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Sentence embedding : In natural language processing, a sentence embedding is a representation of a sentence as a vector of numbers which encodes meaningful semantic information. State of the art embeddings are based on the learned hidden layer representation of dedicated sentence transformer models. BERT pioneered an a... |
Sentence embedding : In recent years, sentence embedding has seen a growing level of interest due to its applications in natural language queryable knowledge bases through the usage of vector indexing for semantic search. LangChain for instance utilizes sentence transformers for purposes of indexing documents. In parti... |
Sentence embedding : A way of testing sentence encodings is to apply them on Sentences Involving Compositional Knowledge (SICK) corpus for both entailment (SICK-E) and relatedness (SICK-R). In the best results are obtained using a BiLSTM network trained on the Stanford Natural Language Inference (SNLI) Corpus. The Pear... |
Sentence embedding : Distributional semantics Word embedding |
Sentence embedding : InferSent sentence embeddings and training code Universal Sentence Encoder Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning == References == |
Generalized Hebbian algorithm : The generalized Hebbian algorithm, also known in the literature as Sanger's rule, is a linear feedforward neural network for unsupervised learning with applications primarily in principal components analysis. First defined in 1989, it is similar to Oja's rule in its formulation and stabi... |
Generalized Hebbian algorithm : Consider a problem of learning a linear code for some data. Each data is a multi-dimensional vector x ∈ R n ^ , and can be (approximately) represented as a linear sum of linear code vectors w 1 , … , w m ,\dots ,w_ . When m = n , it is possible to exactly represent the data. If m < n ... |
Generalized Hebbian algorithm : The generalized Hebbian algorithm is used in applications where a self-organizing map is necessary, or where a feature or principal components analysis can be used. Examples of such cases include artificial intelligence and speech and image processing. Its importance comes from the fact ... |
Generalized Hebbian algorithm : Hebbian learning Factor analysis Contrastive Hebbian learning Oja's rule == References == |
ROCm : ROCm is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains, including general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), and heterogeneous computing. It offers several programming models: HIP (GP... |
ROCm : The first GPGPU software stack from ATI/AMD was Close to Metal, which became Stream. ROCm was launched around 2016 with the Boltzmann Initiative. ROCm stack builds upon previous AMD GPU stacks; some tools trace back to GPUOpen and others to the Heterogeneous System Architecture (HSA). |
ROCm : ROCm as a stack ranges from the kernel driver to the end-user applications. AMD has introductory videos about AMD GCN hardware, and ROCm programming via its learning portal. One of the best technical introductions about the stack and ROCm/HIP programming, remains, to date, to be found on Reddit. |
ROCm : ROCm is primarily targeted at discrete professional GPUs, but unofficial support includes the Vega family and RDNA 2 consumer GPUs. Accelerated Processor Units (APU) are "enabled", but not officially supported. Having ROCm functional there is involved. |
ROCm : There is one kernel-space component, ROCk, and the rest - there is roughly a hundred components in the stack - is made of user-space modules. The unofficial typographic policy is to use: uppercase ROC lowercase following for low-level libraries, i.e. ROCt, and the contrary for user-facing libraries, i.e. rocBLAS... |
ROCm : ROCm competes with other GPU computing stacks: Nvidia CUDA and Intel OneAPI. |
ROCm : AMD Software – a general overview of AMD's drivers, APIs, and development endeavors. GPUOpen – AMD's complementary graphics stack AMD Radeon Software – AMD's software distribution channel |
ROCm : "ROCm official documentation". AMD. February 10, 2022. "ROCm Learning Center". AMD. January 25, 2022. "ROCm official documentation on the github super-project". AMD. January 25, 2022. "ROCm official documentation - pre 5.0". AMD. January 19, 2022. "GPU-Accelerated Applications with AMD Instinct Accelerators & AM... |
PaLM : PaLM (Pathways Language Model) is a 540 billion-parameter dense decoder-only transformer-based large language model (LLM) developed by Google AI. Researchers also trained smaller versions of PaLM (with 8 and 62 billion parameters) to test the effects of model scale. PaLM is capable of a wide range of tasks, incl... |
PaLM : PaLM is pre-trained on a high-quality corpus of 780 billion tokens that comprise various natural language tasks and use cases. This dataset includes filtered webpages, books, Wikipedia articles, news articles, source code obtained from open source repositories on GitHub, and social media conversations. It is bas... |
PaLM : LaMDA, PaLM's predecessor Gemini, PaLM's successor Chinchilla == References == |
