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U-Net : Tensorflow Unet by J Akeret (2017) U-Net source code from Pattern Recognition and Image Processing at Computer Science Department of the University of Freiburg, Germany. |
NetOwl : NetOwl is a suite of multilingual text and identity analytics products that analyze big data in the form of text data – reports, web, social media, etc. – as well as structured entity data about people, organizations, places, and things. NetOwl utilizes artificial intelligence (AI)-based approaches, including ... |
NetOwl : The first NetOwl product was NetOwl Extractor, which was initially released in 1996. Since then, Extractor has added many new capabilities, including relationship and event extraction, categorization, name translation, geotagging, and sentiment analysis, as well as entity extraction in other languages. Other p... |
NetOwl : The NetOwl suite includes, among others, the following text and entity analytics products: |
NetOwl : Knowledge extraction Text mining Data mining Computational linguistics Named entity recognition Unstructured data Document classification |
NetOwl : NetOwl website |
Uncertain data : In computer science, uncertain data is data that contains noise that makes it deviate from the correct, intended or original values. In the age of big data, uncertainty or data veracity is one of the defining characteristics of data. Data is constantly growing in volume, variety, velocity and uncertain... |
Uncertain data : One way to represent uncertain data is through probability distributions. Let us take the example of a relational database. There are three main ways to do represent uncertainty as probability distributions in such a database model. In attribute uncertainty, each uncertain attribute in a tuple is subje... |
Uncertain data : Habich Volk; Clemens Utzny; Ralf Dittmann; Wolfgang Lehner. "Error-Aware Density-Based Clustering of Imprecise Measurement Values". Seventh IEEE International Conference on Data Mining Workshops, 2007. ICDM Workshops 2007. IEEE. Volk Rosentahl; Martin Hahmann; Dirk Habich; Wolfgang Lehner. "Clustering ... |
Region Based Convolutional Neural Networks : Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and localization. The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each... |
Region Based Convolutional Neural Networks : The following covers some of the versions of R-CNN that have been developed. November 2013: R-CNN. April 2015: Fast R-CNN. June 2015: Faster R-CNN. March 2017: Mask R-CNN. June 2019: Mesh R-CNN adds the ability to generate a 3D mesh from a 2D image. |
Region Based Convolutional Neural Networks : Parthasarathy, Dhruv (2017-04-27). "A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN". Medium. Retrieved 2024-09-11. |
Charles Lynn Wayne : Charles Lynn Wayne (1943 – November 23, 2024) was an American program manager at the Defense Advanced Research Projects Agency (DARPA). He was instrumental in creating the Common Task Method for advancing speech recognition and natural language processing technologies by centering around public ben... |
Charles Lynn Wayne : Charles Lynn Wayne was born in Lake City, Florida, in 1943. His father, also Charles Wayne, was a pilot stationed at the Naval Air Station there. His mother was Dorothy Rodenhausen Wayne. He grew up in Guam, Philadelphia, Washington, Providence, and Norfolk. He'd lived in Maryland since 1967. He at... |
OpenAI o1 : OpenAI o1 is a reflective generative pre-trained transformer (GPT). A preview of o1 was released by OpenAI on September 12, 2024. o1 spends time "thinking" before it answers, making it better at complex reasoning tasks, science and programming than GPT-4o. The full version was released to ChatGPT users on D... |
OpenAI o1 : According to OpenAI, o1 has been trained using a new optimization algorithm and a dataset specifically tailored to it; while also meshing in reinforcement learning into its training. OpenAI described o1 as a complement to GPT-4o rather than a successor. o1 spends additional time thinking (generating a chain... |
OpenAI o1 : o1 usually requires more computing time and power than other GPT models by OpenAI, because it generates long chains of thought before making the final response. According to OpenAI, o1 may "fake alignment", that is, generate a response that is contrary to accuracy and its own chain of thought, in about 0.38... |
Ugly duckling theorem : The ugly duckling theorem is an argument showing that classification is not really possible without some sort of bias. More particularly, it assumes finitely many properties combinable by logical connectives, and finitely many objects; it asserts that any two different objects share the same num... |
Ugly duckling theorem : Suppose there are n things in the universe, and one wants to put them into classes or categories. One has no preconceived ideas or biases about what sorts of categories are "natural" or "normal" and what are not. So one has to consider all the possible classes that could be, all the possible way... |
Ugly duckling theorem : A possible way around the ugly duckling theorem would be to introduce a constraint on how similarity is measured by limiting the properties involved in classification, for instance, between A and B. However Medin et al. (1993) point out that this does not actually resolve the arbitrariness or bi... |
