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Data Science and Predictive Analytics : The first edition of the textbook Data Science and Predictive Analytics: Biomedical and Health Applications using R, authored by Ivo D. Dinov, was published in August 2018 by Springer. The second edition of the book was printed in 2023. This textbook covers some of the core mathe... |
Data Science and Predictive Analytics : The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer-reviewed in the Journal of the American Statistical Association, International Statistical Institute’s ISI Review Journal, and the Journal of the American Library Association. Many scholarly... |
Data Science and Predictive Analytics : DSPA textbook (1st edition) Springer website and SpringerLink EBook download DSPA textbook (2nd edition) Springer website is published in The Springer Series in Applied Machine Learning (SSAML) Textbook supporting website |
Physics-informed neural networks : Physics-informed neural networks (PINNs), also referred to as Theory-Trained Neural Networks (TTNs), are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial d... |
Physics-informed neural networks : Most of the physical laws that govern the dynamics of a system can be described by partial differential equations. For example, the Navier–Stokes equations are a set of partial differential equations derived from the conservation laws (i.e., conservation of mass, momentum, and energy)... |
Physics-informed neural networks : A general nonlinear partial differential equation can be: u t + N [ u ; λ ] = 0 , x ∈ Ω , t ∈ [ 0 , T ] +N[u;\lambda ]=0,\quad x\in \Omega ,\quad t\in [0,T] where u ( t , x ) denotes the solution, N [ ⋅ ; λ ] is a nonlinear operator parameterized by λ , and Ω is a subset of R D ^... |
Physics-informed neural networks : PINN is unable to approximate PDEs that have strong non-linearity or sharp gradients that commonly occur in practical fluid flow problems. Piece-wise approximation has been an old practice in the field of numerical approximation. With the capability of approximating strong non-lineari... |
Physics-informed neural networks : In the PINN framework, initial and boundary conditions are not analytically satisfied, thus they need to be included in the loss function of the network to be simultaneously learned with the differential equation (DE) unknown functions. Having competing objectives during the network's... |
Physics-informed neural networks : Regular PINNs are only able to obtain the solution of a forward or inverse problem on a single geometry. It means that for any new geometry (computational domain), one must retrain a PINN. This limitation of regular PINNs imposes high computational costs, specifically for a comprehens... |
Physics-informed neural networks : Physics-informed neural networks (PINNs) have proven particularly effective in solving inverse problems within differential equations, demonstrating their applicability across science, engineering, and economics. They have shown useful for solving inverse problems in a variety of fiel... |
Physics-informed neural networks : Surrogate networks are intended for the unknown functions, namely, the components of the strain and the stress tensors as well as the unknown displacement field, respectively. The residual network provides the residuals of the partial differential equations (PDEs) and of the boundary ... |
Physics-informed neural networks : Deep backward stochastic differential equation method is a numerical method that combines deep learning with Backward stochastic differential equation (BSDE) to solve high-dimensional problems in financial mathematics. By leveraging the powerful function approximation capabilities of ... |
Physics-informed neural networks : An extension or adaptation of PINNs are Biologically-informed neural networks (BINNs). BINNs introduce two key adaptations to the typical PINN framework: (i) the mechanistic terms of the governing PDE are replaced by neural networks, and (ii) the loss function L t o t is modified to ... |
Physics-informed neural networks : Translation and discontinuous behavior are hard to approximate using PINNs. They fail when solving differential equations with slight advective dominance and hence asymptotic behaviour causes the method to fail. Such PDEs could be solved by scaling variables. This difficulty in traini... |
Physics-informed neural networks : Physics Informed Neural Network PINN – repository to implement physics-informed neural network in Python XPINN – repository to implement extended physics-informed neural network (XPINN) in Python PIPN [2]– repository to implement physics-informed PointNet (PIPN) in Python |
List of programming languages for artificial intelligence : Historically, some programming languages have been specifically designed for artificial intelligence (AI) applications. Nowadays, many general-purpose programming languages also have libraries that can be used to develop AI applications. |
List of programming languages for artificial intelligence : Python is a high-level, general-purpose programming language that is popular in artificial intelligence. It has a simple, flexible and easily readable syntax. Its popularity results in a vast ecosystem of libraries, including for deep learning, such as PyTorch... |
