text stringlengths 12 14.7k |
|---|
Automatic summarization : The most common way to evaluate the informativeness of automatic summaries is to compare them with human-made model summaries. Evaluation can be intrinsic or extrinsic, and inter-textual or intra-textual. |
Automatic summarization : The first publication in the area dates back to 1957 (Hans Peter Luhn), starting with a statistical technique. Research increased significantly in 2015. Term frequency–inverse document frequency had been used by 2016. Pattern-based summarization was the most powerful option for multi-document ... |
Automatic summarization : Sentence extraction Text mining Multi-document summarization |
Automatic summarization : 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. |
Automatic summarization : Hercules, Dalianis (2003). Porting and evaluation of automatic summarization. Roxana, Angheluta (2002). The Use of Topic Segmentation for Automatic Summarization. Anne, Buist (2004). Automatic Summarization of Meeting Data: A Feasibility Study (PDF). Archived from the original (PDF) on 2021-01... |
Automated Mathematician : The Automated Mathematician (AM) is one of the earliest successful discovery systems. It was created by Douglas Lenat in Lisp, and in 1977 led to Lenat being awarded the IJCAI Computers and Thought Award. AM worked by generating and modifying short Lisp programs which were then interpreted as ... |
Automated Mathematician : Lenat claimed that the system was composed of hundreds of data structures called "concepts", together with hundreds of "heuristic rules" and a simple flow of control: "AM repeatedly selects the top task from the agenda and tries to carry it out. This is the whole control structure!" Yet the he... |
Automated Mathematician : This intuition was the basis of AM's successor Eurisko, which attempted to generalize the search for mathematical concepts to the search for useful heuristics. |
Automated Mathematician : Computer-assisted proof Automated theorem proving Symbolic mathematics Experimental mathematics HR (software) and Graffiti (program), related math discovery systems |
Automated Mathematician : Edmund Furse; Why did AM run out of steam? Ken Haase's Ph.D. Thesis; Invention and Exploration in Discovery, a rational reconstruction of Doug Lenat's seminal AM program and an analysis of the relationship between invention and exploration in discovery. open source Prolog claimed re-implementa... |
Quantum machine learning : Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. quantum-enhanced machine learning. While machine le... |
Quantum machine learning : Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving and often expediting classical machine learning techniques. Such algorithms typically require one to encode the given classical data set into a quantum computer to make it ac... |
Quantum machine learning : Let \( d_H, d_O \) be two positive integers representing the dimensions of the hidden and observable states, respectively. Define: - \( \mathcal_ \) as the \( C^* \)-algebra of \( d_H \times d_H \) matrices. - \( \mathcal_ \) as the \( C^* \)-algebra of \( d_O \times d_O \) matrices. - The id... |
Quantum machine learning : Hidden Quantum Markov Models (HQMMs) are a quantum-enhanced version of classical Hidden Markov Models (HMMs), which are typically used to model sequential data in various fields like robotics and natural language processing. Unlike other quantum-enhanced machine learning algorithms, HQMMs can... |
Quantum machine learning : A linear map \( \mathcal_H : \mathcal_ \otimes \mathcal_ \to \mathcal_ \) is called a **transition expectation** if it is completely positive and identity-preserving \cite. Similarly, a linear map \( \mathcal_ : \mathcal_ \otimes \mathcal_ \to \mathcal_ \) is called an **emission operator** i... |
Quantum machine learning : The term "quantum machine learning" sometimes refers to classical machine learning performed on data from quantum systems. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. Other applications include learning Hamiltonians and automatically... |
Quantum machine learning : Quantum learning theory pursues a mathematical analysis of the quantum generalizations of classical learning models and of the possible speed-ups or other improvements that they may provide. The framework is very similar to that of classical computational learning theory, but the learner in t... |
Quantum machine learning : The earliest experiments were conducted using the adiabatic D-Wave quantum computer, for instance, to detect cars in digital images using regularized boosting with a nonconvex objective function in a demonstration in 2009. Many experiments followed on the same architecture, and leading tech c... |
Quantum machine learning : While machine learning itself is now not only a research field but an economically significant and fast growing industry and quantum computing is a well established field of both theoretical and experimental research, quantum machine learning remains a purely theoretical field of studies. Att... |
Quantum machine learning : Differentiable programming Quantum computing Quantum algorithm for linear systems of equations Quantum annealing Quantum neural network Quantum image == References == |
Entity linking : In natural language processing, Entity Linking, also referred to as named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD), named-entity normalization (NEN), or Concept Recognition, is the task of assigning a unique identity to entities (such as famous individuals, locati... |
