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Deep Learning Anti-Aliasing : Official website of DLSS, which also includes information about DLAA |
T9 (predictive text) : T9 is a predictive text technology for mobile phones (specifically those that contain a 3×4 numeric keypad), originally developed by Tegic Communications, now part of Nuance Communications. T9 stands for Text on 9 keys. T9 was used on phones from Verizon, NEC, Nokia, Samsung Electronics, Siemens,... |
T9 (predictive text) : T9's objective is to make it easier to enter text messages. It allows words to be formed by a single keypress for each letter, which is an improvement over the multi-tap approach used in conventional mobile phone text entry at the time, in which several letters are associated with each key, and s... |
T9 (predictive text) : Some T9 implementations feature smart punctuation. This feature allows the user to insert sentence and word punctuation using the '1'-key. Depending on the context, smart punctuation inserts sentence punctuation (period or 'full stop') or embedded punctuation (period or hyphen) or word punctuatio... |
T9 (predictive text) : In order to achieve compression ratios of close to 1 byte per word, T9 uses an optimized algorithm that maintains word order and partial words (also known as stems); however, because of this compression, it over-generates words that are sometimes visible as "junk words". This is a side effect of ... |
T9 (predictive text) : On a phone with a numeric keypad, each time a key (1-9) is pressed (when in a text field), the algorithm returns a guess for what letters are most likely for the keys pressed to that point. For example, to enter the word 'the', the user would press 8 then 4 then 3, and the display would display '... |
T9 (predictive text) : Many smart keyboards now exist, such as Gboard or Swiftkey, that have taken the idea of T9 and combined it with the advanced touchscreen technology found in Android phones and iPhones. These advances have made T9 obsolete in newer cellphones for many users, since it is predicated on the use of a ... |
T9 (predictive text) : LetterWise Phoneword Predictive text Telephone keypad XT9 |
T9 (predictive text) : Nuance T9 customer facing site |
Word2vec : Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, ... |
Word2vec : Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a mapping of the set of words to a vector space, ty... |
Word2vec : This section is based on expositions. A corpus is a sequence of words. Both CBOW and skip-gram are methods to learn one vector per word appearing in the corpus. Let V ("vocabulary") be the set of all words appearing in the corpus C . Our goal is to learn one vector v w ∈ R n \in \mathbb ^ for each word w ... |
Word2vec : In 2010, Tomáš Mikolov (then at Brno University of Technology) with co-authors applied a simple recurrent neural network with a single hidden layer to language modelling. Word2vec was created, patented, and published in 2013 by a team of researchers led by Mikolov at Google over two papers. The original pape... |
Word2vec : Results of word2vec training can be sensitive to parametrization. The following are some important parameters in word2vec training. |
Word2vec : There are a variety of extensions to word2vec. |
Word2vec : The reasons for successful word embedding learning in the word2vec framework are poorly understood. Goldberg and Levy point out that the word2vec objective function causes words that occur in similar contexts to have similar embeddings (as measured by cosine similarity) and note that this is in line with J. ... |
Word2vec : The word embedding approach is able to capture multiple different degrees of similarity between words. Mikolov et al. (2013) found that semantic and syntactic patterns can be reproduced using vector arithmetic. Patterns such as "Man is to Woman as Brother is to Sister" can be generated through algebraic oper... |
Word2vec : Mikolov et al. (2013) developed an approach to assessing the quality of a word2vec model which draws on the semantic and syntactic patterns discussed above. They developed a set of 8,869 semantic relations and 10,675 syntactic relations which they use as a benchmark to test the accuracy of a model. When asse... |
Word2vec : Wikipedia2Vec[1] (introduction) |
Wasserstein GAN : The Wasserstein Generative Adversarial Network (WGAN) is a variant of generative adversarial network (GAN) proposed in 2017 that aims to "improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches".... |
Wasserstein GAN : By the Kantorovich-Rubenstein duality, the definition of Wasserstein GAN is clear:A Wasserstein GAN game is defined by a probability space ( Ω , B , μ r e f ) ,\mu _) , where Ω is a metric space, and a constant K > 0 . There are 2 players: generator and discriminator (also called "critic"). The gene... |
Wasserstein GAN : In the Wasserstein GAN game, the discriminator provides a better gradient than in the GAN game. Consider for example a game on the real line where both μ G and μ r e f are Gaussian. Then the optimal Wasserstein critic D W G A N and the optimal GAN discriminator D are plotted as below: For fixed di... |
Wasserstein GAN : Training the generator in Wasserstein GAN is just gradient descent, the same as in GAN (or most deep learning methods), but training the discriminator is different, as the discriminator is now restricted to have bounded Lipschitz norm. There are several methods for this. |
