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ChatScript : Some data in ChatScript is transient, meaning it will disappear at the end of the current volley. Other data is permanent, lasting forever until explicitly killed off. Data can be local to a single user or shared across all users at the bot level. Internally all data is represented as text and is automatic... |
ChatScript : ChatScript embeds the Curl library and can directly read and write facts in JSON to a website. |
ChatScript : A ChatScript engine can run in local or server mode. |
ChatScript : ChatScript comes with a copy of English WordNet embedded within, including its ontology, and creates and extends its own ontology via concept declarations. It has an English language pos-tagger and parser and supports integration with TreeTagger for pos-tagging a number of other languages (TreeTagger comme... |
ChatScript : In addition to an internal fact database, ChatScript supports PostgreSQL, MySQL, MSSQL and MongoDB both for access by scripts, but also as a central filesystem if desired so ChatScript can be scaled horizontally. A common use case is to use a centralized database to host the user files and multiple servers... |
ChatScript : ChatScript also embeds DukTape, ECMAScript E5/E5.1 compatibility, with some semantics updated from ES2015+. |
ChatScript : ChatScript has built-in automatic spell checking, which can be augmented in script as both simple word replacements or context sensitive changes. With appropriate simple rules you can change perfect legal words into other words or delete them. E.g., if you have a concept of ~electronic_goods and don't want... |
ChatScript : A chatbot's control flow is managed by the control script. This is merely another ordinary topic of rules, that invokes API functions of the engine. Thus control is fully configurable by the scripter (and functions exist to allow introspection into the engine). There are pre-processing control flow and pos... |
ChatScript : ChatScript on GitHub |
Knowledge compilation : Knowledge compilation is a family of approaches for addressing the intractability of a number of artificial intelligence problems. A propositional model is compiled in an off-line phase in order to support some queries in polynomial time. Many ways of compiling a propositional model exist. Diffe... |
Knowledge compilation : Some examples of diagram classes include OBDDs, FBDDs, and non-deterministic OBDDs, as well as MDD. Some examples of formula classes include DNF and CNF. Examples of circuit classes include NNF, DNNF, d-DNNF, and SDD. |
Knowledge compilation : c2d: supports compilation to d-DNNF d4: supports compilation to d-DNNF miniC2D: supports compilation to SDD KCBox: supports compilation to OBDD, OBDD[AND], and CCDD == References == |
Multiple instance learning : In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances. In the simple case of multiple-instance binary classifi... |
Multiple instance learning : Depending on the type and variation in training data, machine learning can be roughly categorized into three frameworks: supervised learning, unsupervised learning, and reinforcement learning. Multiple instance learning (MIL) falls under the supervised learning framework, where every traini... |
Multiple instance learning : Keeler et al., in his work in the early 1990s was the first one to explore the area of MIL. The actual term multi-instance learning was introduced in the middle of the 1990s, by Dietterich et al. while they were investigating the problem of drug activity prediction. They tried to create a l... |
Multiple instance learning : Take image classification for example Amores (2013). Given an image, we want to know its target class based on its visual content. For instance, the target class might be "beach", where the image contains both "sand" and "water". In MIL terms, the image is described as a bag X = ,..,X_\ , ... |
Multiple instance learning : If the space of instances is X , then the set of bags is the set of functions N X = ^=\\rightarrow \mathbb \ , which is isomorphic to the set of multi-subsets of X . For each bag B ∈ N X ^ and each instance x ∈ X , B ( x ) is viewed as the number of times x occurs in B . Let Y be... |
Multiple instance learning : Most of the work on multiple instance learning, including Dietterich et al. (1997) and Maron & Lozano-Pérez (1997) early papers, make the assumption regarding the relationship between the instances within a bag and the class label of the bag. Because of its importance, that assumption is of... |
Multiple instance learning : There are two major flavors of algorithms for Multiple Instance Learning: instance-based and metadata-based, or embedding-based algorithms. The term "instance-based" denotes that the algorithm attempts to find a set of representative instances based on an MI assumption and classify future b... |
Multiple instance learning : So far this article has considered multiple instance learning exclusively in the context of binary classifiers. However, the generalizations of single-instance binary classifiers can carry over to the multiple-instance case. One such generalization is the multiple-instance multiple-label pr... |