List of programming languages for artificial intelligence : Prolog is a declarative language where programs are expressed in terms of relations, and execution occurs by running queries over these relations. Prolog is particularly useful for symbolic reasoning, database and language parsing applications. Artificial Inte... |
List of programming languages for artificial intelligence : Glossary of artificial intelligence List of constraint programming languages List of computer algebra systems List of logic programming languages List of constructed languages Fifth-generation programming language |
Lexical simplification : Lexical simplification is a sub-task of text simplification. It can be defined as any lexical substitution task that reduces text complexity. |
Lexical simplification : Lexical substitution Text simplification |
Lexical simplification : Advaith Siddharthan. "Syntactic Simplification and Text Cohesion". In Research on Language and Computation, Volume 4, Issue 1, Jun 2006, Pages 77–109, Springer Science, the Netherlands. Siddhartha Jonnalagadda, Luis Tari, Joerg Hakenberg, Chitta Baral and Graciela Gonzalez. Towards Effective Se... |
Artificial wisdom : Artificial wisdom (AW) is an artificial intelligence (AI) system which is able to display the human traits of wisdom and morals while being able to contemplate its own “endpoint”. Artificial wisdom can be described as artificial intelligence reaching the top-level of decision-making when confronted ... |
Artificial wisdom : There are no universal or standardized definitions for human intelligence, artificial intelligence, human wisdom, or artificial wisdom. However, the DIKW pyramid, describes the continuum of relationship between data, information, knowledge, and wisdom, puts wisdom at the highest level in its hierarc... |
Artificial wisdom : There are notable problems with attempting to create an artificially wise system. Consciousness, autonomy, and will are considered strictly human features. |
Artificial wisdom : == Further reading == |
Powerset (company) : Powerset was an American company based in San Francisco, California, that, in 2006, was developing a natural language search engine for the Internet. On July 1, 2008, Powerset was acquired by Microsoft for an estimated $100 million (~$139 million in 2023). Powerset was working on building a natural... |
Powerset (company) : In a form of beta testing, Powerset opened an online community called Powerlabs on September 17, 2007. Business Week said: "The company hopes the site will marshal thousands of people to help build and improve its search engine before it goes public next year." Said The New York Times: "[Powerset L... |
Powerset (company) : Barney Pell (born March 18, 1968, in Hollywood, California) was co-founder and CEO of Powerset. Pell received his Bachelor of Science degree in symbolic systems from Stanford University in 1989, where he graduated Phi Beta Kappa and was a National Merit Scholar. Pell received a PhD in computer scie... |
Powerset (company) : Powerset attracted a wide range of investors, many of whom had considerable experience in the venture capital field. The company received $12.5 million (~$17.7 million in 2023) in Series A funding during November 2007, co-led by the venture capital firms Foundation Capital and The Founders Fund. Am... |
Powerset (company) : Bing (search engine) Apache HBase |
Powerset (company) : Powerset main web site - redirects to Bing Powerset acquired by Microsoft |
Luminoso : Luminoso is a Cambridge, MA-based text analytics and artificial intelligence company. It spun out of the MIT Media Lab and its crowd-sourced Open Mind Common Sense (OMCS) project. The company has raised $20.6 million in financing, and its clients include Sony, Autodesk, Scotts Miracle-Gro, and GlaxoSmithKlin... |
Luminoso : Luminoso was co-founded in 2010 by Dennis Clark, Jason Alonso, Robyn Speer, and Catherine Havasi, a research scientist at MIT in artificial intelligence and computational linguistics. The company builds on the knowledge base of MIT’s Open Mind Common Sense (OMCS) project, co-founded in 1999 by Havasi, who co... |
Luminoso : The company uses artificial intelligence, natural language processing, and machine learning to derive insights from unstructured data such as contact center interactions, chatbot and live chat transcripts, product reviews, open-ended survey responses, and email. Luminoso's software identifies and quantifies ... |
Luminoso : Luminoso's technology can be accessed via two products: Luminoso Daylight and Luminoso Compass. Luminoso Daylight enables a deep-dive analysis into batch or real-time data, whereas Luminoso Compass automates the categorization of real-time data. Both products offer a user interface as well as an API. Luminos... |
Luminoso : Luminoso continues to actively conduct research in natural language processing and word embeddings and regularly participates in evaluations such as SemEval. At SemEval 2017, Luminoso participated in Task 2, measuring the semantic similarity of word pairs within and across five languages. Its solution outper... |