Ugly duckling theorem : No free lunch in search and optimization No free lunch theorem Identity of indiscernibles – Classification (discernibility) is possible (with or without a bias), but there cannot be separate objects or entities that have all their properties in common. New riddle of induction == Notes == |
Vision transformer : A vision transformer (ViT) is a transformer designed for computer vision. A ViT decomposes an input image into a series of patches (rather than text into tokens), serializes each patch into a vector, and maps it to a smaller dimension with a single matrix multiplication. These vector embeddings are... |
Vision transformer : Transformers were introduced in Attention Is All You Need (2017), and have found widespread use in natural language processing. A 2019 paper applied ideas from the Transformer to computer vision. Specifically, they started with a ResNet, a standard convolutional neural network used for computer vis... |
Vision transformer : The basic architecture, used by the original 2020 paper, is as follows. In summary, it is a BERT-like encoder-only Transformer. The input image is of type R H × W × C ^ , where H , W , C are height, width, channel (RGB). It is then split into square-shaped patches of type R P × P × C ^ . For eac... |
Vision transformer : Typically, ViT uses patch sizes larger than standard CNN kernels (3x3 to 7x7). ViT is more sensitive to the choice of the optimizer, hyperparameters, and network depth. Preprocessing with a layer of smaller-size, overlapping (stride < size) convolutional filters helps with performance and stability... |
Vision transformer : ViT have been used in many Computer Vision tasks with excellent results and in some cases even state-of-the-art. Image Classification, Object Detection, Video Deepfake Detection, Image segmentation, Anomaly detection, Image Synthesis, Cluster analysis, Autonomous Driving. ViT had been used for imag... |
Vision transformer : Transformer (machine learning model) Convolutional neural network Attention (machine learning) Perceiver Deep learning PyTorch TensorFlow |
Vision transformer : Zhang, Aston; Lipton, Zachary; Li, Mu; Smola, Alexander J. (2024). "11.8. Transformers for Vision". Dive into deep learning. Cambridge New York Port Melbourne New Delhi Singapore: Cambridge University Press. ISBN 978-1-009-38943-3. Steiner, Andreas; Kolesnikov, Alexander; Zhai, Xiaohua; Wightman, R... |
Flow-based generative model : A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex o... |
Flow-based generative model : Let z 0 be a (possibly multivariate) random variable with distribution p 0 ( z 0 ) (z_) . For i = 1 , . . . , K , let z i = f i ( z i − 1 ) =f_(z_) be a sequence of random variables transformed from z 0 . The functions f 1 , . . . , f K ,...,f_ should be invertible, i.e. the inverse fun... |
Flow-based generative model : As is generally done when training a deep learning model, the goal with normalizing flows is to minimize the Kullback–Leibler divergence between the model's likelihood and the target distribution to be estimated. Denoting p θ the model's likelihood and p ∗ the target distribution to lear... |
Flow-based generative model : Despite normalizing flows success in estimating high-dimensional densities, some downsides still exist in their designs. First of all, their latent space where input data is projected onto is not a lower-dimensional space and therefore, flow-based models do not allow for compression of dat... |
Flow-based generative model : Flow-based generative models have been applied on a variety of modeling tasks, including: Audio generation Image generation Molecular graph generation Point-cloud modeling Video generation Lossy image compression Anomaly detection |
Flow-based generative model : Flow-based Deep Generative Models Normalizing flow models |
NVDLA : The NVIDIA Deep Learning Accelerator (NVDLA) is an open-source hardware neural network AI accelerator created by Nvidia. The accelerator is written in Verilog and is configurable and scalable to meet many different architecture needs. NVDLA is merely an accelerator and any process must be scheduled and arbitere... |
NVDLA : Official website |
Data physicalization : A data physicalization (or simply physicalization) is a physical artefact whose geometry or material properties encode data. It has the main goals to engage people and to communicate data using computer-supported physical data representations. |
Data physicalization : Before the invention of computers and digital devices, the application of data physicalization already existed in ancient artifacts as a medium to represent abstract information. One example is Blombo ocher plaque which is estimated to be 70000 – 80000 years old. The geometric and iconographic sh... |
Machine learning in earth sciences : Applications of machine learning (ML) in earth sciences include geological mapping, gas leakage detection and geological feature identification. Machine learning is a subdiscipline of artificial intelligence aimed at developing programs that are able to classify, cluster, identify, ... |
Machine learning in earth sciences : The extensive usage of machine learning in various fields has led to a wide range of algorithms of learning methods being applied. Choosing the optimal algorithm for a specific purpose can lead to a significant boost in accuracy: for example, the lithological mapping of gold-bearing... |