Multiple instance learning : Supervised learning Multi-label classification |
Multiple instance learning : Recent reviews of the MIL literature include: Amores (2013), which provides an extensive review and comparative study of the different paradigms, Foulds & Frank (2010), which provides a thorough review of the different assumptions used by different paradigms in the literature. Dietterich, T... |
Personoid : Personoid is the concept coined by Stanisław Lem, a Polish science-fiction writer, in Non Serviam, from his book A Perfect Vacuum (1971). His personoids are an abstraction of functions of human mind and they live in computers; they do not need any human-like physical body. In cognitive and software modeling... |
Personoid : Welt am Draht (1973) The Thirteenth Floor (1999) |
Personoid : Android Humanoid Intelligence Artificial Intelligence Culture Computer Science Cognitive Science Anticipatory science Memetics |
Personoid : Stanisław Lem's book Próżnia Doskonała (1971). The collection of book reviews of nonexistent books. Translated into English by Michael Kandel as A Perfect Vacuum (1983). Personetics. Personoids Organizations Framework: An Approach to Highly Autonomous Software Architectures Archived 2006-09-28 at the Waybac... |
Convolutional neural network : A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images a... |
Convolutional neural network : A convolutional neural network consists of an input layer, hidden layers and an output layer. In a convolutional neural network, the hidden layers include one or more layers that perform convolutions. Typically this includes a layer that performs a dot product of the convolution kernel wi... |
Convolutional neural network : CNN are often compared to the way the brain achieves vision processing in living organisms. |
Convolutional neural network : In the past, traditional multilayer perceptron (MLP) models were used for image recognition. However, the full connectivity between nodes caused the curse of dimensionality, and was computationally intractable with higher-resolution images. A 1000×1000-pixel image with RGB color channels ... |
Convolutional neural network : A CNN architecture is formed by a stack of distinct layers that transform the input volume into an output volume (e.g. holding the class scores) through a differentiable function. A few distinct types of layers are commonly used. These are further discussed below. |
Convolutional neural network : Hyperparameters are various settings that are used to control the learning process. CNNs use more hyperparameters than a standard multilayer perceptron (MLP). |
Convolutional neural network : It is commonly assumed that CNNs are invariant to shifts of the input. Convolution or pooling layers within a CNN that do not have a stride greater than one are indeed equivariant to translations of the input. However, layers with a stride greater than one ignore the Nyquist–Shannon sampl... |
Convolutional neural network : The accuracy of the final model is typically estimated on a sub-part of the dataset set apart at the start, often called a test set. Alternatively, methods such as k-fold cross-validation are applied. Other strategies include using conformal prediction. |
Convolutional neural network : Regularization is a process of introducing additional information to solve an ill-posed problem or to prevent overfitting. CNNs use various types of regularization. |
Convolutional neural network : Pooling loses the precise spatial relationships between high-level parts (such as nose and mouth in a face image). These relationships are needed for identity recognition. Overlapping the pools so that each feature occurs in multiple pools, helps retain the information. Translation alone ... |
Convolutional neural network : For many applications, training data is not very available. Convolutional neural networks usually require a large amount of training data in order to avoid overfitting. A common technique is to train the network on a larger data set from a related domain. Once the network parameters have ... |
Convolutional neural network : End-to-end training and prediction are common practice in computer vision. However, human interpretable explanations are required for critical systems such as a self-driving cars. With recent advances in visual salience, spatial attention, and temporal attention, the most critical spatial... |
Convolutional neural network : Caffe: A library for convolutional neural networks. Created by the Berkeley Vision and Learning Center (BVLC). It supports both CPU and GPU. Developed in C++, and has Python and MATLAB wrappers. Deeplearning4j: Deep learning in Java and Scala on multi-GPU-enabled Spark. A general-purpose ... |
Convolutional neural network : Attention (machine learning) Convolution Deep learning Natural-language processing Neocognitron Scale-invariant feature transform Time delay neural network Vision processing unit |
