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Backpropagation through time : The training data for a recurrent neural network is an ordered sequence of k input-output pairs, ⟨ a 0 , y 0 ⟩ , ⟨ a 1 , y 1 ⟩ , ⟨ a 2 , y 2 ⟩ , . . . , ⟨ a k − 1 , y k − 1 ⟩ _,\mathbf _\rangle ,\langle \mathbf _,\mathbf _\rangle ,\langle \mathbf _,\mathbf _\rangle ,...,\langle \ma... |
Backpropagation through time : Below is pseudocode for a truncated version of BPTT, where the training data contains n input-output pairs, and the network is unfolded for k time steps: Back_Propagation_Through_Time(a, y) // a[t] is the input at time t. y[t] is the output Unfold the network to contain k instances of f... |
Backpropagation through time : BPTT tends to be significantly faster for training recurrent neural networks than general-purpose optimization techniques such as evolutionary optimization. |
Backpropagation through time : BPTT has difficulty with local optima. With recurrent neural networks, local optima are a much more significant problem than with feed-forward neural networks. The recurrent feedback in such networks tends to create chaotic responses in the error surface which cause local optima to occur ... |
Backpropagation through time : Backpropagation through structure == References == |
HireVue : HireVue is an artificial intelligence (AI) and human resources management company headquartered in South Jordan, Utah. Founded in 2004, the company allows its clients to conduct digital interviews during the hiring process, where the job candidate interacts with a computer instead of a human interviewer. The ... |
HireVue : HireVue's service has received some criticism from interview candidates and AI researchers, particularly for its analysis of "micro-expressions". Commentary on its perceived drawbacks and potential to incite anxiety in interviewees has also been made. In 2015, Fortune writer Katherine Reynolds Lewis called vi... |
HireVue : Artificial empathy Artificial intelligence in hiring |
HireVue : Feloni, Richard (June 28, 2017). "Consumer-goods giant Unilever has been hiring employees using brain games and artificial intelligence — and it's a huge success". Business Insider. Insider Inc. Retrieved October 20, 2023. |
Model-based clustering : In statistics, cluster analysis is the algorithmic grouping of objects into homogeneous groups based on numerical measurements. Model-based clustering based on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for cl... |
Model-based clustering : Suppose that for each of n observations we have data on d variables, denoted by y i = ( y i , 1 , … , y i , d ) =(y_,\ldots ,y_) for observation i . Then model-based clustering expresses the probability density function of y i as a finite mixture, or weighted average of G component probabi... |
Model-based clustering : We illustrate the method with a dateset consisting of three measurements (glucose, insulin, sspg) on 145 subjects for the purpose of diagnosing diabetes and the type of diabetes present. The subjects were clinically classified into three groups: normal, chemical diabetes and overt diabetes, but... |
Model-based clustering : An outlier in clustering is a data point that does not belong to any of the clusters. One way of modeling outliers in model-based clustering is to include an additional mixture component that is very dispersed, with for example a uniform distribution. Another approach is to replace the multivar... |
Model-based clustering : Sometimes one or more clusters deviate strongly from the Gaussian assumption. If a Gaussian mixture is fitted to such data, a strongly non-Gaussian cluster will often be represented by several mixture components rather than a single one. In that case, cluster merging can be used to find a bette... |
Model-based clustering : Much of the model-based clustering software is in the form of a publicly and freely available R package. Many of these are listed in the CRAN Task View on Cluster Analysis and Finite Mixture Models. The most used such package is mclust, which is used to cluster continuous data and has been down... |
Model-based clustering : Model-based clustering was first invented in 1950 by Paul Lazarsfeld for clustering multivariate discrete data, in the form of the latent class model. In 1959, Lazarsfeld gave a lecture on latent structure analysis at the University of California-Berkeley, where John H. Wolfe was an M.A. studen... |
Model-based clustering : Scrucca, L.; Fraley, C.; Murphy, T.B.; Raftery, A.E. (2023). Model-Based Clustering, Classification and Density Estimation using mclust in R. Chapman and Hall/CRC Press. ISBN 9781032234953. Bouveyron, C.; Celeux, G.; Murphy, T.B.; Raftery, A.E. (2019). Model-Based Clustering and Classification ... |
Wetware (brain) : Wetware is a term drawn from the computer-related idea of hardware or software, but applied to biological life forms. |
Wetware (brain) : The prefix "wet" is a reference to the water found in living creatures. Wetware is used to describe the elements equivalent to hardware and software found in a person, especially the central nervous system (CNS) and the human mind. The term wetware finds use in works of fiction, in scholarly publicati... |