Organoid intelligence : Organoid intelligence (OI) is an emerging field of study in computer science and biology that develops and studies biological wetware computing using 3D cultures of human brain cells (or brain organoids) and brain-machine interface technologies. Such technologies may be referred to as OIs. |
Organoid intelligence : As opposed to traditional non-organic silicon-based approaches, OI seeks to use lab-grown cerebral organoids to serve as "biological hardware." Scientists hope that such organoids can provide faster, more efficient, and more powerful computing power than regular silicon-based computing and AI wh... |
Organoid intelligence : OI generates complex biological data, necessitating sophisticated methods for processing and analysis. Bioinformatics provides the tools and techniques to decipher raw data, uncovering the patterns and insights. A Python interface is currently available for processing and interaction with brain ... |
Organoid intelligence : Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function, as... |
Organoid intelligence : While researchers are hoping to use OI and biological computing to complement traditional silicon-based computing, there are also questions about the ethics of such an approach. Examples of such ethical issues include OIs gaining consciousness and sentience as organoids and the question of the r... |
Evaluation of binary classifiers : Evaluation of a binary classifier typically assigns a numerical value, or values, to a classifier that represent its accuracy. An example is error rate, which measures how frequently the classifier makes a mistake. There are many metrics that can be used; different fields have differe... |
Evaluation of binary classifiers : Given a data set, a classification (the output of a classifier on that set) gives two numbers: the number of positives and the number of negatives, which add up to the total size of the set. To evaluate a classifier, one compares its output to another reference classification – ideall... |
Evaluation of binary classifiers : Often accuracy is evaluated with a pair of metrics composed in a standard pattern. |
Evaluation of binary classifiers : In addition to the paired metrics, there are also unitary metrics that give a single number to evaluate the test. Perhaps the simplest statistic is accuracy or fraction correct (FC), which measures the fraction of all instances that are correctly categorized; it is the ratio of the nu... |
Evaluation of binary classifiers : Hand has highlighted the importance of choosing an appropriate method of evaluation. However, of the many different methods for evaluating the accuracy of a classifier, there is no general method for determining which method should be used in which circumstances. Different fields have... |
Evaluation of binary classifiers : Often, we want to evaluate not a specific classifier working in a specific way but an underlying technology. Typically, the technology can be adjusted through altering the threshold of a score function, the threshold determining whether the result is a positive or negative. For such e... |
Evaluation of binary classifiers : Apart from accuracy, binary classifiers can be assessed in many other ways, for example in terms of their speed or cost. |
Evaluation of binary classifiers : Probabilistic classification models go beyond providing binary outputs and instead produce probability scores for each class. These models are designed to assess the likelihood or probability of an instance belonging to different classes. In the context of evaluating probabilistic cla... |
Evaluation of binary classifiers : Information retrieval systems, such as databases and web search engines, are evaluated by many different metrics, some of which are derived from the confusion matrix, which divides results into true positives (documents correctly retrieved), true negatives (documents correctly not ret... |
Evaluation of binary classifiers : Population impact measures Attributable risk Attributable risk percent Scoring rule (for probability predictions) Pseudo-R-squared Likelihood ratios |
Evaluation of binary classifiers : Damage Caused by Classification Accuracy and Other Discontinuous Improper Accuracy Scoring Rules |
Grammatik : Grammatik was the first grammar checking program developed for home computer systems. Aspen Software of Albuquerque, NM, released the earliest version of this diction and style checker for personal computers. It was first released no later than 1981, and was inspired by the Writer's Workbench. Grammatik was... |
Machine learning in bioinformatics : Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining. Prior to the emergence of machine learning, bioinformatics algorithms had to be programme... |
Machine learning in bioinformatics : Machine learning algorithms in bioinformatics can be used for prediction, classification, and feature selection. Methods to achieve this task are varied and span many disciplines; most well known among them are machine learning and statistics. Classification and prediction tasks aim... |
Machine learning in bioinformatics : In general, a machine learning system can usually be trained to recognize elements of a certain class given sufficient samples. For example, machine learning methods can be trained to identify specific visual features such as splice sites. Support vector machines have been extensive... |
Machine learning in bioinformatics : An important part of bioinformatics is the management of big datasets, known as databases of reference. Databases exist for each type of biological data, for example for biosynthetic gene clusters and metagenomes. |
