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Weak supervision : Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them. It is characterized by using a combination of a small amount of hum... |
Weak supervision : The acquisition of labeled data for a learning problem often requires a skilled human agent (e.g. to transcribe an audio segment) or a physical experiment (e.g. determining the 3D structure of a protein or determining whether there is oil at a particular location). The cost associated with the labeli... |
Weak supervision : More formally, semi-supervised learning assumes a set of l independently identically distributed examples x 1 , … , x l ∈ X ,\dots ,x_\in X with corresponding labels y 1 , … , y l ∈ Y ,\dots ,y_\in Y and u unlabeled examples x l + 1 , … , x l + u ∈ X ,\dots ,x_\in X are processed. Semi-supervised l... |
Weak supervision : In order to make any use of unlabeled data, some relationship to the underlying distribution of data must exist. Semi-supervised learning algorithms make use of at least one of the following assumptions: |
Weak supervision : The heuristic approach of self-training (also known as self-learning or self-labeling) is historically the oldest approach to semi-supervised learning, with examples of applications starting in the 1960s. The transductive learning framework was formally introduced by Vladimir Vapnik in the 1970s. Int... |
Weak supervision : Human responses to formal semi-supervised learning problems have yielded varying conclusions about the degree of influence of the unlabeled data. More natural learning problems may also be viewed as instances of semi-supervised learning. Much of human concept learning involves a small amount of direc... |
Weak supervision : Chapelle, Olivier; Schölkopf, Bernhard; Zien, Alexander (2006). Semi-supervised learning. Cambridge, Mass.: MIT Press. ISBN 978-0-262-03358-9. |
Weak supervision : Manifold Regularization A freely available MATLAB implementation of the graph-based semi-supervised algorithms Laplacian support vector machines and Laplacian regularized least squares. KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clust... |
Lyra (codec) : Lyra is a lossy audio codec developed by Google that is designed for compressing speech at very low bitrates. Unlike most other audio formats, it compresses data using a machine learning-based algorithm. |
Lyra (codec) : The Lyra codec is designed to transmit speech in real-time when bandwidth is severely restricted, such as over slow or unreliable network connections. It runs at fixed bitrates of 3.2, 6, and 9 kbit/s and it is intended to provide better quality than codecs that use traditional waveform-based algorithms ... |
Lyra (codec) : In December 2017, Google researchers published a preprint paper on replacing the Codec 2 decoder with a WaveNet neural network. They found that a neural network is able to extrapolate features of the voice not described in the Codec 2 bitstream and give better audio quality, and that the use of conventio... |
Lyra (codec) : Lyra: A New Very Low-Bitrate Codec for Speech Compression Google blog post with a demonstration comparing codecs |
Lyra (codec) : Satin (codec), an AI-based codec developed by Microsoft Comparison of audio coding formats Speech coding Videotelephony |
Google Neural Machine Translation : Google Neural Machine Translation (GNMT) was a neural machine translation (NMT) system developed by Google and introduced in November 2016 that used an artificial neural network to increase fluency and accuracy in Google Translate. The neural network consisted of two main blocks, an ... |
Google Neural Machine Translation : The Google Brain project was established in 2011 in the "secretive Google X research lab" by Google Fellow Jeff Dean, Google Researcher Greg Corrado, and Stanford University Computer Science professor Andrew Ng. Ng's work has led to some of the biggest breakthroughs at Google and Sta... |
Google Neural Machine Translation : The GNMT system was said to represent an improvement over the former Google Translate in that it will be able to handle "zero-shot translation", that is it directly translates one language into another. For example, it might be trained just for Japanese-English and Korean-English tra... |
Google Neural Machine Translation : As of December 2021, all of the languages of Google Translate support GNMT, with Latin being the most recent addition. |
Google Neural Machine Translation : Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation The Advantages and Disadvantages of Machine Translation Statistical Machine Translation International Association for Machine Translation (IAMT) Archived June 24, 2010, at the Wayback M... |
Toyota Connected North America : Toyota Connected North America is an information technology and data science company, founded in 2016 and based in Plano, Texas. Toyota Connected is a subsidiary of Toyota Motor Corporation, founded in partnership with Microsoft, and is involved in research and the development of techno... |
Toyota Connected North America : Toyota Connected North America was officially established in April 2016 in Plano, Texas, where the U.S. headquarters of Toyota Motor North America were also hosted; the company's foundation was intended as an expansion of Toyota’s partnership with Microsoft, which had originally begun i... |
Dilution (neural networks) : Dilution and dropout (also called DropConnect) are regularization techniques for reducing overfitting in artificial neural networks by preventing complex co-adaptations on training data. They are an efficient way of performing model averaging with neural networks. Dilution refers to thinnin... |