Probabilistic numerics : Probabilistic numerics is an active field of study at the intersection of applied mathematics, statistics, and machine learning centering on the concept of uncertainty in computation. In probabilistic numerics, tasks in numerical analysis such as finding numerical solutions for integration, lin... |
Probabilistic numerics : A numerical method is an algorithm that approximates the solution to a mathematical problem (examples below include the solution to a linear system of equations, the value of an integral, the solution of a differential equation, the minimum of a multivariate function). In a probabilistic numeri... |
Probabilistic numerics : The interplay between numerical analysis and probability is touched upon by a number of other areas of mathematics, including average-case analysis of numerical methods, information-based complexity, game theory, and statistical decision theory. Precursors to what is now being called "probabili... |
Probabilistic numerics : ProbNum: Probabilistic Numerics in Python. ProbNumDiffEq.jl: Probabilistic numerical ODE solvers based on filtering implemented in Julia. Emukit: Adaptable Python toolbox for decision-making under uncertainty. BackPACK: Built on top of PyTorch. It efficiently computes quantities other than the ... |
Probabilistic numerics : Average-case analysis Information-based complexity Uncertainty quantification == References == |
Hybrid intelligent system : Hybrid intelligent system denotes a software system which employs, in parallel, a combination of methods and techniques from artificial intelligence subfields, such as: Neuro-symbolic systems Neuro-fuzzy systems Hybrid connectionist-symbolic models Fuzzy expert systems Connectionist expert s... |
Hybrid intelligent system : AI alignment AI effect Applications of artificial intelligence Artificial intelligence systems integration Intelligent control Lists List of emerging technologies Outline of artificial intelligence |
Hybrid intelligent system : R. Sun & L. Bookman, (eds.), Computational Architectures Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994. http://www.cogsci.rpi.edu/~rsun/book2-ann.html Archived 2009-05-05 at the Wayback Machine S. Wermter and R. Sun, (eds.) Hybrid Neural Systems. Sp... |
GPT Store : The GPT Store is a platform developed by OpenAI that enables users and developers to create, publish, and monetize GPTs without requiring advanced programming skills. GPTs are custom applications built using the artificial intelligence chatbot known as ChatGPT. |
GPT Store : The GPT Store was announced in October 2023 and launched in January 2024. According to OpenAI, the platform aims to democratize access to advanced artificial intelligence and facilitate the creation of custom chatbot applications without requiring advanced programming skills. The platform has garnered atten... |
GPT Store : The GPT Store allows users to create and customize chatbots, known as GPTs, tailored to various needs such as customer service, personal assistance, video and image creation, and more. GPTs are categorized into various sections, including Programming, Education, and Research. The platform is designed to be ... |
GPT Store : Despite its initial success, the GPT Store has faced criticism concerning potential copyright violations. Some users and companies have expressed concerns about the use of AI-generated content that may infringe on intellectual property rights. For instance, a teacher has alleged that some students created G... |
Bayesian programming : Bayesian programming is a formalism and a methodology for having a technique to specify probabilistic models and solve problems when less than the necessary information is available. Edwin T. Jaynes proposed that probability could be considered as an alternative and an extension of logic for rati... |
Bayesian programming : A Bayesian program is a means of specifying a family of probability distributions. The constituent elements of a Bayesian program are presented below: Program (\pi )\\\\\\\end\\\delta )\end\\\end A program is constructed from a description and a question. A description is constructed using some s... |
Bayesian programming : The comparison between probabilistic approaches (not only bayesian programming) and possibility theories continues to be debated. Possibility theories like, for instance, fuzzy sets, fuzzy logic and possibility theory are alternatives to probability to model uncertainty. They argue that probabili... |
Bayesian programming : The purpose of probabilistic programming is to unify the scope of classical programming languages with probabilistic modeling (especially bayesian networks) to deal with uncertainty while profiting from the programming languages' expressiveness to encode complexity. Extended classical programming... |
Bayesian programming : Kamel Mekhnacha (2013). Bayesian Programming. Chapman and Hall/CRC. doi:10.1201/b16111. ISBN 978-1-4398-8032-6. |