Luminoso : Luminoso has been listed as a "Cool Vendor in AI for Marketing" by Gartner, and has also been named a "Boston Artificial Intelligence Startup to Watch" by BostInno. In May 2017, Luminoso was recognized as having the Best Application for AI in the Enterprise by AI Business, and was also shortlisted as the Bes... |
Luminoso : Major competitors include Clarabridge and Lexalytics. |
Luminoso : The company raised $1.5 million from angel investors in 2012. Its first institutional funding round of $6.5 was completed in July 2014, led by Acadia Woods with participation from Japan’s Digital Garage. The company followed that with a $10M series B funding round in December 2018, led by DVI Equity Partners... |
Luminoso : Official website |
Lifelong Planning A* : LPA* or Lifelong Planning A* is an incremental heuristic search algorithm based on A*. It was first described by Sven Koenig and Maxim Likhachev in 2001. |
Lifelong Planning A* : LPA* is an incremental version of A*, which can adapt to changes in the graph without recalculating the entire graph, by updating the g-values (distance from start) from the previous search during the current search to correct them when necessary. Like A*, LPA* uses a heuristic, which is a lower ... |
Lifelong Planning A* : This code assumes a priority queue queue, which supports the following operations: topKey() returns the (numerically) lowest priority of any node in the queue (or infinity if the queue is empty) pop() removes the node with the lowest priority from the queue and returns it insert(node, priority) i... |
Lifelong Planning A* : Being algorithmically similar to A*, LPA* shares many of its properties. Each node is expanded (visited) at most twice for each run of LPA*. Locally overconsistent nodes are expanded at most once per LPA* run, thus its initial run (in which every node enters the overconsistent state) has similar ... |
Lifelong Planning A* : D* Lite, a reimplementation of the D* algorithm based on LPA* == References == |
GPT-J : GPT-J or GPT-J-6B is an open-source large language model (LLM) developed by EleutherAI in 2021. As the name suggests, it is a generative pre-trained transformer model designed to produce human-like text that continues from a prompt. The optional "6B" in the name refers to the fact that it has 6 billion paramete... |
GPT-J : GPT-J is a GPT-3-like model with 6 billion parameters. Like GPT-3, it is an autoregressive, decoder-only transformer model designed to solve natural language processing (NLP) tasks by predicting how a piece of text will continue. Its architecture differs from GPT-3 in three main ways. The attention and feedforw... |
GPT-J : GPT-J was designed to generate English text from a prompt. It was not designed for translating or generating text in other languages or for performance without first fine-tuning the model for a specific task. Nonetheless, GPT-J performs reasonably well even without fine-tuning, even in translation (at least fro... |
GPT-J : The untuned GPT-J is available on EleutherAI's website, NVIDIA's Triton Inference Server, and NLP Cloud's website. Cerebras and Amazon Web Services offer services to fine-tune the GPT-J model for company-specific tasks. Graphcore offers both fine-tuning and hosting services for the untuned GPT-J, as well as off... |
Latent diffusion model : The Latent Diffusion Model (LDM) is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) group at LMU Munich. Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training im... |
Latent diffusion model : Diffusion models were introduced in 2015 as a method to learn a model that can sample from a highly complex probability distribution. They used techniques from non-equilibrium thermodynamics, especially diffusion. It was accompanied by a software implementation in Theano. A 2019 paper proposed ... |
Latent diffusion model : While the LDM can work for generating arbitrary data conditional on arbitrary data, for concreteness, we describe its operation in conditional text-to-image generation. LDM consists of a variational autoencoder (VAE), a modified U-Net, and a text encoder. The VAE encoder compresses the image fr... |
Latent diffusion model : The LDM is trained by using a Markov chain to gradually add noise to the training images. The model is then trained to reverse this process, starting with a noisy image and gradually removing the noise until it recovers the original image. More specifically, the training process can be describe... |
Latent diffusion model : Diffusion model Generative adversarial network Variational autoencoder Stable Diffusion |
Latent diffusion model : Wang, Phil (2024-09-07). "lucidrains/denoising-diffusion-pytorch". GitHub. Retrieved 2024-09-07. "The Annotated Diffusion Model". huggingface.co. Retrieved 2024-09-07. "U-Net for Stable Diffusion". U-Net for Stable Diffusion. Retrieved 2024-08-31. "Transformer for Stable Diffusion U-Net". Trans... |
Relational data mining : Relational data mining is the data mining technique for relational databases. Unlike traditional data mining algorithms, which look for patterns in a single table (propositional patterns), relational data mining algorithms look for patterns among multiple tables (relational patterns). For most ... |
