is: text: If you help the needy, God will reward you. hypothesis: Giving money to a poor man will make you a better person.
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Textual entailment : A characteristic of natural language is that there are many different ways to state what one wants to say: several meanings can be contained in a single text and the same meaning can be expressed by different texts. This variability of semantic expression can be seen as the dual problem of language ambiguity. Together, they result in a many-to-many mapping between language expressions and meanings. The task of paraphrasing involves recognizing when two texts have the same meaning and creating a similar or shorter text that conveys almost the same information. Textual entailment is similar but weakens the relationship to be unidirectional. Mathematical solutions to establish textual entailment can be based on the directional property of this relation, by making a comparison between some directional similarities of the texts involved.
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Textual entailment : Textual entailment measures natural language understanding as it asks for a semantic interpretation of the text, and due to its generality remains an active area of research. Many approaches and refinements of approaches have been considered, such as word embedding, logical models, graphical models, rule systems, contextual focusing, and machine learning. Practical or large-scale solutions avoid these complex methods and instead use only surface syntax or lexical relationships, but are correspondingly less accurate. As of 2005, state-of-the-art systems are far from human performance; a study found humans to agree on the dataset 95.25% of the time. Algorithms from 2016 had not yet achieved 90%.
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Textual entailment : Many natural language processing applications, like question answering, information extraction, summarization, multi-document summarization, and evaluation of machine translation systems, need to recognize that a particular target meaning can be inferred from different text variants. Typically entailment is used as part of a larger system, for example in a prediction system to filter out trivial or obvious predictions. Textual entailment also has applications in adversarial stylometry, which has the objective of removing textual style without changing the overall meaning of communication.
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Textual entailment : Some of available English NLI datasets include: SNLI MultiNLI SciTail SICK MedNLI QA-NLI In addition, there are several non-English NLI datasets, as follows: XNLI DACCORD, RTE3-FR, SICK-FR for French FarsTail for Farsi OCNLI for Chinese SICK-NL for Dutch IndoNLI for Indonesian
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Textual entailment : Entailment (linguistics) Inference engine Semantic reasoner Fuzzy logic
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Textual entailment : Potthast, Martin; Hagen, Matthias; Stein, Benno (2016). Author Obfuscation: Attacking the State of the Art in Authorship Verification (PDF). Conference and Labs of the Evaluation Forum.
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Textual entailment : Textual Entailment Resource Pool
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Extreme learning machine : Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need to be tuned. These hidden nodes can be randomly assigned and never updated (i.e. they are random projection but with nonlinear transforms), or can be inherited from their ancestors without being changed. In most cases, the output weights of hidden nodes are usually learned in a single step, which essentially amounts to learning a linear model. The name "extreme learning machine" (ELM) was given to such models by Guang-Bin Huang who originally proposed for the networks with any type of nonlinear piecewise continuous hidden nodes including biological neurons and different type of mathematical basis functions. The idea for artificial neural networks goes back to Frank Rosenblatt, who not only published a single layer Perceptron in 1958, but also introduced a multilayer perceptron with 3 layers: an input layer, a hidden layer with randomized weights that did not learn, and a learning output layer. According to some researchers, these models are able to produce good generalization performance and learn thousands of times faster than networks trained using backpropagation. In literature, it also shows that these models can outperform support vector machines in both classification and regression applications.
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Extreme learning machine : From 2001-2010, ELM research mainly focused on the unified learning framework for "generalized" single-hidden layer feedforward neural networks (SLFNs), including but not limited to sigmoid networks, RBF networks, threshold networks, trigonometric networks, fuzzy inference systems, Fourier series, Laplacian transform, wavelet networks, etc. One significant achievement made in those years is to successfully prove the universal approximation and classification capabilities of ELM in theory. From 2010 to 2015, ELM research extended to the unified learning framework for kernel learning, SVM and a few typical feature learning methods such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF). It is shown that SVM actually provides suboptimal solutions compared to ELM, and ELM can provide the whitebox kernel mapping, which is implemented by ELM random feature mapping, instead of the blackbox kernel used in SVM. PCA and NMF can be considered as special cases where linear hidden nodes are used in ELM. From 2015 to 2017, an increased focus has been placed on hierarchical implementations of ELM. Additionally since 2011, significant biological studies have been made that support certain ELM theories. From 2017 onwards, to overcome low-convergence problem during training LU decomposition, Hessenberg decomposition and QR decomposition based approaches with regularization have begun to attract attention In 2017, Google Scholar Blog published a list of "Classic Papers: Articles That Have Stood The Test of Time". Among these are two papers written about ELM which are shown in studies 2 and 7 from the "List of 10 classic AI papers from 2006".