Bayesian programming : A companion site to the Bayesian programming book where to download ProBT an inference engine dedicated to Bayesian programming. The Bayesian-programming.org site Archived 2013-11-23 at archive.today for the promotion of Bayesian programming with detailed information and numerous publications. |
Electricity price forecasting : Electricity price forecasting (EPF) is a branch of energy forecasting which focuses on using mathematical, statistical and machine learning models to predict electricity prices in the future. Over the last 30 years electricity price forecasts have become a fundamental input to energy com... |
Electricity price forecasting : The simplest model for day ahead forecasting is to ask each generation source to bid on blocks of generation and choose the cheapest bids. If not enough bids are submitted, the price is increased. If too many bids are submitted the price can reach zero or become negative. The offer price... |
Electricity price forecasting : Electricity cannot be stored as easily as gas, it is produced at the exact moment of demand. All of the factors of supply and demand will, therefore, have an immediate impact on the price of electricity on the spot market. In addition to production costs, electricity prices are set by su... |
Electricity price forecasting : A variety of methods and ideas have been tried for electricity price forecasting (EPF), with varying degrees of success. They can be broadly classified into six groups. |
Electricity price forecasting : It is customary to talk about short-, medium- and long-term forecasting, but there is no consensus in the literature as to what the thresholds should actually be: Short-term forecasting generally involves horizons from a few minutes up to a few days ahead, and is of prime importance in d... |
Electricity price forecasting : In his extensive review paper, Weron looks ahead and speculates on the directions EPF will or should take over the next decade or so: |
Electricity price forecasting : Energy forecasting Global Energy Forecasting Competitions == References == |
Controlled natural language : Controlled natural languages (CNLs) are subsets of natural languages that are obtained by restricting the grammar and vocabulary in order to reduce or eliminate ambiguity and complexity. Traditionally, controlled languages fall into two major types: those that improve readability for human... |
Controlled natural language : Existing controlled natural languages include: |
Controlled natural language : IETF has reserved simple as a BCP 47 variant subtag for simplified versions of languages. |
Controlled natural language : Controlled Natural Languages Archived 2021-03-08 at the Wayback Machine |
Hugging Face : Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law and based in New York City that develops computation tools for building applications using machine learning. It is most notable for its transformers library built for natural language processing applications... |
Hugging Face : The company was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf in New York City, originally as a company that developed a chatbot app targeted at teenagers. The company was named after the U+1F917 🤗 HUGGING FACE emoji. After open sourcing the model behind the ... |
Hugging Face : OpenAI Station F Kaggle |
Hugging Face : Official website |
Structural risk minimization : Structural risk minimization (SRM) is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be selected from a finite data set, with the consequent problem of overfitting – the model becoming too strongly tailored to the particularities ... |
Structural risk minimization : Vapnik–Chervonenkis theory Support vector machines Model selection Occam Learning Empirical risk minimization Ridge regression Regularization (mathematics) |
Structural risk minimization : Structural risk minimization at the support vector machines website. |
Toy problem : In scientific disciplines, a toy problem or a puzzlelike problem is a problem that is not of immediate scientific interest, yet is used as an expository device to illustrate a trait that may be shared by other, more complicated, instances of the problem, or as a way to explain a particular, more general, ... |
Toy problem : Blocks world Firing squad synchronization problem Monkey and banana problem Secretary problem |
Toy problem : "toy problem". The Jargon Lexicon. |
INDIAai : INDIAai is a web portal launched by the Government of India in May 2022 for artificial intelligence-related developments in India. It is known as the National AI Portal of India, which was jointly started by the Ministry of Electronics and Information Technology (MeitY), the National e-Governance Division (Ne... |
INDIAai : The portal was launched on 30 May 2020, by Ravi Shankar Prasad, the Union Minister for Electronics and IT, Law and Justice and Communications, on the first anniversary of the second tenure of Prime Minister Narendra Modi-led government. A national program for the youth, 'Responsible AI for Youth', was also la... |