Convolutional neural network : CS231n: Convolutional Neural Networks for Visual Recognition — Andrej Karpathy's Stanford computer science course on CNNs in computer vision vdumoulin/conv_arithmetic: A technical report on convolution arithmetic in the context of deep learning. Animations of convolutions. |
Efficiently updatable neural network : In computer strategy games, for example in shogi and chess, an efficiently updatable neural network (NNUE, a Japanese wordplay on Nue, sometimes stylised as ƎUИИ) is a neural network-based evaluation function whose inputs are piece-square tables, or variants thereof like the king-... |
Efficiently updatable neural network : elmo (shogi engine) Stockfish chess engine - The chapter about NNUE features a visualization of NNUE. List of chess software |
Efficiently updatable neural network : NNUE on the Chess Programming Wiki. NNUE evaluation functions for computer shogi on github.com |
Evolvability (computer science) : The term evolvability is used for a recent framework of computational learning introduced by Leslie Valiant in his paper of the same name and described below. The aim of this theory is to model biological evolution and categorize which types of mechanisms are evolvable. Evolution is an... |
Evolvability (computer science) : Let F n \, and R n \, be collections of functions on n variables. Given an ideal function f ∈ F n , the goal is to find by local search a representation r ∈ R n that closely approximates f . This closeness is measured by the performance Perf ( f , r ) (f,r) of r with respect to... |
Evolvability (computer science) : The class of conjunctions and the class of disjunctions are evolvable over the uniform distribution for short conjunctions and disjunctions, respectively. The class of parity functions (which evaluate to the parity of the number of true literals in a given subset of literals) are not e... |
Evolvability (computer science) : Valiant, L. G. (2006), Evolvability, ECCC TR06-120. |
Feed forward (control) : A feed forward (sometimes written feedforward) is an element or pathway within a control system that passes a controlling signal from a source in its external environment to a load elsewhere in its external environment. This is often a command signal from an external operator. In control engine... |
Feed forward (control) : With feed-forward or feedforward control, the disturbances are measured and accounted for before they have time to affect the system. In the house example, a feed-forward system may measure the fact that the door is opened and automatically turn on the heater before the house can get too cold. ... |
Feed forward (control) : Historically, the use of the term feedforward is found in works by Harold S. Black in US patent 1686792 (invented 17 March 1923) and D. M. MacKay as early as 1956. While MacKay's work is in the field of biological control theory, he speaks only of feedforward systems. MacKay does not mention fe... |
Feed forward (control) : The benefits of feedforward control are significant and can often justify the extra cost, time and effort required to implement the technology. Control accuracy can often be improved by as much as an order of magnitude if the mathematical model is of sufficient quality and implementation of the... |
Feed forward (control) : The mathematical model of the plant (machine, process or organism) used by the feedforward control system may be created and input by a control engineer or it may be learned by the control system. Control systems capable of learning and/or adapting their mathematical model have become more prac... |
Feed forward (control) : Black box Smith predictor |
Feed forward (control) : == Further reading == |
Pulse-coupled networks : Pulse-coupled networks or pulse-coupled neural networks (PCNNs) are neural models proposed by modeling a cat's visual cortex, and developed for high-performance biomimetic image processing. In 1989, Eckhorn introduced a neural model to emulate the mechanism of cat's visual cortex. The Eckhorn m... |
Pulse-coupled networks : PCNNs are useful for image processing, as discussed in a book by Thomas Lindblad and Jason M. Kinser. PCNNs have been used in a variety of image processing applications, including: image segmentation, pattern recognition, feature generation, face extraction, motion detection, region growing, im... |
Explicit semantic analysis : In natural language processing and information retrieval, explicit semantic analysis (ESA) is a vectoral representation of text (individual words or entire documents) that uses a document corpus as a knowledge base. Specifically, in ESA, a word is represented as a column vector in the tf–id... |
Explicit semantic analysis : To perform the basic variant of ESA, one starts with a collection of texts, say, all Wikipedia articles; let the number of documents in the collection be N. These are all turned into "bags of words", i.e., term frequency histograms, stored in an inverted index. Using this inverted index, on... |
Explicit semantic analysis : ESA, as originally posited by Gabrilovich and Markovitch, operates under the assumption that the knowledge base contains topically orthogonal concepts. However, it was later shown by Anderka and Stein that ESA also improves the performance of information retrieval systems when it is based n... |