Wetware (brain) : Although the exact definition has shifted over time, the term Wetware and its fundamental reference to "the physical mind" has been around at least since the mid-1950s. Mostly used in relatively obscure articles and papers, it was not until the heyday of cyberpunk, however, that the term found broad a... |
Wetware (brain) : Rat-brain robot aids memory study "Illegal Knowledge" A text about wetware written by the writers' collective of which Lovink was a part |
Discourse relation : A discourse relation (also coherence relation or rhetorical relation) is a description of how two segments of discourse are logically and/or structurally connected to one another. A widely upheld position is that in coherent discourse, every individual utterance is connected by a discourse relation... |
Discourse relation : In a series of seminal papers, Jerry Hobbs investigated the interplay of discourse relations and coherence since the late 1970s. His work has been the basis for most subsequent theories and annotation frameworks of discourse relations. He proposed the following relations: occasion (change of state)... |
Discourse relation : Introduced in 1987, Rhetorical Structure Theory (RST) uses rhetorical relations as a systematic way for an analyst to annotate a given text. An analysis is usually built by reading the text and constructing a tree using the relations. RST has been designed as a framework for the principled annotati... |
Discourse relation : In its original motivation, SDRT attempts to complement Discourse Representation Theory (DRT) with RST-style discourse relations. Asher and Lascarides (2003) categorize SDRT discourse relations into several classes: Content-level relations Text structuring relations Divergent relations Metatalk rel... |
Discourse relation : In the early days of computational discourse, the study of discourse relations was closely entangled with the study of discourse structure, so that theories such as RST and SDRT effectively postulate tree structures. (SDRT permits relations between independent nodes in a tree, but the tree still de... |
Discourse relation : Speech act Contrast (linguistics) |
Discourse relation : Asher, Nicholas and Alex Lascarides (2003). Logics of Conversation. Studies in Natural Language Processing. Cambridge University Press. ISBN 0-521-65058-5 Pitler, Emily and others (2008). "Easily Identifiable Discourse Relations". University of Pennsylvania Department of Computer and Information Sc... |
Discourse relation : Rhetorical Structure Theory — RST website, created by William C. Mann, maintained by Maite Taboada |
Calais (Reuters product) : Calais is a service created by Thomson Reuters that automatically extracts semantic information from web pages in a format that can be used on the semantic web. Calais was launched in January 2008, and is free to use. The technology is now available via the website of Refinitiv, a provider of... |
Calais (Reuters product) : Official website Curry, E.; Freitas, A.; O'Riáin, S. (2010). "The Role of Community-Driven Data Curation for Enterprises" (PDF). In Wood, D. (ed.). Linking Enterprise Data. Boston, MA: Springer US. pp. 25–47. Bibcode:2010led..book.....W. doi:10.1007/978-1-4419-7665-9. hdl:10983/14597. ISBN 97... |
Lottery ticket hypothesis : In machine learning, the lottery ticket hypothesis is that artificial neural networks with random weights can contain subnetworks which entirely by chance can be tuned to a similar level of performance as the complete network. The term derived from considering the tunable subnetwork as the e... |
Lottery ticket hypothesis : Grokking (machine learning) Pruning (artificial neural network) == References == |
Seq2seq : Seq2seq is a family of machine learning approaches used for natural language processing. Applications include language translation, image captioning, conversational models, and text summarization. Seq2seq uses sequence transformation: it turns one sequence into another sequence. |
Seq2seq : One naturally wonders if the problem of translation could conceivably be treated as a problem in cryptography. When I look at an article in Russian, I say: 'This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode. seq2seq is an approach to machine transla... |
Seq2seq : In 2019, Facebook announced its use in symbolic integration and resolution of differential equations. The company claimed that it could solve complex equations more rapidly and with greater accuracy than commercial solutions such as Mathematica, MATLAB and Maple. First, the equation is parsed into a tree stru... |
Seq2seq : Artificial neural network |
Seq2seq : "A ten-minute introduction to sequence-to-sequence learning in Keras". blog.keras.io. Retrieved 2019-12-19. Adiwardana, Daniel; Luong, Minh-Thang; So, David R.; Hall, Jamie; Fiedel, Noah; Thoppilan, Romal; Yang, Zi; Kulshreshtha, Apoorv; Nemade, Gaurav; Lu, Yifeng; Le, Quoc V. (2020-01-31). "Towards a Human-l... |
Lazy learning : (Not to be confused with the lazy learning regime, see Neural tangent kernel). In machine learning, lazy learning is a learning method in which generalization of the training data is, in theory, delayed until a query is made to the system, as opposed to eager learning, where the system tries to generali... |