Entity linking : In entity linking, words of interest (names of persons, locations and companies) are mapped from an input text to corresponding unique entities in a target knowledge base. Words of interest are called named entities (NEs), mentions, or surface forms. The target knowledge base depends on the intended ap... |
Entity linking : Entity linking has been a hot topic in industry and academia for the last decade. Many challenges are unsolved, but many entity linking systems have been proposed, with widely different strengths and weaknesses. Broadly speaking, modern entity linking systems can be divided into two categories: Text-ba... |
Entity linking : Controlled vocabulary Explicit semantic analysis Geoparsing Information extraction Linked data Named entity Named-entity recognition Record linkage Word sense disambiguation Author Name Disambiguation Coreference Annotation == References == |
Optical neural network : An optical neural network is a physical implementation of an artificial neural network with optical components. Early optical neural networks used a photorefractive Volume hologram to interconnect arrays of input neurons to arrays of output with synaptic weights in proportion to the multiplexed... |
Optical neural network : Biological neural networks function on an electrochemical basis, while optical neural networks use electromagnetic waves. Optical interfaces to biological neural networks can be created with optogenetics, but is not the same as an optical neural networks. In biological neural networks there exi... |
Optical neural network : With the increasing significance of computer vision in various domains, the computational cost of these tasks has increased, making it more important to develop the new approaches of the processing acceleration. Optical computing has emerged as a potential alternative to GPU acceleration for mo... |
Optical neural network : In 2007 there was one model of Optical Neural Network: the Programmable Optical Array/Analogic Computer (POAC). It had been implemented in the year 2000 and reported based on modified Joint Fourier Transform Correlator (JTC) and Bacteriorhodopsin (BR) as a holographic optical memory. Full paral... |
Optical neural network : Taichi from Tsinghua University in Beijing is a hybrid ONN that combines the power efficiency and parallelism of optical diffraction and the configurability of optical interference. Taichi offers 13.96 million parameters. Taichi avoids the high error rates that afflict deep (multi-layer) networ... |
Optical neural network : Optical computing Quantum neural network == References == |
Brain technology : Brain technology, or self-learning know-how systems, defines a technology that employs latest findings in neuroscience. [see also neuro implants] The term was first introduced by the Artificial Intelligence Laboratory in Zurich, Switzerland, in the context of the Roboy project. Brain Technology can b... |
Brain technology : The first demonstrations of BC in humans and animals took place in the 1960s when Grey Walter demonstrated use of non-invasively recorded encephalogram (EEG) signals from a human subject to control a slide projector (Graimann et al., 2010). Soon after Jacques J. Vidal coined the term brain–computer i... |
Eager learning : In artificial intelligence, eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to lazy learning, where generalization beyond the training data is delayed until a query is made to the system. T... |
Text-to-image personalization : Text-to-Image personalization is a task in deep learning for computer graphics that augments pre-trained text-to-image generative models. In this task, a generative model that was trained on large-scale data (usually a foundation model), is adapted such that it can generate images of nov... |
Text-to-image personalization : Text-to-Image personalization was first proposed during August 2022 by two concurrent works, Textual Inversion and DreamBooth. In both cases, a user provides a few images (typically 3–5) of a concept, like their own dog, together with a coarse descriptor of the concept class (like the wo... |
Text-to-image personalization : The key idea in textual inversion is to add a new term to the vocabulary of the diffusion model that corresponds to the new (personalized) concept. Textual inversion optimizes the vector embedding of that new term such that using it as an input text prompt will generate images that are s... |
Text-to-image personalization : Several approaches were proposed to refine and improve over the original methods. These include the following. Low-rank Adaptation (LoRA) - an adapter-based technique for efficient finetuning of models. In the case of text-to-image models, LoRA is typically used to modify the cross-atten... |
Text-to-image personalization : Text-to-image personalization methods must contend with several challenges. At their core is the goal of achieving high-fidelity to the personal concept while maintaining high alignment between novel prompts containing the subject, and the generated images (typically referred to as ‘edit... |
Chroma (vector database) : Chroma or ChromaDB is an open-source vector database tailored to applications with large language models. Its headquarters are in San Francisco. In April 2023, it raised 18 million US dollars as seed funding. ChromaDB has been used in academic studies on artificial intelligence, particularly ... |