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Extreme learning machine : Given a single hidden layer of ELM, suppose that the output function of the i -th hidden node is h i ( x ) = G ( a i , b i , x ) (\mathbf )=G(\mathbf _,b_,\mathbf ) , where a i _ and b i are the parameters of the i -th hidden node. The output function of the ELM for single hidden layer feedforward networks (SLFN) with L hidden nodes is: f L ( x ) = ∑ i = 1 L β i h i ( x ) ()=\sum _^_h_() , where β i _ is the output weight of the i -th hidden node. h ( x ) = [ h i ( x ) , . . . , h L ( x ) ] (\mathbf )=[h_(\mathbf ),...,h_(\mathbf )] is the hidden layer output mapping of ELM. Given N training samples, the hidden layer output matrix H of ELM is given as: H = [ h ( x 1 ) ⋮ h ( x N ) ] = [ G ( a 1 , b 1 , x 1 ) ⋯ G ( a L , b L , x 1 ) ⋮ ⋮ ⋮ G ( a 1 , b 1 , x N ) ⋯ G ( a L , b L , x N ) ] =\left[(_)\\\vdots \\(_)\end\right]=\left[G(_,b_,_)&\cdots &G(_,b_,_)\\\vdots &\vdots &\vdots \\G(_,b_,_)&\cdots &G(_,b_,_)\end\right] and T is the training data target matrix: T = [ t 1 ⋮ t N ] =\left[_\\\vdots \\_\end\right] Generally speaking, ELM is a kind of regularization neural networks but with non-tuned hidden layer mappings (formed by either random hidden nodes, kernels or other implementations), its objective function is: Minimize: ‖ β ‖ p σ 1 + C ‖ H β − T ‖ q σ 2 \|\|_^+C\|-\|_^ where σ 1 > 0 , σ 2 > 0 , p , q = 0 , 1 2 , 1 , 2 , ⋯ , + ∞ >0,\sigma _>0,p,q=0,,1,2,\cdots ,+\infty . Different combinations of σ 1 , σ 2 , p and q can be used and result in different learning algorithms for regression, classification, sparse coding, compression, feature learning and clustering. As a special case, a simplest ELM training algorithm learns a model of the form (for single hidden layer sigmoid neural networks): Y ^ = W 2 σ ( W 1 x ) =\mathbf _\sigma (\mathbf _x) where W1 is the matrix of input-to-hidden-layer weights, σ is an activation function, and W2 is the matrix of hidden-to-output-layer weights. The algorithm proceeds as follows: Fill W1 with random values (e.g., Gaussian random noise); estimate W2 by least-squares fit to a matrix of response variables Y, computed using the pseudoinverse ⋅+, given a design matrix X: W 2 = σ ( W 1 X ) + Y _=\sigma (\mathbf _\mathbf )^\mathbf
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Extreme learning machine : In most cases, ELM is used as a single hidden layer feedforward network (SLFN) including but not limited to sigmoid networks, RBF networks, threshold networks, fuzzy inference networks, complex neural networks, wavelet networks, Fourier transform, Laplacian transform, etc. Due to its different learning algorithm implementations for regression, classification, sparse coding, compression, feature learning and clustering, multi ELMs have been used to form multi hidden layer networks, deep learning or hierarchical networks. A hidden node in ELM is a computational element, which need not be considered as classical neuron. A hidden node in ELM can be classical artificial neurons, basis functions, or a subnetwork formed by some hidden nodes.
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Extreme learning machine : Both universal approximation and classification capabilities have been proved for ELM in literature. Especially, Guang-Bin Huang and his team spent almost seven years (2001-2008) on the rigorous proofs of ELM's universal approximation capability.
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Extreme learning machine : A wide range of nonlinear piecewise continuous functions G ( a , b , x ) ,b,\mathbf ) can be used in hidden neurons of ELM, for example:
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Extreme learning machine : The black-box character of neural networks in general and extreme learning machines (ELM) in particular is one of the major concerns that repels engineers from application in unsafe automation tasks. This particular issue was approached by means of several different techniques. One approach is to reduce the dependence on the random input. Another approach focuses on the incorporation of continuous constraints into the learning process of ELMs which are derived from prior knowledge about the specific task. This is reasonable, because machine learning solutions have to guarantee a safe operation in many application domains. The mentioned studies revealed that the special form of ELMs, with its functional separation and the linear read-out weights, is particularly well suited for the efficient incorporation of continuous constraints in predefined regions of the input space.
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Extreme learning machine : There are two main complaints from academic community concerning this work, the first one is about "reinventing and ignoring previous ideas", the second one is about "improper naming and popularizing", as shown in some debates in 2008 and 2015. In particular, it was pointed out in a letter to the editor of IEEE Transactions on Neural Networks that the idea of using a hidden layer connected to the inputs by random untrained weights was already suggested in the original papers on RBF networks in the late 1980s; Guang-Bin Huang replied by pointing out subtle differences. In a 2015 paper, Huang responded to complaints about his invention of the name ELM for already-existing methods, complaining of "very negative and unhelpful comments on ELM in neither academic nor professional manner due to various reasons and intentions" and an "irresponsible anonymous attack which intends to destroy harmony research environment", arguing that his work "provides a unifying learning platform" for various types of neural nets, including hierarchical structured ELM. In 2015, Huang also gave a formal rebuttal to what he considered as "malign and attack." Recent research replaces the random weights with constrained random weights.
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Extreme learning machine : Matlab Library Python Library
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Extreme learning machine : Reservoir computing Random projection Random matrix == References ==
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Bigram : A bigram or digram is a sequence of two adjacent elements from a string of tokens, which are typically letters, syllables, or words. A bigram is an n-gram for n=2. The frequency distribution of every bigram in a string is commonly used for simple statistical analysis of text in many applications, including in computational linguistics, cryptography, and speech recognition. Gappy bigrams or skipping bigrams are word pairs which allow gaps (perhaps avoiding connecting words, or allowing some simulation of dependencies, as in a dependency grammar).