Lazy learning : The main advantage gained in employing a lazy learning method is that the target function will be approximated locally, such as in the k-nearest neighbor algorithm. Because the target function is approximated locally for each query to the system, lazy learning systems can simultaneously solve multiple p... |
Lazy learning : Theoretical disadvantages with lazy learning include: The large space requirement to store the entire training dataset. In practice, this is not an issue because of advances in hardware and the relatively small number of attributes (e.g., as co-occurrence frequency) that need to be stored. Particularly ... |
Lazy learning : K-nearest neighbors, which is a special case of instance-based learning. Local regression. Lazy naive Bayes rules, which are extensively used in commercial spam detection software. Here, the spammers keep getting smarter and revising their spamming strategies, and therefore the learning rules must also ... |
Lazy learning : lazy: Lazy Learning for Local Regression, R package with reference manual "The Lazy Learning Package". Archived from the original on 16 February 2012. Webb G.I. (2011) Lazy Learning. In: Sammut C., Webb G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA David W. Aha: Lazy learning. Kluwer... |
Artificial intelligence in India : The artificial intelligence (AI) market in India is projected to reach $8 billion by 2025, growing at a compound annual growth rate (CAGR) of over 40% from 2020 to 2025. This growth is part of the broader AI boom, a global period of rapid technological advancements with India being pi... |
Artificial intelligence in India : The mission which has a five-year budget of ₹3,660 crore, was authorized by the Union Cabinet in December 2018 under the Department of Science and Technology. A technological vertical in AI and ML, IoT, data bank and DaaS, data analysis, autonomous systems and robotics, cyber security... |
Artificial intelligence in India : India currently does not have specific laws regulating artificial intelligence (AI). However, the Indian government has introduced several initiatives and guidelines aimed at the responsible development and deployment of AI technologies. The Indian government has tasked NITI Aayog, it... |
Artificial intelligence in India : The Bharat GPT is a non-profit initiative, started in February 2023. The goal is to develop India focused multilingual, multimodal large language models and generative pre-trained transformer. Together with the applications and implementation frameworks, the Bharat GPT Consortium inte... |
Artificial intelligence in India : IIT Bombay Professor Ganesh Ramakrishnan thought of creating a homegrown solution that would use GenAI and take into account the linguistic and cultural diversity of India. An open-source, multimodal, multilingual, India-centric foundation model called BharatGen was formally introduce... |
Artificial intelligence in India : On January 30, 2025, Ashwini Vaishnaw, Minister for Electronics & Information Technology, confirmed that the IndiaAI Mission would customize native AI solutions for the Indian context using Indian languages, supported by a state-of-the-art shared computing infrastructure. The initial ... |
Artificial intelligence in India : India's Ministry of Electronics and Information Technology and Japan's Ministry of Economy, Trade, and Industry signed a memorandum of cooperation on October 29, 2018, which outlines the two nations' cooperation in AI and IoT as part of India-Japan Cooperation on Digital Partnership. ... |
Artificial intelligence in India : The following is a list of notable AI companies of India, along with their corporate headquarters location. |
Artificial intelligence in India : In order to determine whether to create an AI Safety Institute (AISI) that can establish standards, frameworks, and guidelines for AI development without serving as a regulatory body or stifling innovation, MeitY conducted consultation process with Meta Platforms, Google, Microsoft, I... |
Artificial intelligence in India : HCLTech Infosys Tata Elxsi == References == |
Proximal gradient methods for learning : Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which studies algorithms for a general class of convex regularization problems where the regularization penalty may not be differentiable. O... |
Proximal gradient methods for learning : Proximal gradient methods are applicable in a wide variety of scenarios for solving convex optimization problems of the form min x ∈ H F ( x ) + R ( x ) , F(x)+R(x), where F is convex and differentiable with Lipschitz continuous gradient, R is a convex, lower semicontinuous fu... |
Proximal gradient methods for learning : Consider the regularized empirical risk minimization problem with square loss and with the ℓ 1 norm as the regularization penalty: min w ∈ R d 1 n ∑ i = 1 n ( y i − ⟨ w , x i ⟩ ) 2 + λ ‖ w ‖ 1 , ^\sum _^(y_-\langle w,x_\rangle )^+\lambda \|w\|_, where x i ∈ R d and y i ∈ R . \... |
Proximal gradient methods for learning : There have been numerous developments within the past decade in convex optimization techniques which have influenced the application of proximal gradient methods in statistical learning theory. Here we survey a few important topics which can greatly improve practical algorithmic... |