Oja's rule : Oja's learning rule, or simply Oja's rule, named after Finnish computer scientist Erkki Oja (Finnish pronunciation: [ˈojɑ], AW-yuh), is a model of how neurons in the brain or in artificial neural networks change connection strength, or learn, over time. It is a modification of the standard Hebb's Rule that... |
Oja's rule : Oja's rule requires a number of simplifications to derive, but in its final form it is demonstrably stable, unlike Hebb's rule. It is a single-neuron special case of the Generalized Hebbian Algorithm. However, Oja's rule can also be generalized in other ways to varying degrees of stability and success. |
Oja's rule : Oja's rule was originally described in Oja's 1982 paper, but the principle of self-organization to which it is applied is first attributed to Alan Turing in 1952. PCA has also had a long history of use before Oja's rule formalized its use in network computation in 1989. The model can thus be applied to any... |
Oja's rule : BCM theory Contrastive Hebbian learning Generalized Hebbian algorithm Independent components analysis Principal component analysis Self-organizing map Synaptic plasticity |
Oja's rule : Oja, Erkki: Oja learning rule in Scholarpedia Oja, Erkki: Aalto University |
ISLRN : The ISLRN or International Standard Language Resource Number is Persistent Unique Identifier for Language Resources. |
ISLRN : On November 18, 2013, 12 major organisations (see list below) from the fields Language Resources and Technologies, Computational Linguistics, and Digital Humanities held a cooperation meeting in Paris (France) and agreed to announce the establishment of the International Standard Language Resource Number (ISLRN... |
ISLRN : The Joint Research Centre(JRC), the [European Commission]'s in-house science service, was the first organisation to adopt the ISLRN initiative and requested. 2500 resources and tools have already been allocated an ISLRN. These resources include written data (Annotated corpus, Annotated text, List of misspelled ... |
ISLRN : Providing Language Resources with unique names and identifiers using a standardized nomenclature ensures the identification of each Language Resources and streamlines the citation with proper references in activities within Human Language Technology as well as in documents and scientific publications. Such uniq... |
ISLRN : ISLRN Portal |
Natural-language programming : Natural-language programming (NLP) is an ontology-assisted way of programming in terms of natural-language sentences, e.g. English. A structured document with Content, sections and subsections for explanations of sentences forms a NLP document, which is actually a computer program. Natura... |
Natural-language programming : The smallest unit of statement in NLP is a sentence. Each sentence is stated in terms of concepts from the underlying ontology, attributes in that ontology and named objects in capital letters. In an NLP text every sentence unambiguously compiles into a procedure call in the underlying hi... |
Natural-language programming : Natural-language programming is a top-down method of writing software. Its stages are as follows: Definition of an ontology – taxonomy – of concepts needed to describe tasks in the topic addressed. Each concept and all their attributes are defined in natural-language words. This ontology ... |
Natural-language programming : A natural-language program is a precise formal description of some procedure that its author created. It is human readable and it can also be read by a suitable software agent. For example, a web page in an NLP format can be read by a software personal assistant agent to a person and she ... |
Natural-language programming : An ontology class is a natural-language program that is not a concept in the sense as humans use concepts. Concepts in an NLP are examples (samples) of generic human concepts. Each sentence in a natural-language program is either (1) stating a relationship in a world model or (2) carries ... |
Natural-language programming : Researchers have started to experiment with natural language programming environments that use plain language prompts and then use AI (specifically large language models) to turn natural language into formal code. For example Spatial Pixel created a natural language programming environmen... |
Natural-language programming : Controlled natural language Context-free language Domain-specific language (or DSL) End-user programming Knowledge representation Natural-language processing Source-code generation Very high-level programming language |
Natural-language programming : Books Natural Language Programming of Agents and Robotic Devices: publishing for agents and humans in sEnglish by S M Veres, ISBN 978-0-9558417-0-5, London, June 2008. Dijkstra, Edsger W. (1979). "On the foolishness of "natural language programming"". Program Construction. Lecture Notes i... |
Natural-language programming : English Script (dormant since 2016) Plain English Programming Programming language using English sentences in ASCII. SEMPRE – a toolkit for training semantic parsers sysbrain.com – sEnglish Editor in C++/ROS for robot programming to develop transparent robots. wy-lang.org – "Programming L... |
Decision tree pruning : Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predict... |