Explicit semantic analysis : Cross-language explicit semantic analysis (CL-ESA) is a multilingual generalization of ESA. CL-ESA exploits a document-aligned multilingual reference collection (e.g., again, Wikipedia) to represent a document as a language-independent concept vector. The relatedness of two documents in dif... |
Explicit semantic analysis : Explicit semantic analysis on Evgeniy Gabrilovich's homepage; has links to implementations |
Empirical dynamic modeling : Empirical dynamic modeling (EDM) is a framework for analysis and prediction of nonlinear dynamical systems. Applications include population dynamics, ecosystem service, medicine, neuroscience, dynamical systems, geophysics, and human-computer interaction. EDM was originally developed by Rob... |
Empirical dynamic modeling : Mathematical models have tremendous power to describe observations of real-world systems. They are routinely used to test hypothesis, explain mechanisms and predict future outcomes. However, real-world systems are often nonlinear and multidimensional, in some instances rendering explicit eq... |
Empirical dynamic modeling : EDM is continuing to evolve. As of 2022, the main algorithms are Simplex projection, Sequential locally weighted global linear maps (S-Map) projection, Multivariate embedding in Simplex or S-Map, Convergent cross mapping (CCM), and Multiview Embeding, described below. Nearest neighbors are ... |
Empirical dynamic modeling : Extensions to EDM techniques include: Generalized Theorems for Nonlinear State Space Reconstruction Extended Convergent Cross Mapping Dynamic stability S-Map regularization Visual analytics with EDM Convergent Cross Sorting Expert system with EDM hybrid Sliding windows based on the extended... |
Empirical dynamic modeling : System dynamics Complex dynamics Nonlinear dimensionality reduction |
Empirical dynamic modeling : Chang, CW., Ushio, M. & Hsieh, Ch. (2017). "Empirical dynamic modeling for beginners". Ecol Res. 32 (6): 785–796. Bibcode:2017EcoR...32..785C. doi:10.1007/s11284-017-1469-9. hdl:2433/235326. S2CID 4641225.: CS1 maint: multiple names: authors list (link) Stephan B Munch, Antoine Brias, Georg... |
Empirical dynamic modeling : Animations State Space Reconstruction: Time Series and Dynamic Systems on YouTube State Space Reconstruction: Takens' Theorem and Shadow Manifolds on YouTube State Space Reconstruction: Convergent Cross Mapping on YouTube Online books or lecture notes EDM Introduction. Introduction with vid... |
Neuro-fuzzy : In the field of artificial intelligence, the designation neuro-fuzzy refers to combinations of artificial neural networks and fuzzy logic. |
Neuro-fuzzy : Neuro-fuzzy hybridization results in a hybrid intelligent system that combines the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks. Neuro-fuzzy hybridization is widely termed as fuzzy neural network (FNN) or neuro-fuzzy system (NFS) in the liter... |
Neuro-fuzzy : Pseudo outer product-based fuzzy neural networks (POPFNN) are a family of neuro-fuzzy systems that are based on the linguistic fuzzy model. Three members of POPFNN exist in the literature: POPFNN-AARS(S), which is based on the Approximate Analogical Reasoning Scheme POPFNN-CRI(S), which is based on common... |
Neuro-fuzzy : Abraham A., "Adaptation of Fuzzy Inference System Using Neural Learning, Fuzzy System Engineering: Theory and Practice", Nadia Nedjah et al. (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag Germany, ISBN 3-540-25322-X, Chapter 3, pp. 53–83, 2005. information on publisher's site. Ang, K. K.... |
Neuro-fuzzy : A Definition of Interpretability of Fuzzy Systems |
Alignment Research Center : The Alignment Research Center (ARC) is a nonprofit research institute based in Berkeley, California, dedicated to the alignment of advanced artificial intelligence with human values and priorities. Established by former OpenAI researcher Paul Christiano, ARC focuses on recognizing and compre... |
Alignment Research Center : ARC's mission is to ensure that powerful machine learning systems of the future are designed and developed safely and for the benefit of humanity. It was founded in April 2021 by Paul Christiano and other researchers focused on the theoretical challenges of AI alignment. They attempt to deve... |
Lessac Technologies : Lessac Technologies, Inc. (LTI) is an American firm which develops voice synthesis software, licenses technology and sells synthesized novels as MP3 files. The firm currently has seven patents granted and three more pending for its automated methods of converting digital text into human-sounding s... |
Double descent : Double descent in statistics and machine learning is the phenomenon where a model with a small number of parameters and a model with an extremely large number of parameters both have a small training error, but a model whose number of parameters is about the same as the number of data points used to tr... |