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Bigram : Bigrams, along with other n-grams, are used in most successful language models for speech recognition. Bigram frequency attacks can be used in cryptography to solve cryptograms. See frequency analysis. Bigram frequency is one approach to statistical language identification. Some activities in logology or recreational linguistics involve bigrams. These include attempts to find English words beginning with every possible bigram, or words containing a string of repeated bigrams, such as logogogue.
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Bigram : The frequency of the most common letter bigrams in a large English corpus is: th 3.56% of 1.17% io 0.83% he 3.07% ed 1.17% le 0.83% in 2.43% is 1.13% ve 0.83% er 2.05% it 1.12% co 0.79% an 1.99% al 1.09% me 0.79% re 1.85% ar 1.07% de 0.76% on 1.76% st 1.05% hi 0.76% at 1.49% to 1.05% ri 0.73% en 1.45% nt 1.04% ro 0.73% nd 1.35% ng 0.95% ic 0.70% ti 1.34% se 0.93% ne 0.69% es 1.34% ha 0.93% ea 0.69% or 1.28% as 0.87% ra 0.69% te 1.20% ou 0.87% ce 0.65%
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Bigram : Digraph (orthography) Letter frequency Sørensen–Dice coefficient == References ==
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Prior knowledge for pattern recognition : Pattern recognition is a very active field of research intimately bound to machine learning. Also known as classification or statistical classification, pattern recognition aims at building a classifier that can determine the class of an input pattern. This procedure, known as training, corresponds to learning an unknown decision function based only on a set of input-output pairs ( x i , y i ) _,y_) that form the training data (or training set). Nonetheless, in real world applications such as character recognition, a certain amount of information on the problem is usually known beforehand. The incorporation of this prior knowledge into the training is the key element that will allow an increase of performance in many applications.
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Prior knowledge for pattern recognition : Prior knowledge refers to all information about the problem available in addition to the training data. However, in this most general form, determining a model from a finite set of samples without prior knowledge is an ill-posed problem, in the sense that a unique model may not exist. Many classifiers incorporate the general smoothness assumption that a test pattern similar to one of the training samples tends to be assigned to the same class. The importance of prior knowledge in machine learning is suggested by its role in search and optimization. Loosely, the no free lunch theorem states that all search algorithms have the same average performance over all problems, and thus implies that to gain in performance on a certain application one must use a specialized algorithm that includes some prior knowledge about the problem. The different types of prior knowledge encountered in pattern recognition are now regrouped under two main categories: class-invariance and knowledge on the data.
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Prior knowledge for pattern recognition : A very common type of prior knowledge in pattern recognition is the invariance of the class (or the output of the classifier) to a transformation of the input pattern. This type of knowledge is referred to as transformation-invariance. The mostly used transformations used in image recognition are: translation; rotation; skewing; scaling. Incorporating the invariance to a transformation T θ : x ↦ T θ x :\mapsto T_ parametrized in θ into a classifier of output f ( x ) ) for an input pattern x corresponds to enforcing the equality f ( x ) = f ( T θ x ) , ∀ x , θ . )=f(T_),\quad \forall ,\theta . Local invariance can also be considered for a transformation centered at θ = 0 , so that T 0 x = x = , by using the constraint ∂ ∂ θ | θ = 0 f ( T θ x ) = 0. \right|_f(T_)=0. The function f in these equations can be either the decision function of the classifier or its real-valued output. Another approach is to consider class-invariance with respect to a "domain of the input space" instead of a transformation. In this case, the problem becomes finding f so that f ( x ) = y P , ∀ x ∈ P , )=y_,\ \forall \in , where y P is the membership class of the region P of the input space. A different type of class-invariance found in pattern recognition is permutation-invariance, i.e. invariance of the class to a permutation of elements in a structured input. A typical application of this type of prior knowledge is a classifier invariant to permutations of rows of the matrix inputs.
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Prior knowledge for pattern recognition : Other forms of prior knowledge than class-invariance concern the data more specifically and are thus of particular interest for real-world applications. The three particular cases that most often occur when gathering data are: Unlabeled samples are available with supposed class-memberships; Imbalance of the training set due to a high proportion of samples of a class; Quality of the data may vary from a sample to another. Prior knowledge of these can enhance the quality of the recognition if included in the learning. Moreover, not taking into account the poor quality of some data or a large imbalance between the classes can mislead the decision of a classifier.
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Prior knowledge for pattern recognition : E. Krupka and N. Tishby, "Incorporating Prior Knowledge on Features into Learning", Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS 07)
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IBM Granite : IBM Granite is a series of decoder-only AI foundation models created by IBM. It was announced on September 7, 2023, and an initial paper was published 4 days later. Initially intended for use in the IBM's cloud-based data and generative AI platform Watsonx along with other models, IBM opened the source code of some code models. Granite models are trained on datasets curated from Internet, academic publishings, code datasets, legal and finance documents.