Decision tree pruning : Pruning processes can be divided into two types (pre- and post-pruning). Pre-pruning procedures prevent a complete induction of the training set by replacing a stop () criterion in the induction algorithm (e.g. max. Tree depth or information gain (Attr)> minGain). Pre-pruning methods are conside... |
Decision tree pruning : Pruning could be applied in a compression scheme of a learning algorithm to remove the redundant details without compromising the model's performances. In neural networks, pruning removes entire neurons or layers of neurons. |
Decision tree pruning : Alpha–beta pruning Artificial neural network Null-move heuristic Pruning (artificial neural network) |
Decision tree pruning : Pearl, Judea (1984). Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley. ISBN 978-0-201-05594-8. Mansour, Y. (1997). "Pessimistic decision tree pruning based on tree size". Proc. 14th International Conference on Machine Learning. pp. 195–201. Breslow, L. A.; A... |
Decision tree pruning : MDL based decision tree pruning Archived 2017-08-29 at the Wayback Machine Decision tree pruning using backpropagation neural networks |
Decision tree pruning : Fast, Bottom-Up Decision Tree Pruning Algorithm Introduction to Decision tree pruning |
Machine unlearning : Machine unlearning is a branch of machine learning focused on removing specific undesired element, such as private data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up. Large language models, ... |
Machine unlearning : Early research efforts were largely motivated by Article 17 of the GDPR, the European Union's privacy regulation commonly known as the "right to be forgotten" (RTBF), introduced in 2014. |
Machine unlearning : The GDPR did not anticipate that the development of large language models would make data erasure a complex task. This issue has since led to research on "machine unlearning," with a growing focus on removing copyrighted material, harmful content, dangerous capabilities, and misinformation. Just as... |
Personality computing : Personality computing is a research field related to artificial intelligence and personality psychology that studies personality by means of computational techniques from different sources, including text, multimedia, and social networks. |
Personality computing : Personality computing addresses three main problems involving personality: automatic personality recognition, perception, and synthesis. Automatic personality recognition is the inference of the personality type of target individuals from their digital footprint. Automatic personality perception... |
Personality computing : Personality computing began around 2005 with the pioneering research in personality recognition by Shlomo Argamon and later by François Mairesse. These works showed that personality traits could be inferred with reasonable accuracy from text, such as blogs, self-presentations, and email addresse... |
Personality computing : Personality computing techniques, in particular personality recognition and perception, have applications in Social media marketing, where they can help reducing the cost of advertising campaigns through psychological targeting. == References == |
Compositional pattern-producing network : Compositional pattern-producing networks (CPPNs) are a variation of artificial neural networks (ANNs) that have an architecture whose evolution is guided by genetic algorithms. While ANNs often contain only sigmoid functions and sometimes Gaussian functions, CPPNs can include b... |
Compositional pattern-producing network : Evolutionary art Interactive evolutionary computation |
Compositional pattern-producing network : "PicBreeder.org" Archived 2011-07-25 at the Wayback Machine – Online, collaborative art generated by CPPNs evolved with NeuroEvolution of Augmenting Topologies. "EndlessForms.com" Archived 2018-11-14 at the Wayback Machine – A 3D version of Picbreeder, where you interactively e... |
Bradley–Terry model : The Bradley–Terry model is a probability model for the outcome of pairwise comparisons between items, teams, or objects. Given a pair of items i and j drawn from some population, it estimates the probability that the pairwise comparison i > j turns out true, as where pi is a positive real-valued s... |
Bradley–Terry model : The model is named after Ralph A. Bradley and Milton E. Terry, who presented it in 1952, although it had already been studied by Ernst Zermelo in the 1920s. Applications of the model include the ranking of competitors in sports, chess, and other competitions, the ranking of products in paired comp... |
Bradley–Terry model : The Bradley–Terry model can be parametrized in various ways. Equation (1) is perhaps the most common, but there are a number of others. Bradley and Terry themselves defined exponential score functions p i = e β i =e^ , so that Pr ( i > j ) = e β i e β i + e β j . +e^. Alternatively, one can use a ... |
Bradley–Terry model : A standard generalization of the BT model is the Plackett–Luce model, which models ranking N items. In the same notation as BT model: Pr ( y 1 > ⋯ > y N ) = p y 1 p y 1 + ⋯ + p y N p y 2 p y 2 + ⋯ + p y N … p y N p y N >\dots >y_)=+\dots +p_+\dots +p_\dots This can be imagined as drawing from an... |
Bradley–Terry model : The most common application of the Bradley–Terry model is to infer the values of the parameters p i given an observed set of outcomes i > j , such as wins and losses in a competition. The simplest way to estimate the parameters is by maximum likelihood estimation, i.e., by maximizing the likelih... |