Relational data mining : Multi-Relation Association Rules: Multi-Relation Association Rules (MRAR) is a new class of association rules which in contrast to primitive, simple and even multi-relational association rules (that are usually extracted from multi-relational databases), each rule item consists of one entity bu... |
Relational data mining : Safarii: a Data Mining environment for analysing large relational databases based on a multi-relational data mining engine. Dataconda: a software, free for research and teaching purposes, that helps mining relational databases without the use of SQL. |
Relational data mining : Relational dataset repository: a collection of publicly available relational datasets. |
Relational data mining : Data mining Structure mining Database mining |
Relational data mining : Web page for a text book on relational data mining |
Connectionist expert system : Connectionist expert systems are artificial neural network (ANN) based expert systems where the ANN generates inferencing rules e.g., fuzzy-multi layer perceptron where linguistic and natural form of inputs are used. Apart from that, rough set theory may be used for encoding knowledge in t... |
Connectionist expert system : Sun, Ron (1994). Integrating rules and connectionism for robust commonsense reasoning. Hoboken, N.J: Wiley & Sons. ISBN 0-471-59324-9. |
Connectionist expert system : Gallant, Stephen I. (February 1988). "Connectionist expert systems". Comm. ACM. 31 (2): 152–69. doi:10.1145/42372.42377. S2CID 12971665. resource page: http://www.cogsci.rpi.edu/~rsun/reason.html Leão Bde F, Reátegui EB (1993). "HYCONES: a hybrid connectionist expert system". Proc Annu Sym... |
Hard sigmoid : In artificial intelligence, especially computer vision and artificial neural networks, a hard sigmoid is non-smooth function used in place of a sigmoid function. These retain the basic shape of a sigmoid, rising from 0 to 1, but using simpler functions, especially piecewise linear functions or piecewise ... |
Hard sigmoid : The most extreme examples are the sign function or Heaviside step function, which go from −1 to 1 or 0 to 1 (which to use depends on normalization) at 0. Other examples include the Theano library, which provides two approximations: ultra_fast_sigmoid, which is a multi-part piecewise approximation and har... |
Perceiver : Perceiver is a variant of the Transformer architecture, adapted for processing arbitrary forms of data, such as images, sounds and video, and spatial data. Unlike previous notable Transformer systems such as BERT and GPT-3, which were designed for text processing, the Perceiver is designed as a general arch... |
Perceiver : Perceiver is designed without modality-specific elements. For example, it does not have elements specialized to handle images, or text, or audio. Further it can handle multiple correlated input streams of heterogeneous types. It uses a small set of latent units that forms an attention bottleneck through whi... |
Perceiver : Perceiver's performance is comparable to ResNet-50 and ViT on ImageNet without 2D convolutions. It attends to 50,000 pixels. It is competitive in all modalities in AudioSet. |
Perceiver : Convolutional neural network Transformer (machine learning model) |
Perceiver : DeepMind Perceiver and Perceiver IO | Paper Explained on YouTube Perceiver: General Perception with Iterative Attention (Google DeepMind Research Paper Explained) on YouTube, with the Fourier features explained in more detail |
Instance-based learning : In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Because computation is ... |
Instance-based learning : Analogical modeling == References == |
Pruning (artificial neural network) : In deep learning, pruning is the practice of removing parameters from an existing artificial neural network. The goal of this process is to reduce the size (parameter count) of the neural network (and therefore the computational resources required to run it) whilst maintaining accu... |
Pruning (artificial neural network) : A basic algorithm for pruning is as follows: Evaluate the importance of each neuron. Rank the neurons according to their importance (assuming there is a clearly defined measure for "importance"). Remove the least important neuron. Check a termination condition (to be determined by ... |
Pruning (artificial neural network) : Most work on neural network pruning focuses on removing weights, namely, setting their values to zero. Early work suggested to also change the values of non-pruned weights. |
Pruning (artificial neural network) : Knowledge distillation Neural Darwinism == References == |
Energy-based model : An energy-based model (EBM) (also called Canonical Ensemble Learning or Learning via Canonical Ensemble – CEL and LCE, respectively) is an application of canonical ensemble formulation from statistical physics for learning from data. The approach prominently appears in generative artificial intelli... |
Energy-based model : For a given input x , the model describes an energy E θ ( x ) (x) such that the Boltzmann distribution P θ ( x ) = exp ( − β E θ ( x ) ) / Z ( θ ) (x)=\exp(-\beta E_(x))/Z(\theta ) is a probability (density), and typically β = 1 . Since the normalization constant: Z ( θ ) := ∫ x ∈ X exp ( − β... |