Dilution (neural networks) : Dilution is usually split in weak dilution and strong dilution. Weak dilution describes the process in which the finite fraction of removed connections is small, and strong dilution refers to when this fraction is large. There is no clear distinction on where the limit between strong and we... |
Dilution (neural networks) : Output from a layer of linear nodes, in an artificial neural net can be described as y i – output from node i w i j – real weight before dilution, also called the Hebb connection strength x j – input from node j This can be written in vector notation as y – output vector W – weight... |
Dilution (neural networks) : During weak dilution, the finite fraction of removed connections (the weights) is small, giving rise to a tiny uncertainty. This edge-case can be solved exactly with mean field theory. In weak dilution the impact on the weights can be described as w i j ^ – diluted weight w i j – real wei... |
Dilution (neural networks) : When the dilution is strong, the finite fraction of removed connections (the weights) is large, giving rise to a huge uncertainty. |
Dilution (neural networks) : Dropout is a special case of the previous weight equation (3), where the aforementioned equation is adjusted to remove a whole row in the vector matrix, and not only random weights P ( c ) – the probability c to keep a row in the weight matrix w j _ – real row in the weight matrix before... |
Dilution (neural networks) : Although there have been examples of randomly removing connections between neurons in a neural network to improve models, this technique was first introduced with the name dropout by Geoffrey Hinton, et al. in 2012. Google currently holds the patent for the dropout technique. |
Dilution (neural networks) : AlexNet Convolutional neural network § Dropout |
Anomaly detection : In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion ... |
Anomaly detection : Many attempts have been made in the statistical and computer science communities to define an anomaly. The most prevalent ones include the following, and can be categorised into three groups: those that are ambiguous, those that are specific to a method with pre-defined thresholds usually chosen emp... |
Anomaly detection : Anomaly detection is applicable in a very large number and variety of domains, and is an important subarea of unsupervised machine learning. As such it has applications in cyber-security, intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor netwo... |
Anomaly detection : Many anomaly detection techniques have been proposed in literature. The performance of methods usually depend on the data sets. For example, some may be suited to detecting local outliers, while others global, and methods have little systematic advantages over another when compared across many data ... |
Anomaly detection : Dynamic networks, such as those representing financial systems, social media interactions, and transportation infrastructure, are subject to constant change, making anomaly detection within them a complex task. Unlike static graphs, dynamic networks reflect evolving relationships and states, requiri... |
Anomaly detection : Many of the methods discussed above only yield an anomaly score prediction, which often can be explained to users as the point being in a region of low data density (or relatively low density compared to the neighbor's densities). In explainable artificial intelligence, the users demand methods with... |
Anomaly detection : ELKI is an open-source Java data mining toolkit that contains several anomaly detection algorithms, as well as index acceleration for them. PyOD is an open-source Python library developed specifically for anomaly detection. scikit-learn is an open-source Python library that contains some algorithms ... |
Anomaly detection : Anomaly detection benchmark data repository with carefully chosen data sets of the Ludwig-Maximilians-Universität München; Mirror Archived 2022-03-31 at the Wayback Machine at University of São Paulo. ODDS – ODDS: A large collection of publicly available outlier detection datasets with ground truth ... |
Anomaly detection : Change detection Statistical process control Novelty detection Hierarchical temporal memory == References == |
Pythia (machine learning) : Pythia is an ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks. It was created by Yannis Assael, Thea Sommerschield, and Jonathan Prag, researchers from Google DeepMind and the University of Oxford. To study the society and t... |
Aggregation (linguistics) : In linguistics, aggregation is a subtask of natural language generation, which involves merging syntactic constituents (such as sentences and phrases) together. Sometimes aggregation can be done at a conceptual level. |
Aggregation (linguistics) : A simple example of syntactic aggregation is merging the two sentences John went to the shop and John bought an apple into the single sentence John went to the shop and bought an apple. Syntactic aggregation can be much more complex than this. For example, aggregation can embed one of the co... |
Aggregation (linguistics) : Aggregation algorithms must do two things: Decide when two constituents should be aggregated Decide how two constituents should be aggregated, and create the aggregated structure The first issue, deciding when to aggregate, is poorly understood. Aggegration decisions certainly depend on the ... |
Aggregation (linguistics) : Unfortunately there is not much software available for performing aggregation. However the SimpleNLG system does include limited support for basic aggregation. For example, the following code causes SimpleNLG to print out The man is hungry and buys an apple. |