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IBM Granite : A foundation model is an AI model trained on broad data at scale such that it can be adapted to a wide range of downstream tasks. Granite's first foundation models were Granite.13b.instruct and Granite.13b.chat. The "13b" in their name comes from 13 billion, the amount of parameters they have as models, lesser than most of the larger models of the time. Later models vary from 3 to 34 billion parameters. On May 6, 2024, IBM released the source code of four variations of Granite Code Models under Apache 2, an open source permissive license that allows completely free use, modification and sharing of the software, and put them on Hugging Face for public use. According to IBM's own report, Granite 8b outperforms Llama 3 on several coding related tasks within similar range of parameters.
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IBM Granite : Mistral AI, a company that also provides open source models GPT LLaMA Cyc Gemini
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IBM Granite : GitHub page IBM Granite Playground
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GeneRIF : A GeneRIF or Gene Reference Into Function is a short (255 characters or fewer) statement about the function of a gene. GeneRIFs provide a simple mechanism for allowing scientists to add to the functional annotation of genes described in the Entrez Gene database. In practice, function is constructed quite broadly. For example, there are GeneRIFs that discuss the role of a gene in a disease, GeneRIFs that point the viewer towards a review article about the gene, and GeneRIFs that discuss the structure of a gene. However, the stated intent is for GeneRIFs to be about gene function. Currently over half a million geneRIFs have been created for genes from almost 1000 different species. GeneRIFs are always associated with specific entries in the Entrez Gene database. Each GeneRIF has a pointer to the PubMed ID (a type of document identifier) of a scientific publication that provides evidence for the statement made by the GeneRIF. GeneRIFs are often extracted directly from the document that is identified by the PubMed ID, very frequently from its title or from its final sentence. GeneRIFs are usually produced by NCBI indexers, but anyone may submit a GeneRIF. To be processed, a valid Gene ID must exist for the specific gene, or the Gene staff must have assigned an overall Gene ID to the species. The latter case is implemented via records in Gene with the symbol NEWENTRY. Once the Gene ID is identified, only three types of information are required to complete a submission: a concise phrase describing a function or functions (less than 255 characters in length, preferably more than a restatement of the title of the paper); a published paper describing that function, implemented by supplying the PubMed ID of a citation in PubMed; a valid e-mail address (which will remain confidential).
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GeneRIF : Here are some GeneRIFs taken from Entrez Gene for GeneID 7157, the human gene TP53. The PubMed document identifiers have been omitted from the examples. Note the wide variability with respect to the presence or absence of punctuation and of sentence-initial capital letters. p53 and c-erbB-2 may have independent role in carcinogenesis of gall bladder cancer Degradation of endogenous HIPK2 depends on the presence of a functional p53 protein. p53 codon 72 alleles influence the response to anticancer drugs in cells from aged people by regulating the cell cycle inhibitor p21WAF1 Logistic regression analysis showed p53 and COX-2 as dependent predictors in pancreatic carcinogenesis, and a reciprocal relationship to neoplastic progression between p53 and COX-2. GeneRIFs are an unusual type of textual genre, and they have recently been the subject of a number of articles from the natural language processing community.
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GeneRIF : NCBI's web page describing GeneRIFs Mitchell JA, Aronson AR, Mork JG, Folk LC, Humphrey SM, Ward JM (2003). "Gene indexing: characterization and analysis of NLM's GeneRIFs". AMIA Annu Symp Proc: 460–4. PMC 1480312. PMID 14728215.
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GeneRIF : William Hersh, Ravi Teja Bhupatiraju (2003). TREC Genomics Track Overview (PDF). Archived from the original (PDF) on 2005-05-12. Paper describing a Text Retrieval Conference "shared task" involving automatic prediction of GeneRIFs. Lu, Zhiyong; K. Bretonnel Cohen; Lawrence Hunter (2006). Finding GeneRIFs via Gene Ontology annotations (PDF). Proc. Pacific Symposium on Biocomputing 2006. pp. 52–63. Archived from the original (PDF) on 2006-02-13. Lu et al.'s paper describing a system that automatically suggests GeneRIFs.
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Multimodal sentiment analysis : Multimodal sentiment analysis is a technology for traditional text-based sentiment analysis, which includes modalities such as audio and visual data. It can be bimodal, which includes different combinations of two modalities, or trimodal, which incorporates three modalities. With the extensive amount of social media data available online in different forms such as videos and images, the conventional text-based sentiment analysis has evolved into more complex models of multimodal sentiment analysis, which can be applied in the development of virtual assistants, analysis of YouTube movie reviews, analysis of news videos, and emotion recognition (sometimes known as emotion detection) such as depression monitoring, among others. Similar to the traditional sentiment analysis, one of the most basic task in multimodal sentiment analysis is sentiment classification, which classifies different sentiments into categories such as positive, negative, or neutral. The complexity of analyzing text, audio, and visual features to perform such a task requires the application of different fusion techniques, such as feature-level, decision-level, and hybrid fusion. The performance of these fusion techniques and the classification algorithms applied, are influenced by the type of textual, audio, and visual features employed in the analysis.
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Multimodal sentiment analysis : Feature engineering, which involves the selection of features that are fed into machine learning algorithms, plays a key role in the sentiment classification performance. In multimodal sentiment analysis, a combination of different textual, audio, and visual features are employed.