Proximal gradient methods for learning : Proximal gradient methods provide a general framework which is applicable to a wide variety of problems in statistical learning theory. Certain problems in learning can often involve data which has additional structure that is known a priori. In the past several years there have... |
Proximal gradient methods for learning : Convex analysis Proximal gradient method Regularization Statistical learning theory == References == |
Knowledge-based recommender system : Knowledge-based recommender systems (knowledge based recommenders) are a specific type of recommender system that are based on explicit knowledge about the item assortment, user preferences, and recommendation criteria (i.e., which item should be recommended in which context). These... |
Knowledge-based recommender system : Knowledge-based recommender systems are well suited to complex domains where items are not purchased very often, such as apartments and cars. Further examples of item domains relevant for knowledge-based recommender systems are financial services, digital cameras, and tourist destin... |
Knowledge-based recommender system : Knowledge-based recommender systems are often conversational, i.e., user requirements and preferences are elicited within the scope of a feedback loop. A major reason for the conversational nature of knowledge-based recommender systems is the complexity of the item domain where it i... |
Knowledge-based recommender system : In a search-based recommender, user feedback is given in terms of answers to questions which restrict the set of relevant items. An example of such a question is "Which type of lens system do you prefer: fixed or exchangeable lenses?". On the technical level, search-based recommenda... |
Knowledge-based recommender system : In a navigation-based recommender, user feedback is typically provided in terms of "critiques" which specify change requests regarding the item currently recommended to the user. Critiques are then used for the recommendation of the next "candidate" item. An example of a critique in... |
Knowledge-based recommender system : Recommender system Collaborative filtering Cold start Case-based reasoning Constraint satisfaction Knowledge-based configuration Guided selling |
Knowledge-based recommender system : Systems and datasets WeeVis Wiki-based Recommendation Environment VITA: Knowledge-based Recommender for Financial Services MyProductAdvisor Entree Dataset |
Niki.ai : Niki was an artificial intelligence company headquartered in Bangalore, Karnataka. It was founded in May 2015 by IIT Kharagpur graduates Sachin Jaiswal, Keshav Prawasi, Shishir Modi, and Nitin Babel. The Niki android app was launched for a limited beta in June 2015, then released for public during YourStory's... |
Niki.ai : The product is an artificial intelligence-powered chatbot which works as an intelligent personal assistant, named Niki. Leveraging natural language processing and machine learning, Niki presents a chat-based natural language user interface to the users where they can interact with Niki in their natural langua... |
Niki.ai : In September 2017, Infosys Finacle joined with Niki.ai to provide chat-based service to banking customers. In August 2017, Niki partnered with LazyPay to enable a 'buy now, pay later' feature for its users. |
Niki.ai : Artificial Intelligence AI Companies of India |
Niki.ai : Official website Blog niki.ai on Medium SAP.IO |
Accelerated Linear Algebra : XLA (Accelerated Linear Algebra) is an open-source compiler for machine learning developed by the OpenXLA project. XLA is designed to improve the performance of machine learning models by optimizing the computation graphs at a lower level, making it particularly useful for large-scale compu... |
Accelerated Linear Algebra : x86-64 ARM64 NVIDIA GPU AMD GPU Intel GPU Apple GPU Google TPU AWS Trainium, Inferentia Cerebras Graphcore IPU |
Accelerated Linear Algebra : TensorFlow PyTorch JAX == References == |
Lynda Soderholm : Lynda Soderholm is a physical chemist at the U.S. Department of Energy's (DOE) Argonne National Laboratory with a specialty in f-block elements. She is a senior scientist and the lead of the Actinide, Geochemistry & Separation Sciences Theme within Argonne's Chemical Sciences and Engineering Division.... |
Lynda Soderholm : Soderholm was awarded her PhD in 1982 by McMaster University under the direction of Prof John Greedan. Her dissertation focused on characterizing the structural and magnetic properties of a series of ternary f-ion oxides. After graduating, she was awarded a NATO postdoctoral fellow at the Centre natio... |
Lynda Soderholm : University of Chicago Board of Governors' Distinguished Performance Award, 2009. Fellow of the American Association for the Advancement of Science, 2013. Argonne Distinguished Fellow, 2016 DOE materials sciences research competition for Outstanding Scientific Accomplishments in Solid State Physics, 19... |
Lynda Soderholm : Beno, M. A.; Soderholm, L.; Capone, D. W., II; Hinks, D. G.; Jorgensen, J. D.; Grace, J. D.; Schuller, I. K.; Segre, C. U.; Zhang, K., Structure of the single-phase high-temperature superconductor yttrium barium copper oxide (YBa2Cu3O7−δ). Appl. Phys. Lett. 1987, 51 (1), 57–9. Soderholm, L.; Zhang, K.... |
Lynda Soderholm : Solvent extraction system for plutonium colloids and other oxide nano-particles, (2016). |
Lynda Soderholm : Lynda Soderholm publications indexed by Google Scholar |