Wasserstein GAN : From GAN to WGAN Wasserstein GAN and the Kantorovich-Rubinstein Duality Depth First Learning: Wasserstein GAN |
Wasserstein GAN : Generative adversarial network Wasserstein metric Earth mover's distance Transportation theory |
Solomonoff's theory of inductive inference : Solomonoff's theory of inductive inference proves that, under its common sense assumptions (axioms), the best possible scientific model is the shortest algorithm that generates the empirical data under consideration. In addition to the choice of data, other assumptions are t... |
Solomonoff's theory of inductive inference : Algorithmic information theory Bayesian inference Inductive inference Inductive probability Mill's methods Minimum description length Minimum message length For a philosophical viewpoint, see: Problem of induction and New riddle of induction |
Solomonoff's theory of inductive inference : Angluin, Dana; Smith, Carl H. (Sep 1983). "Inductive Inference: Theory and Methods". Computing Surveys. 15 (3): 237–269. doi:10.1145/356914.356918. S2CID 3209224. Burgin, M. (2005), Super-recursive Algorithms, Monographs in computer science, Springer. ISBN 0-387-95569-0 Burg... |
Hierarchical control system : A hierarchical control system (HCS) is a form of control system in which a set of devices and governing software is arranged in a hierarchical tree. When the links in the tree are implemented by a computer network, then that hierarchical control system is also a form of networked control s... |
Hierarchical control system : A human-built system with complex behavior is often organized as a hierarchy. For example, a command hierarchy has among its notable features the organizational chart of superiors, subordinates, and lines of organizational communication. Hierarchical control systems are organized similarly... |
Hierarchical control system : The accompanying diagram is a general hierarchical model which shows functional manufacturing levels using computerised control of an industrial control system. Referring to the diagram; Level 0 contains the field devices such as flow and temperature sensors, and final control elements, su... |
Hierarchical control system : Command hierarchy, a hierarchical power structure Hierarchical organization, a hierarchical organizational structure |
Hierarchical control system : Albus, J.S. (1996). "The Engineering of Mind". From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior. MIT Press. Albus, J.S. (2000). "4-D/RCS reference model architecture for unmanned ground vehicles". Robotics and Automation, 2000... |
Hierarchical control system : The RCS (Realtime Control System) Library Texai An open source project to create artificial intelligence using an Albus hierarchical control system |
Dynamic epistemic logic : Dynamic epistemic logic (DEL) is a logical framework dealing with knowledge and information change. Typically, DEL focuses on situations involving multiple agents and studies how their knowledge changes when events occur. These events can change factual properties of the actual world (they are... |
Dynamic epistemic logic : Epistemic logic is a modal logic dealing with the notions of knowledge and belief. As a logic, it is concerned with understanding the process of reasoning about knowledge and belief: which principles relating the notions of knowledge and belief are intuitively plausible? Like epistemology, it ... |
Dynamic epistemic logic : Dynamic Epistemic Logic (DEL) is a logical framework for modeling epistemic situations involving several agents, and changes that occur to these situations as a result of incoming information or more generally incoming action. The methodology of DEL is such that it splits the task of represent... |
Dynamic epistemic logic : Epistemic logic Epistemology Logic in computer science Modal logic |
Dynamic epistemic logic : van Benthem, Johan (2011). Logical Dynamics of Information and Interaction. Cambridge University Press. ISBN 978-0521873970. Hans, van Ditmarsch; Halpern, Joseph; van der Hoek, Wiebe; Kooi, Barteld (2015). Handbook of Epistemic Logic. London: College publication. ISBN 978-1848901582. van Ditma... |
Dynamic epistemic logic : Baltag, Alexandru; Renne, Bryan. "Dynamic Epistemic Logic". In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy. van Ditmarsch, Hans; van der Hoek, Wiebe; Kooi, Barteld. "Dynamic Epistemic Logic". Internet Encyclopedia of Philosophy. Hendricks, Vincent; Symons, John. "Epistemic Logi... |
Audio inpainting : Audio inpainting (also known as audio interpolation) is an audio restoration task which deals with the reconstruction of missing or corrupted portions of a digital audio signal. Inpainting techniques are employed when parts of the audio have been lost due to various factors such as transmission error... |
Audio inpainting : Consider a digital audio signal x . A corrupted version of x , which is the audio signal presenting missing gaps to be reconstructed, can be defined as x ~ = m ∘ x =\mathbf \circ \mathbf , where m is a binary mask encoding the reliable or missing samples of x , and ∘ represents the eleme... |