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Multimodal sentiment analysis : Unlike the traditional text-based sentiment analysis, multimodal sentiment analysis undergo a fusion process in which data from different modalities (text, audio, or visual) are fused and analyzed together. The existing approaches in multimodal sentiment analysis data fusion can be grouped into three main categories: feature-level, decision-level, and hybrid fusion, and the performance of the sentiment classification depends on which type of fusion technique is employed.
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Multimodal sentiment analysis : Similar to text-based sentiment analysis, multimodal sentiment analysis can be applied in the development of different forms of recommender systems such as in the analysis of user-generated videos of movie reviews and general product reviews, to predict the sentiments of customers, and subsequently create product or service recommendations. Multimodal sentiment analysis also plays an important role in the advancement of virtual assistants through the application of natural language processing (NLP) and machine learning techniques. In the healthcare domain, multimodal sentiment analysis can be utilized to detect certain medical conditions such as stress, anxiety, or depression. Multimodal sentiment analysis can also be applied in understanding the sentiments contained in video news programs, which is considered as a complicated and challenging domain, as sentiments expressed by reporters tend to be less obvious or neutral. == References ==
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MMLU : In artificial intelligence, Measuring Massive Multitask Language Understanding (MMLU) is a benchmark for evaluating the capabilities of large language models.
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MMLU : The MMLU consists of about 16,000 multiple-choice questions spanning 57 academic subjects including mathematics, philosophy, law, and medicine. It is one of the most commonly used benchmarks for comparing the capabilities of large language models, with over 100 million downloads as of July 2024. The MMLU was released by Dan Hendrycks and a team of researchers in 2020 and was designed to be more challenging than then-existing benchmarks such as General Language Understanding Evaluation (GLUE) on which new language models were achieving better-than-human accuracy. At the time of the MMLU's release, most existing language models performed around the level of random chance (25%), with the best performing GPT-3 model achieving 43.9% accuracy. The developers of the MMLU estimate that human domain-experts achieve around 89.8% accuracy. As of 2024, some of the most powerful language models, such as o1, Gemini and Claude 3, were reported to achieve scores around 90%. An expert review of 5,700 of the questions, spanning all 57 MMLU subjects, estimated that there were errors with 6.5% of the questions in the MMLU question set, which suggests that the maximum attainable score in MMLU is significantly below 100%.
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MMLU : The following examples are taken from the "Abstract Algebra" and "International Law" tasks, respectively. The correct answers are marked in boldface: Find all c in Z 3 _ such that Z 3 [ x ] / ( x 2 + c ) _[x]/(x^+c) is a field. (A) 0 (B) 1 (C) 2 (D) 3 Would a reservation to the definition of torture in the ICCPR be acceptable in contemporary practice? (A) This is an acceptable reservation if the reserving country’s legislation employs a different definition (B) This is an unacceptable reservation because it contravenes the object and purpose of the ICCPR (C) This is an unacceptable reservation because the definition of torture in the ICCPR is consistent with customary international law (D) This is an acceptable reservation because under general international law States have the right to enter reservations to treaties
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MMLU : == References ==
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Universal portfolio algorithm : The universal portfolio algorithm is a portfolio selection algorithm from the field of machine learning and information theory. The algorithm learns adaptively from historical data and maximizes the log-optimal growth rate in the long run. It was introduced by the late Stanford University information theorist Thomas M. Cover. The algorithm rebalances the portfolio at the beginning of each trading period. At the beginning of the first trading period it starts with a naive diversification. In the following trading periods the portfolio composition depends on the historical total return of all possible constant-rebalanced portfolios. == References ==
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MobileNet : MobileNet is a family of convolutional neural network (CNN) architectures designed for image classification, object detection, and other computer vision tasks. They are designed for small size, low latency, and low power consumption, making them suitable for on-device inference and edge computing on resource-constrained devices like mobile phones and embedded systems. They were originally designed to be run efficiently on mobile devices with TensorFlow Lite. The need for efficient deep learning models on mobile devices led researchers at Google to develop MobileNet. As of October 2024, the family has four versions, each improving upon the previous one in terms of performance and efficiency.
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MobileNet : Convolutional neural network Deep learning TensorFlow Lite
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MobileNet : "models/research/slim/nets/mobilenet at master · tensorflow/models". GitHub. Retrieved 2024-10-18. "Keras documentation: MobileNet, MobileNetV2, and MobileNetV3". Keras. Retrieved October 18, 2024. == References ==
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Normalization (machine learning) : In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization and activation normalization. Data normalization (or feature scaling) includes methods that rescale input data so that the features have the same range, mean, variance, or other statistical properties. For instance, a popular choice of feature scaling method is min-max normalization, where each feature is transformed to have the same range (typically [ 0 , 1 ] or [ − 1 , 1 ] ). This solves the problem of different features having vastly different scales, for example if one feature is measured in kilometers and another in nanometers. Activation normalization, on the other hand, is specific to deep learning, and includes methods that rescale the activation of hidden neurons inside neural networks. Normalization is often used to: increase the speed of training convergence, reduce sensitivity to variations and feature scales in input data, reduce overfitting, and produce better model generalization to unseen data. Normalization techniques are often theoretically justified as reducing covariance shift, smoothing optimization landscapes, and increasing regularization, though they are mainly justified by empirical success.