INDIAai : It aims to function as a one-stop portal for all AI-related development in India. The platform publishes resources such as articles, news, interviews, and investment funding news and events for AI startups, AI companies, and educational firms related to artificial intelligence in India. It also distributes do... |
INDIAai : Official website |
Neural Turing machine : A neural Turing machine (NTM) is a recurrent neural network model of a Turing machine. The approach was published by Alex Graves et al. in 2014. NTMs combine the fuzzy pattern matching capabilities of neural networks with the algorithmic power of programmable computers. An NTM has a neural netwo... |
Connectionist temporal classification : Connectionist temporal classification (CTC) is a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks to tackle sequence problems where the timing is variable. It can be used for tasks like on-line hand... |
Connectionist temporal classification : Section 16.4, "CTC" in Jurafsky and Martin's Speech and Language Processing, 3rd edition Hannun, Awni (27 November 2017). "Sequence Modeling with CTC". Distill. 2 (11): e8. doi:10.23915/distill.00008. ISSN 2476-0757. |
Jpred : Jpred v.4 is the latest version of the JPred Protein Secondary Structure Prediction Server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction, that has existed since 1998 in different versions. In addition to protein secondary structure, JPred a... |
Jpred : The static HTML pages of JPred 2 are still available for reference. |
Jpred : The JPred v3 followed on from previous versions of JPred developed and maintained by James Cuff and Jonathan Barber (see JPred References). This release added new functionality and fixed many bugs. The highlights are: New, friendlier user interface Retrained and optimised version of Jnet (v2) - mean secondary s... |
Jpred : The current version of JPred (v4) has the following improvements and updates incorporated: Retrained on the latest UniRef90 and SCOPe/ASTRAL version of Jnet (v2.3.1) - mean secondary structure prediction accuracy of >82%. Upgraded the Web Server to the latest technologies (Bootstrap framework, JavaScript) and u... |
Jpred : PSIPRED List of protein structure prediction software == References == |
Word embedding : In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar ... |
Word embedding : In distributional semantics, a quantitative methodological approach for understanding meaning in observed language, word embeddings or semantic feature space models have been used as a knowledge representation for some time. Such models aim to quantify and categorize semantic similarities between lingu... |
Word embedding : Historically, one of the main limitations of static word embeddings or word vector space models is that words with multiple meanings are conflated into a single representation (a single vector in the semantic space). In other words, polysemy and homonymy are not handled properly. For example, in the se... |
Word embedding : Word embeddings for n-grams in biological sequences (e.g. DNA, RNA, and Proteins) for bioinformatics applications have been proposed by Asgari and Mofrad. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-v... |
Word embedding : Word embeddings with applications in game design have been proposed by Rabii and Cook as a way to discover emergent gameplay using logs of gameplay data. The process requires transcribing actions that occur during a game within a formal language and then using the resulting text to create word embeddin... |
Word embedding : The idea has been extended to embeddings of entire sentences or even documents, e.g. in the form of the thought vectors concept. In 2015, some researchers suggested "skip-thought vectors" as a means to improve the quality of machine translation. A more recent and popular approach for representing sente... |
Word embedding : Software for training and using word embeddings includes Tomáš Mikolov's Word2vec, Stanford University's GloVe, GN-GloVe, Flair embeddings, AllenNLP's ELMo, BERT, fastText, Gensim, Indra, and Deeplearning4j. Principal Component Analysis (PCA) and T-Distributed Stochastic Neighbour Embedding (t-SNE) are... |
Word embedding : Word embeddings may contain the biases and stereotypes contained in the trained dataset, as Bolukbasi et al. points out in the 2016 paper “Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings” that a publicly available (and popular) word2vec embedding trained on Google News... |
Word embedding : Embedding (machine learning) Brown clustering Distributional–relational database == References == |
Embodied agent : In artificial intelligence, an embodied agent, also sometimes referred to as an interface agent, is an intelligent agent that interacts with the environment through a physical body within that environment. Agents that are represented graphically with a body, for example a human or a cartoon animal, are... |
Embodied agent : Embodied conversational agents are a form of intelligent user interface. Graphically embodied agents aim to unite gesture, facial expression and speech to enable face-to-face communication with users, providing a powerful means of human-computer interaction. |