Audio inpainting : There exist various techniques to perform audio inpainting. These can vary significantly, influenced by factors such as the specific application requirements, the length of the gaps and the available data. In the literature, these techniques are broadly divided in model-based techniques (sometimes al... |
Audio inpainting : Audio inpainting finds applications in a wide range of fields, including audio restoration and audio forensics among the others. In these fields, audio inpainting can be used to eliminate noise, glitches, or undesired distortions from an audio recording, thus enhancing its quality and intelligibility... |
Audio inpainting : Audio forensics Audio restoration Image inpainting Packet loss concealment == References == |
Artificial intelligence engineering : Artificial intelligence engineering (AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solut... |
Artificial intelligence engineering : AI engineering integrates a variety of technical domains and practices, all of which are essential to building scalable, reliable, and ethical AI systems. |
Artificial intelligence engineering : An AI engineer's workload revolves around the AI system's life cycle, which is a complex, multi-stage process. This process may involve building models from scratch or using pre-existing models through transfer learning, depending on the project's requirements. Each approach presen... |
Artificial intelligence engineering : MLOps, or Artificial Intelligence Operations (AIOps), is a critical component in modern AI engineering, integrating machine learning model development with reliable and efficient operations practices. Similar to the DevOps practices in software development, MLOps provides a framewo... |
Artificial intelligence engineering : AI engineering faces a distinctive set of challenges that differentiate it from traditional software development. One of the primary issues is model drift, where AI models degrade in performance over time due to changes in data patterns, necessitating continuous retraining and adap... |
Artificial intelligence engineering : Training large-scale AI models involves processing immense datasets over prolonged periods, consuming considerable amounts of energy. This has raised concerns about the environmental impact of AI technologies, given the expansion of data centers required to support AI training and ... |
Artificial intelligence engineering : Education in AI engineering typically involves advanced courses in software and data engineering. Key topics include machine learning, deep learning, natural language processing and computer vision. Many universities now offer specialized programs in AI engineering at both the unde... |
Artificial intelligence engineering : Comparison of cognitive architectures Comparison of deep learning software List of datasets in computer vision and image processing List of datasets for machine-learning research Model compression Neural architecture search == References == |
Google Books Ngram Viewer : The Google Books Ngram Viewer is an online search engine that charts the frequencies of any set of search strings using a yearly count of n-grams found in printed sources published between 1500 and 2022 in Google's text corpora in English, Chinese (simplified), French, German, Hebrew, Italia... |
Google Books Ngram Viewer : In the development processes, Google teamed up with two Harvard researchers, Jean-Baptiste Michel and Erez Lieberman Aiden, and quietly released the program on December 16, 2010. Before the release, it was difficult to quantify the rate of linguistic change because of the absence of a databa... |
Google Books Ngram Viewer : Commas delimit user-entered search terms, where each comma-separated term is searched in the database as an n-gram (for example, "nursery school" is a 2-gram or bigram). The Ngram Viewer then returns a plotted line chart. Note that due to limitations on the size of the Ngram database, only m... |
Google Books Ngram Viewer : The data sets of the Ngram Viewer have been criticized for their reliance upon inaccurate optical character recognition (OCR) and for including large numbers of incorrectly dated and categorized texts. Because of these errors, and because they are uncontrolled for bias (such as the increasin... |
Google Books Ngram Viewer : Google Trends Lexical analysis |
Google Books Ngram Viewer : Lin, Yuri; et al. (July 2012). "Syntactic Annotations for the Google Books Ngram Corpus" (PDF). Proceedings of the 50th Annual Meeting. Demo Papers. 2. Jeju, Republic of Korea: Association for Computational Linguistics: 169–174. 2390499. Whitepaper presenting the 2012 edition of the Google B... |
Large memory storage and retrieval neural network : A large memory storage and retrieval neural network (LAMSTAR) is a fast deep learning neural network of many layers that can use many filters simultaneously. These filters may be nonlinear, stochastic, logic, non-stationary, or even non-analytical. They are biological... |
Artificial intelligence industry in China : The artificial intelligence industry in the People's Republic of China is a rapidly developing multi-billion dollar industry. The roots of China's AI development started in the late 1970s following Deng Xiaoping's economic reforms emphasizing science and technology as the cou... |
Artificial intelligence industry in China : The research and development of artificial intelligence in China started in the 1980s, with the announcement by Deng Xiaoping of the importance of science and technology for China's economic growth. |