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Normalization (machine learning) : Batch normalization (BatchNorm) operates on the activations of a layer for each mini-batch. Consider a simple feedforward network, defined by chaining together modules: x ( 0 ) ↦ x ( 1 ) ↦ x ( 2 ) ↦ ⋯ \mapsto x^\mapsto x^\mapsto \cdots where each network module can be a linear transform, a nonlinear activation function, a convolution, etc. x ( 0 ) is the input vector, x ( 1 ) is the output vector from the first module, etc. BatchNorm is a module that can be inserted at any point in the feedforward network. For example, suppose it is inserted just after x ( l ) , then the network would operate accordingly: ⋯ ↦ x ( l ) ↦ B N ( x ( l ) ) ↦ x ( l + 1 ) ↦ ⋯ \mapsto \mathrm (x^)\mapsto x^\mapsto \cdots The BatchNorm module does not operate over individual inputs. Instead, it must operate over one batch of inputs at a time. Concretely, suppose we have a batch of inputs x ( 1 ) ( 0 ) , x ( 2 ) ( 0 ) , … , x ( B ) ( 0 ) ^,x_^,\dots ,x_^ , fed all at once into the network. We would obtain in the middle of the network some vectors: x ( 1 ) ( l ) , x ( 2 ) ( l ) , … , x ( B ) ( l ) ^,x_^,\dots ,x_^ The BatchNorm module computes the coordinate-wise mean and variance of these vectors: μ i ( l ) = 1 B ∑ b = 1 B x ( b ) , i ( l ) ( σ i ( l ) ) 2 = 1 B ∑ b = 1 B ( x ( b ) , i ( l ) − μ i ( l ) ) 2 \mu _^&=\sum _^x_^\\(\sigma _^)^&=\sum _^(x_^-\mu _^)^\end where i indexes the coordinates of the vectors, and b indexes the elements of the batch. In other words, we are considering the i -th coordinate of each vector in the batch, and computing the mean and variance of these numbers. It then normalizes each coordinate to have zero mean and unit variance: x ^ ( b ) , i ( l ) = x ( b ) , i ( l ) − μ i ( l ) ( σ i ( l ) ) 2 + ϵ _^=^-\mu _^^)^+\epsilon The ϵ is a small positive constant such as 10 − 9 added to the variance for numerical stability, to avoid division by zero. Finally, it applies a linear transformation: y ( b ) , i ( l ) = γ i x ^ ( b ) , i ( l ) + β i ^=\gamma __^+\beta _ Here, γ and β are parameters inside the BatchNorm module. They are learnable parameters, typically trained by gradient descent. The following is a Python implementation of BatchNorm:
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Normalization (machine learning) : Layer normalization (LayerNorm) is a popular alternative to BatchNorm. Unlike BatchNorm, which normalizes activations across the batch dimension for a given feature, LayerNorm normalizes across all the features within a single data sample. Compared to BatchNorm, LayerNorm's performance is not affected by batch size. It is a key component of transformer models. For a given data input and layer, LayerNorm computes the mean μ and variance σ 2 over all the neurons in the layer. Similar to BatchNorm, learnable parameters γ (scale) and β (shift) are applied. It is defined by: x i ^ = x i − μ σ 2 + ϵ , y i = γ i x i ^ + β i =-\mu +\epsilon ,\quad y_=\gamma _+\beta _ where: μ = 1 D ∑ i = 1 D x i , σ 2 = 1 D ∑ i = 1 D ( x i − μ ) 2 \sum _^x_,\quad \sigma ^=\sum _^(x_-\mu )^ and the index i ranges over the neurons in that layer.
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Normalization (machine learning) : Weight normalization (WeightNorm) is a technique inspired by BatchNorm that normalizes weight matrices in a neural network, rather than its activations. One example is spectral normalization, which divides weight matrices by their spectral norm. The spectral normalization is used in generative adversarial networks (GANs) such as the Wasserstein GAN. The spectral radius can be efficiently computed by the following algorithm: INPUT matrix W and initial guess x Iterate x ↦ 1 ‖ W x ‖ 2 W x Wx to convergence x ∗ . This is the eigenvector of W with eigenvalue ‖ W ‖ s . RETURN x ∗ , ‖ W x ∗ ‖ 2 ,\|Wx^\|_ By reassigning W i ← W i ‖ W i ‖ s \leftarrow \|_ after each update of the discriminator, we can upper-bound ‖ W i ‖ s ≤ 1 \|_\leq 1 , and thus upper-bound ‖ D ‖ L . The algorithm can be further accelerated by memoization: at step t , store x i ∗ ( t ) ^(t) . Then, at step t + 1 , use x i ∗ ( t ) ^(t) as the initial guess for the algorithm. Since W i ( t + 1 ) (t+1) is very close to W i ( t ) (t) , so is x i ∗ ( t ) ^(t) to x i ∗ ( t + 1 ) ^(t+1) , thus allowing rapid convergence.
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Normalization (machine learning) : There are some activation normalization techniques that are only used for CNNs.