Soboleva modified hyperbolic tangent : The Soboleva modified hyperbolic tangent, also known as (parametric) Soboleva modified hyperbolic tangent activation function ([P]SMHTAF), is a special S-shaped function based on the hyperbolic tangent, given by |
Soboleva modified hyperbolic tangent : This function was originally proposed as "modified hyperbolic tangent" by Ukrainian scientist Elena V. Soboleva (Елена В. Соболева) as a utility function for multi-objective optimization and choice modelling in decision-making. |
Soboleva modified hyperbolic tangent : The function has since been introduced into neural network theory and practice. It was also used in economics for modelling consumption and investment, to approximate current-voltage characteristics of field-effect transistors and light-emitting diodes, to design antenna feeders, ... |
Soboleva modified hyperbolic tangent : Derivative of the function is defined by the formula: smht ′ ( x ) ≐ a e a x + b e − b x e c x + e − d x − smht ( x ) c e c x − d e − d x e c x + e − d x '(x)\doteq +be^+e^-\operatorname (x)-de^+e^ The following conditions are keeping the function limited on y-axes: a ≤ c, b... |
Soboleva modified hyperbolic tangent : Activation function e (mathematical constant) Equal incircles theorem, based on sinh Hausdorff distance Inverse hyperbolic functions List of integrals of hyperbolic functions Poinsot's spirals Sigmoid function |
Soboleva modified hyperbolic tangent : Iliev, Anton; Kyurkchiev, Nikolay; Markov, Svetoslav (2017). "A Note on the New Activation Function of Gompertz Type". Biomath Communications. 4 (2). Faculty of Mathematics and Informatics, University of Plovdiv "Paisii Hilendarski", Plovdiv, Bulgaria / Institute of Mathematics an... |
Situated approach (artificial intelligence) : In artificial intelligence research, the situated approach builds agents that are designed to behave effectively successfully in their environment. This requires designing AI "from the bottom-up" by focussing on the basic perceptual and motor skills required to survive. The... |
Situated approach (artificial intelligence) : Classically, a software entity is defined as a simulated element, able to act on itself and on its environment, and which has an internal representation of itself and of the outside world. An entity can communicate with other entities, and its behavior is the consequence of... |
Situated approach (artificial intelligence) : Arsenio, Artur M. (2004) Towards an embodied and situated AI, In: Proceedings of the International FLAIRS conference, 2004. (online) The Artificial Life Route To Artificial Intelligence: Building Embodied, Situated Agents, Luc Steels and Rodney Brooks Eds., Lawrence Erlbaum... |
Situated approach (artificial intelligence) : Article Artificial Intelligence: The situated approach from the Encyclopædia Britannica Nouvelle AI - Definition Reactive planning and nouvelle AI |
Statistical relational learning : Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational structure. Typically, the knowledge re... |
Statistical relational learning : A number of canonical tasks are associated with statistical relational learning, the most common ones being. collective classification, i.e. the (simultaneous) prediction of the class of several objects given objects' attributes and their relations link prediction, i.e. predicting whet... |
Statistical relational learning : One of the fundamental design goals of the representation formalisms developed in SRL is to abstract away from concrete entities and to represent instead general principles that are intended to be universally applicable. Since there are countless ways in which such principles can be re... |
Statistical relational learning : Association rule learning Formal concept analysis Fuzzy logic Grammar induction Knowledge graph embedding |
Statistical relational learning : Brian Milch, and Stuart J. Russell: First-Order Probabilistic Languages: Into the Unknown, Inductive Logic Programming, volume 4455 of Lecture Notes in Computer Science, page 10–24. Springer, 2006 Rodrigo de Salvo Braz, Eyal Amir, and Dan Roth: A Survey of First-Order Probabilistic Mod... |
News analytics : In trading strategy, news analysis refers to the measurement of the various qualitative and quantitative attributes of textual (unstructured data) news stories. Some of these attributes are: sentiment, relevance, and novelty. Expressing news stories as numbers and metadata permits the manipulation of e... |
News analytics : The application of sophisticated linguistic analysis to news and social media has grown from an area of research to mature product solutions since 2007. News analytics and news sentiment calculations are now routinely used by both buy-side and sell-side in alpha generation, trading execution, risk mana... |
News analytics : Being able to express news stories as numbers permits the manipulation of everyday information in a statistical way that allows computers not only to make decisions once made only by humans, but to do so more efficiently. Since market participants are always looking for an edge, the speed of computer c... |
News analytics : Computational linguistics Sentiment analysis Text mining Trading the news Unstructured data Natural language processing Information asymmetry Algorithmic trading == References == |