Types of artificial neural networks : There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and th... |
Types of artificial neural networks : In feedforward neural networks the information moves from the input to output directly in every layer. There can be hidden layers with or without cycles/loops to sequence inputs. Feedforward networks can be constructed with various types of units, such as binary McCulloch–Pitts neu... |
Types of artificial neural networks : Regulatory feedback networks account for feedback found throughout brain recognition processing areas. Instead of recognition-inference being feedforward (inputs-to-output) as in neural networks, regulatory feedback assumes inference iteratively compares inputs to outputs & neurons... |
Types of artificial neural networks : Radial basis functions are functions that have a distance criterion with respect to a center. Radial basis functions have been applied as a replacement for the sigmoidal hidden layer transfer characteristic in multi-layer perceptrons. RBF networks have two layers: In the first, inp... |
Types of artificial neural networks : A deep belief network (DBN) is a probabilistic, generative model made up of multiple hidden layers. It can be considered a composition of simple learning modules. A DBN can be used to generatively pre-train a deep neural network (DNN) by using the learned DBN weights as the initial... |
Types of artificial neural networks : Recurrent neural networks (RNN) propagate data forward, but also backwards, from later processing stages to earlier stages. RNN can be used as general sequence processors. |
Types of artificial neural networks : Biological studies have shown that the human brain operates as a collection of small networks. This realization gave birth to the concept of modular neural networks, in which several small networks cooperate or compete to solve problems. |
Types of artificial neural networks : A physical neural network includes electrically adjustable resistance material to simulate artificial synapses. Examples include the ADALINE memristor-based neural network. An optical neural network is a physical implementation of an artificial neural network with optical component... |
Types of artificial neural networks : Unlike static neural networks, dynamic neural networks adapt their structure and/or parameters to the input during inference showing time-dependent behaviour, such as transient phenomena and delay effects. Dynamic neural networks in which the parameters may change over time are rel... |
Types of artificial neural networks : Memory networks incorporate long-term memory. The long-term memory can be read and written to, with the goal of using it for prediction. These models have been applied in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge bas... |
Types of artificial neural networks : Fukushima, Kunihiko (1987). "A hierarchical neural network model for selective attention". In Eckmiller, R.; Von der Malsburg, C. (eds.). Neural computers. Springer-Verlag. pp. 81–90. Fukushima, Kunihiko (2007). "Neocognitron". Scholarpedia. 2 (1): 1717. Bibcode:2007SchpJ...2.1717F... |
ChatGPT : ChatGPT is a generative artificial intelligence chatbot developed by OpenAI and launched in 2022. It is currently based on the GPT-4o large language model (LLM). ChatGPT can generate human-like conversational responses and enables users to refine and steer a conversation towards a desired length, format, styl... |
ChatGPT : ChatGPT is based on particular GPT foundation models, namely GPT-4, GPT-4o and GPT-4o mini, that were fine-tuned to target conversational usage. The fine-tuning process leveraged supervised learning and reinforcement learning from human feedback (RLHF). Both approaches employed human trainers to improve model... |
ChatGPT : The following table lists the main model versions of ChatGPT, describing the significant changes included with each version: |
ChatGPT : OpenAI engineers have said that they had not expected ChatGPT to be very successful and were surprised by the coverage and attention that it received. ChatGPT was widely assessed in December 2022 as having some unprecedented and powerful capabilities. Kevin Roose of The New York Times called it "the best arti... |
ChatGPT : Intelligent agent – Software agent which acts autonomously Ethics of artificial intelligence – Challenges related to the responsible development and use of AI |
ChatGPT : Biswas, Som (April 1, 2023). "ChatGPT and the Future of Medical Writing". Radiology. 307 (2): e223312. doi:10.1148/radiol.223312. ISSN 0033-8419. PMID 36728748. S2CID 256501098. Chang, Kent K.; Cramer, Mackenzie; Soni, Sandeep; Bamman, David (April 28, 2023). "Speak, Memory: An Archaeology of Books Known to C... |
ChatGPT : Official website |
Catastrophic interference : Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to abruptly and drastically forget previously learned information upon learning new information. Neural networks are an important part of the connectionist approach to cognitive ... |
Catastrophic interference : The term catastrophic interference was originally coined by McCloskey and Cohen (1989) but was also brought to the attention of the scientific community by research from Ratcliff (1990). |
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