Embodied agent : Face-to-face communication allows communication protocols that give a much richer communication channel than other means of communicating. It enables pragmatic communication acts such as conversational turn-taking, facial expression of emotions, information structure and emphasis, visualisation and ico... |
Embodied agent : Bates, Joseph (1994), "The Role of Emotion in Believable Agents", Communications of the ACM, 37 (7): 122–125, CiteSeerX 10.1.1.47.8186, doi:10.1145/176789.176803, S2CID 207178664. Cassell, Justin (2000), "More than Just Another Pretty Face: Embodied Conversational Interface Agents" (PDF), Communication... |
Embodied agent : "AI Makes Strides in Virtual Worlds More Like Our Own". Quanta Magazine. June 24, 2022. |
Inception (deep learning architecture) : Inception is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1). The series was historically important as an early CNN that separates the stem (data ingest), body (data process... |
Inception (deep learning architecture) : A list of all Inception models released by Google: "models/research/slim/README.md at master · tensorflow/models". GitHub. Retrieved 2024-10-19. |
Kernel density estimation : In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights. KDE answers a fundamental data smoothing prob... |
Kernel density estimation : Let (x1, x2, ..., xn) be independent and identically distributed samples drawn from some univariate distribution with an unknown density f at any given point x. We are interested in estimating the shape of this function f. Its kernel density estimator is f ^ h ( x ) = 1 n ∑ i = 1 n K h ( x −... |
Kernel density estimation : Kernel density estimates are closely related to histograms, but can be endowed with properties such as smoothness or continuity by using a suitable kernel. The diagram below based on these 6 data points illustrates this relationship: For the histogram, first, the horizontal axis is divided i... |
Kernel density estimation : The bandwidth of the kernel is a free parameter which exhibits a strong influence on the resulting estimate. To illustrate its effect, we take a simulated random sample from the standard normal distribution (plotted at the blue spikes in the rug plot on the horizontal axis). The grey curve i... |
Kernel density estimation : Given the sample (x1, x2, ..., xn), it is natural to estimate the characteristic function φ(t) = E[eitX] as φ ^ ( t ) = 1 n ∑ j = 1 n e i t x j (t)=\sum _^e^ Knowing the characteristic function, it is possible to find the corresponding probability density function through the Fourier transfo... |
Kernel density estimation : We can extend the definition of the (global) mode to a local sense and define the local modes: M = (x)<0\ Namely, M is the collection of points for which the density function is locally maximized. A natural estimator of M is a plug-in from KDE, where g ( x ) and λ 1 ( x ) (x) are KDE ver... |
Kernel density estimation : A non-exhaustive list of software implementations of kernel density estimators includes: In Analytica release 4.4, the Smoothing option for PDF results uses KDE, and from expressions it is available via the built-in Pdf function. In C/C++, FIGTree is a library that can be used to compute ker... |
Kernel density estimation : Kernel (statistics) Kernel smoothing Kernel regression Density estimation (with presentation of other examples) Mean-shift Scale space: The triplets form a scale space representation of the data. Multivariate kernel density estimation Variable kernel density estimation Head/tail breaks |
Kernel density estimation : Härdle, Wolfgang; Müller, Marlene; Sperlich, Stefan; Werwatz, Axel (2004). Nonparametric and Semiparametric Models. Springer Series in Statistics. Berlin Heidelberg: Springer-Verlag. pp. 39–83. ISBN 978-3-540-20722-1. |
Kernel density estimation : Introduction to kernel density estimation A short tutorial which motivates kernel density estimators as an improvement over histograms. Kernel Bandwidth Optimization A free online tool that generates an optimized kernel density estimate. Free Online Software (Calculator) computes the Kernel ... |
Progress in artificial intelligence : Progress in artificial intelligence (AI) refers to the advances, milestones, and breakthroughs that have been achieved in the field of artificial intelligence over time. AI is a multidisciplinary branch of computer science that aims to create machines and systems capable of perform... |
Progress in artificial intelligence : There are many useful abilities that can be described as showing some form of intelligence. This gives better insight into the comparative success of artificial intelligence in different areas. AI, like electricity or the steam engine, is a general-purpose technology. There is no c... |
Progress in artificial intelligence : In his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark. The Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge ... |
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