ChatGPT Deep Research : Deep Research is an AI agent integrated into ChatGPT, which generates cited reports on a user-specified topic by autonomously browsing the web for 5 to 30 minutes. |
ChatGPT Deep Research : It can interpret and analyze text, images and PDFs, and will soon be capable of producing visualizations and embedding images in its reports. It is based on a specialized version of OpenAI's o3 model. Deep Research scored 26.6% on the "Humanity's Last Exam" benchmark, surpassing rivals like Deep... |
Brilliant Labs : Brilliant Labs is a Singapore-based technology company that produces open source eyewear featuring artificial intelligence (AI). Brilliant Labs was founded in 2019 in Hong Kong by Bobak Tavangar, a former Apple program lead. Tavangar said he saw the potential for integrating the capabilities of artific... |
Adaptive neuro fuzzy inference system : An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s. Since it integrates both neural networks... |
Adaptive neuro fuzzy inference system : It is possible to identify two parts in the network structure, namely premise and consequence parts. In more details, the architecture is composed by five layers. The first layer takes the input values and determines the membership functions belonging to them. It is commonly call... |
OpenAI Operator : OpenAI Operator is an AI agent developed by OpenAI, capable of autonomously performing tasks through web browser interactions, including filling forms, placing online orders, scheduling appointments, and other repetitive browser-based tasks. It uses OpenAI's advanced models to expand practical automat... |
OpenAI Operator : In benchmark assessments, Operator achieved notable success, scoring 38.1% on OSWorld benchmarks (OS-level tasks) and 58.1% on WebArena benchmarks (web interactions). However, it has not yet reached human-level accuracy and faces limitations with intricate user interfaces and extended workflows. |
OpenAI Operator : OpenAI emphasizes privacy and safety measures within Operator, including stringent data protection protocols and built-in safety checks designed to prevent unauthorized sensitive actions or information misuse. |
OpenAI Operator : Initially, Operator is only available to ChatGPT Pro subscribers in the U.S., with plans for broader availability to Plus, Team, and Enterprise users in the future. == References == |
Action model learning : Action model learning (sometimes abbreviated action learning) is an area of machine learning concerned with creation and modification of software agent's knowledge about effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in... |
Action model learning : Given a training set E consisting of examples e = ( s , a , s ′ ) , where s , s ′ are observations of a world state from two consecutive time steps t , t ′ and a is an action instance observed in time step t , the goal of action model learning in general is to construct an action model ⟨ D... |
Action model learning : Machine learning Automated planning and scheduling Action language PDDL Architecture description language Inductive reasoning Computational logic Knowledge representation == References == |
W-shingling : In natural language processing a w-shingling is a set of unique shingles (therefore n-grams) each of which is composed of contiguous subsequences of tokens within a document, which can then be used to ascertain the similarity between documents. The symbol w denotes the quantity of tokens in each shingle s... |
W-shingling : For a given shingle size, the degree to which two documents A and B resemble each other can be expressed as the ratio of the magnitudes of their shinglings' intersection and union, or r ( A , B ) = | S ( A ) ∩ S ( B ) | | S ( A ) ∪ S ( B ) | \over where |A| is the size of set A. The resemblance is a num... |
W-shingling : Bag-of-words model Jaccard index Concept mining k-mer MinHash N-gram Rabin fingerprint Rolling hash Vector space model |
W-shingling : Broder; Glassman; Manasse; Zweig (1997). "Syntactic Clustering of the Web". SRC Technical Note #1997-015. Manber (1993). "Finding Similar Files in a Large File System" (PDF). Does not yet use the term "shingling". Manning, Christopher D.; Raghavan, Prabhakar; Schütze, Hinrich (7 July 2008). "w-shingling".... |
COTSBot : COTSBot is a small autonomous underwater vehicle (AUV) 4.5 feet (1.4 m) long, which is designed by Queensland University of Technology (QUT) to kill the very destructive crown-of-thorns starfish (Acanthaster planci) in the Great Barrier Reef off the north-east coast of Australia. It identifies its target usin... |
Outline of natural language processing : The following outline is provided as an overview of and topical guide to natural-language processing: natural-language processing – computer activity in which computers are entailed to analyze, understand, alter, or generate natural language. This includes the automation of any ... |
Outline of natural language processing : Natural-language processing can be described as all of the following: A field of science – systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe. An applied science – field that applies human knowledge t... |
Outline of natural language processing : The following technologies make natural-language processing possible: Communication – the activity of a source sending a message to a receiver Language – Speech – Writing – Computing – Computers – Computer programming – Information extraction – User interface – Software – Text e... |
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