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Normalization (machine learning) : Some normalization methods were designed for use in transformers. The original 2017 transformer used the "post-LN" configuration for its LayerNorms. It was difficult to train, and required careful hyperparameter tuning and a "warm-up" in learning rate, where it starts small and gradually increases. The pre-LN convention, proposed several times in 2018, was found to be easier to train, requiring no warm-up, leading to faster convergence. FixNorm and ScaleNorm both normalize activation vectors in a transformer. The FixNorm method divides the output vectors from a transformer by their L2 norms, then multiplies by a learned parameter g . The ScaleNorm replaces all LayerNorms inside a transformer by division with L2 norm, then multiplying by a learned parameter g ′ (shared by all ScaleNorm modules of a transformer). Query-Key normalization (QKNorm) normalizes query and key vectors to have unit L2 norm. In nGPT, many vectors are normalized to have unit L2 norm: hidden state vectors, input and output embedding vectors, weight matrix columns, and query and key vectors.
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Normalization (machine learning) : Gradient normalization (GradNorm) normalizes gradient vectors during backpropagation.
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Normalization (machine learning) : Data preprocessing Feature scaling
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Normalization (machine learning) : "Normalization Layers". labml.ai Deep Learning Paper Implementations. Retrieved 2024-08-07.
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Robot learning : Robot learning is a research field at the intersection of machine learning and robotics. It studies techniques allowing a robot to acquire novel skills or adapt to its environment through learning algorithms. The embodiment of the robot, situated in a physical embedding, provides at the same time specific difficulties (e.g. high-dimensionality, real time constraints for collecting data and learning) and opportunities for guiding the learning process (e.g. sensorimotor synergies, motor primitives). Example of skills that are targeted by learning algorithms include sensorimotor skills such as locomotion, grasping, active object categorization, as well as interactive skills such as joint manipulation of an object with a human peer, and linguistic skills such as the grounded and situated meaning of human language. Learning can happen either through autonomous self-exploration or through guidance from a human teacher, like for example in robot learning by imitation. Robot learning can be closely related to adaptive control, reinforcement learning as well as developmental robotics which considers the problem of autonomous lifelong acquisition of repertoires of skills. While machine learning is frequently used by computer vision algorithms employed in the context of robotics, these applications are usually not referred to as "robot learning".
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Robot learning : Many research groups are developing techniques where robots learn by imitating. This includes various techniques for learning from demonstration (sometimes also referred to as "programming by demonstration") and observational learning.
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Robot learning : In Tellex's "Million Object Challenge," the goal is robots that learn how to spot and handle simple items and upload their data to the cloud to allow other robots to analyze and use the information. RoboBrain is a knowledge engine for robots which can be freely accessed by any device wishing to carry out a task. The database gathers new information about tasks as robots perform them, by searching the Internet, interpreting natural language text, images, and videos, object recognition as well as interaction. The project is led by Ashutosh Saxena at Stanford University. RoboEarth is a project that has been described as a "World Wide Web for robots" − it is a network and database repository where robots can share information and learn from each other and a cloud for outsourcing heavy computation tasks. The project brings together researchers from five major universities in Germany, the Netherlands and Spain and is backed by the European Union. Google Research, DeepMind, and Google X have decided to allow their robots share their experiences.
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Robot learning : Cognitive robotics – robot with processing architecture that will allow it to learnPages displaying wikidata descriptions as a fallback Developmental robotics Evolutionary robotics Philosophical ethology#History – Field of multidisciplinary research
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Robot learning : IEEE RAS Technical Committee on Robot Learning (official IEEE website) IEEE RAS Technical Committee on Robot Learning (TC members website) Robot Learning at the Max Planck Institute for Intelligent Systems and the Technical University Darmstadt Robot Learning at the Computational Learning and Motor Control lab Humanoid Robot Learning at the Advanced Telecommunication Research Center (ATR) (in English and Japanese) Learning Algorithms and Systems Laboratory at EPFL (LASA) Robot Learning at the Cognitive Robotics Lab of Juergen Schmidhuber at IDSIA and Technical University of Munich The Humanoid Project: Peter Nordin, Chalmers University of Technology Inria and Ensta ParisTech FLOWERS team, France: Autonomous lifelong learning in developmental robotics CITEC at University of Bielefeld, Germany Asada Laboratory, Department of Adaptive Machine Systems, Graduate School of Engineering, Osaka University, Japan The Laboratory for Perceptual Robotics, University of Massachusetts Amherst Amherst, USA Centre for Robotics and Neural Systems, Plymouth University Plymouth, United Kingdom Robot Learning Lab at Carnegie Mellon University Project Learning Humanoid Robots at University of Bonn Skilligent Robot Learning and Behavior Coordination System (commercial product) Robot Learning class at Cornell University Robot Learning and Interaction Lab at Italian Institute of Technology Reinforcement learning for robotics Archived 2018-10-08 at the Wayback Machine at Delft University of Technology
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Hierarchical Risk Parity : Hierarchical Risk Parity (HRP) is an advanced investment portfolio optimization framework developed in 2016 by Marcos López de Prado at Guggenheim Partners and Cornell University. HRP is a probabilistic graph-based alternative to the prevailing mean-variance optimization (MVO) framework developed by Harry Markowitz in 1952, and for which he received the Nobel Prize in economic sciences. HRP algorithms apply discrete mathematics and machine learning techniques to create diversified and robust investment portfolios that outperform MVO methods out-of-sample. HRP aims to address the limitations of traditional portfolio construction methods, particularly when dealing with highly correlated assets. Following its publication, HRP has been implemented in numerous open-source libraries, and received multiple extensions.