Artificial intelligence industry in China : According to a February 2019 publication by the Center for a New American Security, CCP general secretary Xi Jinping – believes that being at the forefront of AI technology will be critical to the future of global military and economic power competition. By 2025, the State Co... |
Artificial intelligence industry in China : Leading AI-centric companies and start-ups include Baidu, Tencent, Alibaba, SenseTime, 4Paradigm and Yitu Technology. Chinese AI companies iFlytek, SenseTime, Cloudwalk and DJI have received attention for facial recognition, sound recognition and drone technologies. China's g... |
Artificial intelligence industry in China : Academic Jinghan Zeng argued the Chinese government's commitment to global AI leadership and technological competition was driven by its previous underperformance in innovation which was seen by the CCP as a part of the century of humiliation. According to Zeng, there are his... |
Artificial intelligence industry in China : Artificial intelligence Artificial intelligence arms race China Brain Project Fifth generation computer List of artificial intelligence companies Regulation of artificial intelligence |
Artificial intelligence industry in China : Hannas, William C.; Chang, Huey-Meei, eds. (29 July 2022). Chinese Power and Artificial Intelligence: Perspectives and Challenges (1st ed.). London: Routledge. doi:10.4324/9781003212980. ISBN 9781003212980. OCLC 1320821529. |
Resisting AI : Resisting AI: An Anti-fascist Approach to Artificial Intelligence is a book on artificial intelligence (AI) by Dan McQuillan, published in 2022 by Bristol University Press. |
Resisting AI : Resisting AI takes the form of an extended essay, which contrasts optimistic visions about AI's potential by arguing that AI may best be seen as a continuation and reinforcement of bureaucratic forms of discrimination and violence, ultimately fostering authoritarian outcomes. For McQuillan, AI's promise ... |
Resisting AI : The book is praised for "masterfully disassembles AI as an epistemological, social, and political paradigm, and for his examination of how most of the data that is fed into "privatized AI infrastructure is “amputated” from context or embodied experience and ultimately processed through crowdsourcing." On... |
Resisting AI : Shoshana Zuboff Surveillance capitalism Weapons of Math Destruction Alain Supiot |
Resisting AI : Algorithmic Justice League Cardiff University: Data Justice Lab, School of Journalism, Media and Culture. |
BCPNN : A Bayesian Confidence Propagation Neural Network (BCPNN) is an artificial neural network inspired by Bayes' theorem, which regards neural computation and processing as probabilistic inference. Neural unit activations represent probability ("confidence") in the presence of input features or categories, synaptic ... |
BCPNN : The BCPNN network architecture is modular in terms of hypercolumns and minicolumns. This modular structure is inspired by and generalized from the modular structure of the mammalian cortex. In abstract models, the minicolumns serve as the smallest units and they typically feature a membrane time constant and ad... |
BCPNN : The BCPNN learning rule was derived from Bayes rule and is Hebbian such that neural units with activity correlated over time get excitatory connections between them whereas anti-correlation generates inhibition and lack of correlation gives zero connections. The independence assumptions are the same as in naïve... |
BCPNN : The cortex inspired modular architecture of BCPNN has been the basis for several spiking neural network models of cortex aimed at studying its associative memory functions. In these models, minicolumns comprise about 30 model pyramidal cells and a hypercolumn comprises ten or more such minicolumns and a populat... |
BCPNN : The point-wise mutual information weights of BCPNN is since long one of the standard methods for detection of drug adverse reactions. BCPNN has recently been successfully applied to Machine Learning classification benchmarks, most notably the hand written digits of the MNIST database. The BCPNN approach uses bi... |
BCPNN : The structure of BCPNN with its cortex-like modular architecture and massively parallel correlation based Hebbian learning makes it quite hardware friendly. Implementation with reduced number of bits in synaptic state variables have been shown to be feasible. BCPNN has further been the target for parallel simul... |
University of Technology Sydney : The University of Technology Sydney (UTS) is a public research university located in Sydney, New South Wales, Australia. The university was founded in its current form in 1988, though its origins as a technical institution can be traced back to the 1870s. UTS is a founding member of th... |
University of Technology Sydney : The Sydney Mechanics' School of Arts (the oldest continuously running Mechanics' Institute in Australia) was established in 1833. In the 1870s, the school expanded into technical education and formed the Working Men's College, which was later taken over by the NSW government to form th... |