Energy-based model : The term "energy-based models" was first coined in a 2003 JMLR paper where the authors defined a generalisation of independent components analysis to the overcomplete setting using EBMs. Other early work on EBMs proposed models that represented energy as a composition of latent and observable varia... |
Energy-based model : EBMs demonstrate useful properties: Simplicity and stability–The EBM is the only object that needs to be designed and trained. Separate networks need not be trained to ensure balance. Adaptive computation time–An EBM can generate sharp, diverse samples or (more quickly) coarse, less diverse samples... |
Energy-based model : On image datasets such as CIFAR-10 and ImageNet 32x32, an EBM model generated high-quality images relatively quickly. It supported combining features learned from one type of image for generating other types of images. It was able to generalize using out-of-distribution datasets, outperforming flow... |
Energy-based model : Target applications include natural language processing, robotics and computer vision. The first energy-based generative neural network is the generative ConvNet proposed in 2016 for image patterns, where the neural network is a convolutional neural network. The model has been generalized to variou... |
Energy-based model : EBMs compete with techniques such as variational autoencoders (VAEs), generative adversarial networks (GANs) or normalizing flows. |
Energy-based model : Empirical likelihood Posterior predictive distribution Contrastive learning |
Energy-based model : Implicit Generation and Generalization in Energy-Based Models Yilun Du, Igor Mordatch https://arxiv.org/abs/1903.08689 Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One, Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin S... |
Energy-based model : "CIAR NCAP Summer School". www.cs.toronto.edu. Retrieved 2019-12-27. Dayan, Peter; Hinton, Geoffrey; Neal, Radford; Zemel, Richard S. (1999), "Helmholtz Machine", Unsupervised Learning, The MIT Press, doi:10.7551/mitpress/7011.003.0017, ISBN 978-0-262-28803-3 Hinton, Geoffrey E. (August 2002). "Tra... |
Machine translation software usability : The sections below give objective criteria for evaluating the usability of machine translation software output. |
Machine translation software usability : Do repeated translations converge on a single expression in both languages? I.e. does the translation method show stationarity or produce a canonical form? Does the translation become stationary without losing the original meaning? This metric has been criticized as not being we... |
Machine translation software usability : Is the system adaptive to colloquialism, argot or slang? The French language has many rules for creating words in the speech and writing of popular culture. Two such rules are: (a) The reverse spelling of words such as femme to meuf. (This is called verlan.) (b) The attachment o... |
Machine translation software usability : Is the output grammatical or well-formed in the target language? Using an interlingua should be helpful in this regard, because with a fixed interlingua one should be able to write a grammatical mapping to the target language from the interlingua. Consider the following Arabic l... |
Machine translation software usability : Do repeated re-translations preserve the semantics of the original sentence? For example, consider the following English input passed multiple times into and out of French using the Google translator as of 27 December 2006: Better a day earlier than a day late. ==> Améliorer un ... |
Machine translation software usability : An interesting peculiarity of Google Translate as of 24 January 2008 (corrected as of 25 January 2008) is the following result when translating from English to Spanish, which shows an embedded joke in the English-Spanish dictionary which has some added poignancy given recent eve... |
Machine translation software usability : Comparison of machine translation applications Evaluation of machine translation Round-trip translation Translation |
Machine translation software usability : Gimenez, Jesus and Enrique Amigo. (2005) IQmt: A framework for machine translation evaluation. NIST. Annual machine translation system evaluations and evaluation plan. Papineni, Kishore, Salim Roukos, Todd Ward and Wei-Jing Zhu. (2002) BLEU: A Method for automatic evaluation of ... |
Retrieval-augmented generation : Retrieval-augmented generation (RAG) is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a spec... |
Retrieval-augmented generation : In June 2024, Ars Technica reported, "But LLMs aren’t humans, of course. Their training data can age quickly, particularly in more time-sensitive queries. In addition, the LLM often can’t distinguish specific sources of its knowledge, as all its training data is blended together into a ... |
Retrieval-augmented generation : Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating an information-retrieval mechanism that allows models to access and utilize additional data beyond their original training set. AWS states, "RAG allows LLMs to retrieve relevant information from ... |
Retrieval-augmented generation : Improvements to the basic process above can be applied at different stages in the RAG flow. |
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