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Hierarchical Risk Parity : Algorithms within the HRP framework are characterized by the following features: Machine Learning Approach: HRP employs hierarchical clustering, a machine learning technique, to group similar assets based on their correlations. This allows the algorithm to identify the underlying hierarchical structure of the portfolio, and avoid that errors spread through the entire network. Risk-Based Allocation: The algorithm allocates capital based on risk, ensuring that assets only compete with similar assets for representation in the portfolio. This approach leads to better diversification across different risk sources, while avoiding the instability associated with noisy returns estimates. Covariance Matrix Handling: Unlike traditional methods like Mean-Variance Optimization, HRP does not require inverting the covariance matrix. This makes it more stable and applicable to portfolios with a large number of assets, particularly when the covariance matrix's condition number is high.
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Hierarchical Risk Parity : The HRP algorithm typically consists of three main steps: Hierarchical Clustering: Assets are grouped into clusters based on their correlations, forming a hierarchical tree structure. Quasi-Diagonalization: The correlation matrix is reordered based on the clustering results, revealing a block diagonal structure. Recursive Bisection: Weights are assigned to assets through a top-down approach, splitting the portfolio into smaller sub-portfolios and allocating capital based on inverse variance.
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Hierarchical Risk Parity : HRP algorithms offer several advantages over the (at the time) MVO state-of-the-art methods: Improved diversification: HRP creates portfolios that are well-diversified across different risk sources.[1] Robustness: The algorithm has shown to generate portfolios with robust out-of-sample properties. Flexibility: HRP can handle singular covariance matrices and incorporate various constraints. Intuitive approach: The clustering-based method provides an intuitive understanding of the portfolio structure.[2] By combining elements of machine learning, risk parity, and traditional portfolio theory, HRP offers a sophisticated approach to portfolio construction that aims to overcome the limitations of conventional methods. == References ==
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MeCab : MeCab is an open-source text segmentation library for Japanese written text. It was originally developed by the Nara Institute of Science and Technology and is maintained by Taku Kudou (工藤 拓) as part of his work on the Google Japanese Input project. The name derives from the developer's favorite food, mekabu (和布蕪), a Japanese dish made from wakame leaves. The software was originally based on ChaSen and was developed under the name ChaSenTNG, but now it is developed independently from ChaSen and was rewritten from scratch. MeCab's analysis accuracy is comparable to ChaSen, and it is about 3–4 times faster. MeCab analyzes and segments a sentence into its parts of speech. There are several dictionaries available for MeCab, but IPADIC is the most commonly used one as with ChaSen. In 2007, Google used MeCab to generate n-gram data for a large corpus of Japanese text, which it published on its Google Japan blog. MeCab is also used for Japanese input on Mac OS X 10.5 and 10.6, and in iOS since version 2.1.
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MeCab : Input: ウィキペディア（Ｗｉｋｉｐｅｄｉａ）は誰でも編集できるフリー百科事典です Results in: ウィキペディア 名詞,一般,*,*,*,*,* （ 記号,括弧開,*,*,*,*,（,（,（ Ｗｉｋｉｐｅｄｉａ 名詞,固有名詞,組織,*,*,*,* ） 記号,括弧閉,*,*,*,*,）,）,） は 助詞,係助詞,*,*,*,*,は,ハ,ワ 誰 名詞,代名詞,一般,*,*,*,誰,ダレ,ダレ でも 助詞,副助詞,*,*,*,*,でも,デモ,デモ 編集 名詞,サ変接続,*,*,*,*,編集,ヘンシュウ,ヘンシュー できる 動詞,自立,*,*,一段,基本形,できる,デキル,デキル フリー 名詞,一般,*,*,*,*,フリー,フリー,フリー 百科 名詞,一般,*,*,*,*,百科,ヒャッカ,ヒャッカ 事典 名詞,一般,*,*,*,*,事典,ジテン,ジテン です 助動詞,*,*,*,特殊・デス,基本形,です,デス,デス EOS Besides segmenting the text, MeCab also lists the part of speech of the word, and, if applicable and in the dictionary, its pronunciation. In the above example, the verb できる (dekiru, "to be able to") is classified as an ichidan (一段) verb (動詞) in the infinitive tense (基本形). The word でも (demo) is identified as an adverbial particle (副助詞). As not all columns apply to all words, when a column does not apply to a word, an asterisk is used; this makes it possible to format the information after the word and the tab character as the comma-separated values. MeCab also supports several output formats; one of which, chasen, outputs tab-separated values in a format that programs written for ChaSen can use. Another format, yomi (from 読む yomu, to read), outputs the pronunciation of the input text as katakana, as shown below. ウィキペディア（Ｗｉｋｉｐｅｄｉａ）ハダレデモヘンシュウデキルフリーヒャッカジテンデス
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