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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 inter... |
Computational semantics : Discourse representation theory Formal semantics (natural language) Minimal recursion semantics Natural-language understanding Semantic compression Semantic parsing Semantic Web SemEval WordNet |
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., Mus... |
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) |
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 execut... |
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 p... |
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 optimi... |
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 research... |
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. Beni... |
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 articl... |
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... |
You.com : Official website |
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... |
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 negat... |
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. |
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 o... |
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 ... |
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 process... |
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 Pres... |
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 domai... |
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 sum... |
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 statis... |
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 designe... |
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, ... |
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 == |
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 r... |
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. |
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... |
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 ... |
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 min... |
Intelligent automation : Robotic process automation Artificial intelligence Automation == References == |
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. |
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... |
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 h... |
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... |
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... |
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 enta... |
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 |
Textual entailment : Entailment (linguistics) Inference engine Semantic reasoner Fuzzy logic |
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. |
Textual entailment : Textual Entailment Resource Pool |
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 ... |
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 se... |
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... |
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 differen... |
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. |
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: |
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 i... |
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... |
Extreme learning machine : Matlab Library Python Library |
Extreme learning machine : Reservoir computing Random projection Random matrix == References == |
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 ... |
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 recre... |
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%... |
Bigram : Digraph (orthography) Letter frequency Sørensen–Dice coefficient == References == |
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 ... |
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... |
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 im... |
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... |
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) |
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 co... |
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, l... |
IBM Granite : Mistral AI, a company that also provides open source models GPT LLaMA Cyc Gemini |
IBM Granite : GitHub page IBM Granite Playground |
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 broa... |
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 independe... |
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. |
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 ... |
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 ext... |
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. |
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 group... |
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 s... |
MMLU : In artificial intelligence, Measuring Massive Multitask Language Understanding (MMLU) is a benchmark for evaluating the capabilities of large language models. |
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 rel... |
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 IC... |
MMLU : == References == |
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 Universit... |
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 resourc... |
MobileNet : Convolutional neural network Deep learning TensorFlow Lite |
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 == |
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 feat... |
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 transf... |
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 performanc... |
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 g... |
Normalization (machine learning) : There are some activation normalization techniques that are only used for CNNs. |
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 gradua... |
Normalization (machine learning) : Gradient normalization (GradNorm) normalizes gradient vectors during backpropagation. |
Normalization (machine learning) : Data preprocessing Feature scaling |
Normalization (machine learning) : "Normalization Layers". labml.ai Deep Learning Paper Implementations. Retrieved 2024-08-07. |
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 speci... |
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. |
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 o... |
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 |
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 Con... |
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 devel... |
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... |
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... |
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 proper... |
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... |
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,*,*,... |
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