University of Technology Sydney : The UTS city campus is located at the southern border of Sydney's central business district, close to Central station and Railway Square, within Sydney's emerging Tech Central. The UTS Tower is the nucleus of the city campus, fronting on to Broadway. The campus consists of five distinc... |
University of Technology Sydney : In 2021, the former Dean of Science Diane Jolley was found guilty of causing financial disadvantage by deception after orchestrating a campaign of intimidation – against herself – while pushing to cut the UTS traditional Chinese medicine degree. Cutting of the traditional Chinese medic... |
University of Technology Sydney : List of universities in Australia UTS Glenda Adams Award for New Writing, a literary award sponsored by UTS |
University of Technology Sydney : Official website ActivateUTS website UTS Sport website |
Semantic analytics : Semantic analytics, also termed semantic relatedness, is the use of ontologies to analyze content in web resources. This field of research combines text analytics and Semantic Web technologies like RDF. Semantic analytics measures the relatedness of different ontological concepts. Some academic res... |
Semantic analytics : An important milestone in the beginning of semantic analytics occurred in 1996, although the historical progression of these algorithms is largely subjective. In his seminal study publication, Philip Resnik established that computers have the capacity to emulate human judgement. Spanning the public... |
Semantic analytics : Given the subjective nature of the field, different methods used in semantic analytics depend on the domain of application. No singular methods is considered correct, however one of the most generally effective and applicable method is explicit semantic analysis (ESA). ESA was developed by Evgeniy ... |
Semantic analytics : Entity linking Ontology building / knowledge base population Search and query tasks Natural language processing Spoken dialog systems (e.g., Amazon Alexa, Google Assistant, Microsoft's Cortana) Artificial intelligence Knowledge management The application of semantic analysis methods generally strea... |
Semantic analytics : Relationship extraction Semantic similarity Text analytics |
Frequency principle/spectral bias : The frequency principle/spectral bias is a phenomenon observed in the study of artificial neural networks (ANNs), specifically deep neural networks (DNNs). It describes the tendency of deep neural networks to fit target functions from low to high frequencies during the training proce... |
Softplus : In mathematics and machine learning, the softplus function is f ( x ) = log ( 1 + e x ) . ). It is a smooth approximation (in fact, an analytic function) to the ramp function, which is known as the rectifier or ReLU (rectified linear unit) in machine learning. For large negative x it is log ( 1 + e x ) ... |
Softplus : The derivative of softplus is the logistic function: f ′ ( x ) = e x 1 + e x = 1 1 + e − x = The logistic sigmoid function is a smooth approximation of the derivative of the rectifier, the Heaviside step function. |
Softplus : This function can be approximated as: ln ( 1 + e x ) ≈ \right)\approx \ln 2,&x=0,\\[6pt],&x\neq 0\end By making the change of variables x = y ln ( 2 ) , this is equivalent to log 2 ( 1 + 2 y ) ≈ (1+2^)\approx 1,&y=0,\\[6pt],&y\neq 0.\end A sharpness parameter k may be included: f ( x ) = ln ( 1 + e... |
Rhetorical structure theory : Rhetorical structure theory (RST) is a theory of text organization that describes relations that hold between parts of text. It was originally developed by William Mann, Sandra Thompson, Christian M. I. M. Matthiessen and others at the University of Southern California's Information Scienc... |
Rhetorical structure theory : Rhetorical relations or coherence relations or discourse relations are paratactic (coordinate) or hypotactic (subordinate) relations that hold across two or more text spans. It is widely accepted that notion of coherence is through text relations like this. RST using rhetorical relations p... |
Rhetorical structure theory : RST establishes two different types of units. Nuclei are considered as the most important parts of text whereas satellites contribute to the nuclei and are secondary. Nucleus contains basic information and satellite contains additional information about nucleus. The satellite is often inco... |
Rhetorical structure theory : RST relations are applied recursively in a text, until all units in that text are constituents in an RST relation. The result of such analyses is that RST structure are typically represented as trees, with one top level relation that encompasses other relations at lower levels. |
Rhetorical structure theory : From linguistic point of view, RST proposes a different view of text organization than most linguistic theories. RST points to a tight relation between relations and coherence in text From a computational point of view, it provides a characterization of text relations that has been impleme... |
Rhetorical structure theory : Computer scientists Ana Cristina Bicharra Garcia and Clarisse Sieckenius de Souz have used RST as the basis of a design rationale system called ADD+. In ADD+, RST is used as the basis for the rhetorical organization of a knowledge base, in a way comparable to other knowledge representation... |
Rhetorical structure theory : Argument mining Parse tree == References == |
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