Bradley–Terry model : Ordinal regression Rasch model Scale (social sciences) Elo rating system Thurstonian model == References == |
Transformer (deep learning architecture) : The transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which was proposed in the 2017 paper "Attention Is All You Need". Text is converted to numerical representations called tokens, and e... |
Transformer (deep learning architecture) : All transformers have the same primary components: Tokenizers, which convert text into tokens. Embedding layer, which converts tokens and positions of the tokens into vector representations. Transformer layers, which carry out repeated transformations on the vector representat... |
Transformer (deep learning architecture) : The transformer has had great success in natural language processing (NLP). Many large language models such as GPT-2, GPT-3, GPT-4, AlbertAGPT, Claude, BERT, XLNet, RoBERTa and ChatGPT demonstrate the ability of transformers to perform a wide variety of NLP-related subtasks an... |
Transformer (deep learning architecture) : seq2seq – Family of machine learning approaches Perceiver – Variant of Transformer designed for multimodal data Vision transformer – Machine learning model for vision processing Large language model – Type of machine learning model BERT (language model) – Series of language mo... |
Google DeepMind : DeepMind Technologies Limited, trading as Google DeepMind or simply DeepMind, is a British–American artificial intelligence research laboratory which serves as a subsidiary of Alphabet Inc. Founded in the UK in 2010, it was acquired by Google in 2014 and merged with Google AI's Google Brain division t... |
Google DeepMind : The start-up was founded by Demis Hassabis, Shane Legg and Mustafa Suleyman in November 2010. Hassabis and Legg first met at the Gatsby Computational Neuroscience Unit at University College London (UCL). Demis Hassabis has said that the start-up began working on artificial intelligence technology by t... |
Google DeepMind : Google Research released a paper in 2016 regarding AI safety and avoiding undesirable behaviour during the AI learning process. In 2017 DeepMind released GridWorld, an open-source testbed for evaluating whether an algorithm learns to disable its kill switch or otherwise exhibits certain undesirable be... |
Google DeepMind : In July 2016, a collaboration between DeepMind and Moorfields Eye Hospital was announced to develop AI applications for healthcare. DeepMind would be applied to the analysis of anonymised eye scans, searching for early signs of diseases leading to blindness. In August 2016, a research programme with U... |
Google DeepMind : In October 2017, DeepMind announced a new research unit, DeepMind Ethics & Society. Their goal is to fund external research of the following themes: privacy, transparency, and fairness; economic impacts; governance and accountability; managing AI risk; AI morality and values; and how AI can address th... |
Google DeepMind : DeepMind sponsors three chairs of machine learning: At the University of Cambridge, held by Neil Lawrence, in the Department of Computer Science and Technology, At the University of Oxford, held by Michael Bronstein, in the Department of Computer Science, and At the University College London, held by ... |
Google DeepMind : Anthropic Cohere Glossary of artificial intelligence Imagen OpenAI Robot Constitution |
Google DeepMind : Official website GitHub Repositories |
ELMo : ELMo (embeddings from language model) is a word embedding method for representing a sequence of words as a corresponding sequence of vectors. It was created by researchers at the Allen Institute for Artificial Intelligence, and University of Washington and first released in February, 2018. It is a bidirectional ... |
ELMo : ELMo is a multilayered bidirectional LSTM on top of a token embedding layer. The output of all LSTMs concatenated together consists of the token embedding. The input text sequence is first mapped by an embedding layer into a sequence of vectors. Then two parts are run in parallel over it. The forward part is a 2... |
ELMo : ELMo is one link in a historical evolution of language modelling. Consider a simple problem of document classification, where we want to assign a label (e.g., "spam", "not spam", "politics", "sports") to a given piece of text. The simplest approach is the "bag of words" approach, where each word in the document ... |
Grokking (machine learning) : In machine learning, grokking, or delayed generalization, is a transition to generalization that occurs many training iterations after the interpolation threshold, after many iterations of seemingly little progress, as opposed to the usual process where generalization occurs slowly and pro... |
Grokking (machine learning) : Grokking was introduced in January 2022 by OpenAI researchers investigating how neural network perform calculations. It derives from the word grok coined by Robert Heinlein in his novel Stranger in a Strange Land. Grokking can be understood as a phase transition during the training process... |
Grokking (machine learning) : Deep double descent == References == |
Pattern recognition : Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergen... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.