Time delay neural network : Time delay neural network (TDNN) is a multilayer artificial neural network architecture whose purpose is to 1) classify patterns with shift-invariance, and 2) model context at each layer of the network. Shift-invariant classification means that the classifier does not require explicit segmen... |
Time delay neural network : The TDNN was introduced in the late 1980s and applied to a task of phoneme classification for automatic speech recognition in speech signals where the automatic determination of precise segments or feature boundaries was difficult or impossible. Because the TDNN recognizes phonemes and their... |
Time delay neural network : The Time Delay Neural Network, like other neural networks, operates with multiple interconnected layers of perceptrons, and is implemented as a feedforward neural network. All neurons (at each layer) of a TDNN receive inputs from the outputs of neurons at the layer below but with two differe... |
Time delay neural network : Convolutional neural network – a convolutional neural net where the convolution is performed along the time axis of the data is very similar to a TDNN. Recurrent neural networks – a recurrent neural network also handles temporal data, albeit in a different manner. Instead of a time-varied in... |
Inferential theory of learning : Inferential Theory of Learning (ITL) is an area of machine learning which describes inferential processes performed by learning agents. ITL has been continuously developed by Ryszard S. Michalski, starting in the 1980s. The first known publication of ITL was in 1983. In the ITL learning... |
Inferential theory of learning : The most relevant published usage of ITL was in scientific journal published in 2012 and used ITL as a way to describe how agent-based learning works. According to the journal "The Inferential Theory of Learning (ITL) provides an elegant way of describing learning processes by agents". |
Infomax : Infomax, or the principle of maximum information preservation, is an optimization principle for artificial neural networks and other information processing systems. It prescribes that a function that maps a set of input values x to a set of output values z ( x ) should be chosen or learned so as to maximize... |
Infomax : (Becker and Hinton, 1992) showed that the contrastive InfoMax objective allows a neural network to learn to identify surfaces in random dot stereograms (in one dimension). One of the applications of infomax has been to an independent component analysis algorithm that finds independent signals by maximizing en... |
Infomax : Bell AJ, Sejnowski TJ (December 1997). "The "Independent Components" of Natural Scenes are Edge Filters". Vision Res. 37 (23): 3327–38. doi:10.1016/S0042-6989(97)00121-1. PMC 2882863. PMID 9425547. Linsker R (1997). "A local learning rule that enables information maximization for arbitrary input distributions... |
Weak supervision : Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them. It is characterized by using a combination of a small amount of hum... |
Weak supervision : The acquisition of labeled data for a learning problem often requires a skilled human agent (e.g. to transcribe an audio segment) or a physical experiment (e.g. determining the 3D structure of a protein or determining whether there is oil at a particular location). The cost associated with the labeli... |
Weak supervision : More formally, semi-supervised learning assumes a set of l independently identically distributed examples x 1 , … , x l ∈ X ,\dots ,x_\in X with corresponding labels y 1 , … , y l ∈ Y ,\dots ,y_\in Y and u unlabeled examples x l + 1 , … , x l + u ∈ X ,\dots ,x_\in X are processed. Semi-supervised l... |
Weak supervision : In order to make any use of unlabeled data, some relationship to the underlying distribution of data must exist. Semi-supervised learning algorithms make use of at least one of the following assumptions: |
Weak supervision : The heuristic approach of self-training (also known as self-learning or self-labeling) is historically the oldest approach to semi-supervised learning, with examples of applications starting in the 1960s. The transductive learning framework was formally introduced by Vladimir Vapnik in the 1970s. Int... |
Weak supervision : Human responses to formal semi-supervised learning problems have yielded varying conclusions about the degree of influence of the unlabeled data. More natural learning problems may also be viewed as instances of semi-supervised learning. Much of human concept learning involves a small amount of direc... |
Weak supervision : Chapelle, Olivier; Schölkopf, Bernhard; Zien, Alexander (2006). Semi-supervised learning. Cambridge, Mass.: MIT Press. ISBN 978-0-262-03358-9. |
Weak supervision : Manifold Regularization A freely available MATLAB implementation of the graph-based semi-supervised algorithms Laplacian support vector machines and Laplacian regularized least squares. KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clust... |
StatMuse : StatMuse Inc. is an American artificial intelligence company founded in 2014. The company maintains its own eponymous website where it hosts a database of sports statistics. |
StatMuse : Friends Adam Elmore and Eli Dawson founded the company in 2014. In email correspondence to the Springfield News-Leader, Elmore detailed that he and Dawson, fans of the National Basketball Association (NBA), were compelled to create StatMuse after they realized there was not a place online they could search "... |