Double descent : Early observations of what would later be called double descent in specific models date back to 1989. The term "double descent" was coined by Belkin et. al. in 2019, when the phenomenon gained popularity as a broader concept exhibited by many models. The latter development was prompted by a perceived c... |
Double descent : Double descent occurs in linear regression with isotropic Gaussian covariates and isotropic Gaussian noise. A model of double descent at the thermodynamic limit has been analyzed using the replica trick, and the result has been confirmed numerically. |
Double descent : The scaling behavior of double descent has been found to follow a broken neural scaling law functional form. |
Double descent : Grokking (machine learning) |
Double descent : Mikhail Belkin; Daniel Hsu; Ji Xu (2020). "Two Models of Double Descent for Weak Features". SIAM Journal on Mathematics of Data Science. 2 (4): 1167–1180. arXiv:1903.07571. doi:10.1137/20M1336072. Mount, John (3 April 2024). "The m = n Machine Learning Anomaly". Preetum Nakkiran; Gal Kaplun; Yamini Ban... |
Double descent : Brent Werness; Jared Wilber. "Double Descent: Part 1: A Visual Introduction". Brent Werness; Jared Wilber. "Double Descent: Part 2: A Mathematical Explanation". Understanding "Deep Double Descent" at evhub. |
Corpus of Linguistic Acceptability : Corpus of Linguistic Acceptability (CoLA) is a dataset the primary purpose of which is to serve as a benchmark for evaluating the ability of artificial neural networks, including large language models, to judge the grammatical correctness of sentences. It consists of 10,657 English ... |
Corpus of Linguistic Acceptability : The publicly available version of CoLA contains 9,594 sentences that belong to training and development sets. It excludes 1,063 sentences reserved for a held-out test set. |
Corpus of Linguistic Acceptability : Warstadt, Alex. "CoLA - The Corpus of Linguistic Acceptability". == References == |
Sequence labeling : In machine learning, sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of a sequence of observed values. A common example of a sequence labeling task is part of speech tagging, which seeks to assign a part of speech... |
Sequence labeling : Artificial intelligence Bayesian networks (of which HMMs are an example) Classification (machine learning) Linear dynamical system, which applies to tasks where the "label" is actually a real number Machine learning Pattern recognition Sequence mining |
Sequence labeling : Erdogan H., [1]. "Sequence labeling: generative and discriminative approaches, hidden Markov models, conditional random fields and structured SVMs," ICMLA 2010 tutorial, Bethesda, MD (2010) |
MAREC : The MAtrixware REsearch Collection (MAREC) is a standardised patent data corpus available for research purposes. MAREC seeks to represent patent documents of several languages in order to answer specific research questions. It consists of 19 million patent documents in different languages, normalised to a highl... |
MAREC : MAREC is used in the Patent Language Translations Online (PLuTO) project. |
MAREC : User guide and statistics Information Retrieval Facility Archived 2008-05-22 at the Wayback Machine |
Retrieval-based Voice Conversion : Retrieval-based Voice Conversion (RVC) is an open source voice conversion AI algorithm that enables realistic speech-to-speech transformations, accurately preserving the intonation and audio characteristics of the original speaker. |
Retrieval-based Voice Conversion : In contrast to text-to-speech systems such as ElevenLabs, RVC differs by providing speech-to-speech outputs instead. It maintains the modulation, timbre and vocal attributes of the original speaker, making it suitable for applications where emotional tone is crucial. The algorithm ena... |
Retrieval-based Voice Conversion : The technology enables voice changing and mimicry, allowing users to create accurate models of others using only a negligible amount of minutes of clear audio samples. These voice models can be saved as .pth (PyTorch) files. While this capability facilitates numerous creative applicat... |
Retrieval-based Voice Conversion : Retrieval-based-Voice-Conversion-WebUI on GitHub |
BulSemCor : The Bulgarian Sense-annotated Corpus (BulSemCor) (Bulgarian: Български семантично анотиран корпус (БулСемКор)) is a structured corpus of Bulgarian texts in which each lexical item is assigned a sense tag. BulSemCor was created by the Department of Computational Linguistics at the Institute for Bulgarian Lan... |
BulSemCor : BulSemCor was created as part of a nationally funded project titled "BulNet – A lexico-semantic network for the Bulgarian Language" (2005–2010). It follows the general methodology of SemCor combined with some specific principles. The corpus for annotation consists of 101,791 tokens covering an excerpt from ... |
BulSemCor : The number of annotated tokens is 99,480 (the difference in the number of tokens compared to the initial corpus is due to the fact that some of them are not linguistic items). The simple word count is 86,842 and multiword expressions (MWE) are 5,797 (12,638 tokens). |
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