Hierarchical temporal memory : Hierarchical temporal memory (HTM) is a biologically constrained machine intelligence technology developed by Numenta. Originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee, HTM is primarily used today for anomaly detection in streaming data. The tech... |
Hierarchical temporal memory : A typical HTM network is a tree-shaped hierarchy of levels (not to be confused with the "layers" of the neocortex, as described below). These levels are composed of smaller elements called regions (or nodes). A single level in the hierarchy possibly contains several regions. Higher hierar... |
Hierarchical temporal memory : HTM is the algorithmic component to Jeff Hawkins’ Thousand Brains Theory of Intelligence. So new findings on the neocortex are progressively incorporated into the HTM model, which changes over time in response. The new findings do not necessarily invalidate the previous parts of the model... |
Hierarchical temporal memory : HTM attempts to implement the functionality that is characteristic of a hierarchically related group of cortical regions in the neocortex. A region of the neocortex corresponds to one or more levels in the HTM hierarchy, while the hippocampus is remotely similar to the highest HTM level. ... |
Hierarchical temporal memory : Integrating memory component with neural networks has a long history dating back to early research in distributed representations and self-organizing maps. For example, in sparse distributed memory (SDM), the patterns encoded by neural networks are used as memory addresses for content-add... |
Hierarchical temporal memory : Artificial consciousness Artificial general intelligence Belief revision Cognitive architecture Convolutional neural network List of artificial intelligence projects Memory-prediction framework Multiple trace theory Neural history compressor Neural Turing machine |
Hierarchical temporal memory : Ahmad, Subutai; Hawkins, Jeff (25 March 2015). "Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory". arXiv:1503.07469 [q-bio.NC]. Hawkins, Jeff (April 2007). "Learn like a Human". IEEE Spectrum. Maltoni, Davide (April 13, 2011). "Pattern... |
Hierarchical temporal memory : HTM at Numenta HTM Basics with Rahul (Numenta), talk about the cortical learning algorithm (CLA) used by the HTM model on YouTube |
M-theory (learning framework) : In machine learning and computer vision, M-theory is a learning framework inspired by feed-forward processing in the ventral stream of visual cortex and originally developed for recognition and classification of objects in visual scenes. M-theory was later applied to other areas, such as... |
M-theory (learning framework) : M-theory is based on a quantitative theory of the ventral stream of visual cortex. Understanding how visual cortex works in object recognition is still a challenging task for neuroscience. Humans and primates are able to memorize and recognize objects after seeing just couple of examples... |
Vicuna LLM : Vicuna LLM is an omnibus large language model used in AI research. Its methodology is to enable the public at large to contrast and compare the accuracy of LLMs "in the wild" (an example of citizen science) and to vote on their output; a question-and-answer chat format is used. At the beginning of each rou... |
Vicuna LLM : [1] Test bed |
Text mining : Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websi... |
Text mining : Text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining; indeed, Ronen Fe... |
Text mining : Subtasks—components of a larger text-analytics effort—typically include: Dimensionality reduction is an important technique for pre-processing data. It is used to identify the root word for actual words and reduce the size of the text data. Information retrieval or identification of a corpus is a preparat... |
Text mining : Text mining technology is now broadly applied to a wide variety of government, research, and business needs. All these groups may use text mining for records management and searching documents relevant to their daily activities. Legal professionals may use text mining for e-discovery, for example. Governm... |
Text mining : Text mining computer programs are available from many commercial and open source companies and sources. |
Text mining : Until recently, websites most often used text-based searches, which only found documents containing specific user-defined words or phrases. Now, through use of a semantic web, text mining can find content based on meaning and context (rather than just by a specific word). Additionally, text mining softwar... |
Text mining : Marti Hearst: What Is Text Mining? (October 2003) Automatic Content Extraction, Linguistic Data Consortium Archived 2013-09-25 at the Wayback Machine Automatic Content Extraction, NIST |
Discovery system (AI research) : A discovery system is an artificial intelligence system that attempts to discover new scientific concepts or laws. The aim of discovery systems is to automate scientific data analysis and the scientific discovery process. Ideally, an artificial intelligence system should be able to sear... |
Discovery system (AI research) : Autoclass was a Bayesian Classification System written in 1986 Automated Mathematician was one of the earliest successful discovery systems. It was written in 1977 and worked by generating a modifying small Lisp programs Eurisko was a Sequel to Automated Mathematician written in 1984 Da... |
Discovery system (AI research) : After a couple of decades with little interest in discovery systems, the interest in using AI to uncover natural laws and scientific explanations was renewed by the work of Michael Schmidt, then a PhD student in Computational Biology at Cornell University. Schmidt and his advisor, Hod L... |
Discovery system (AI research) : The AI revolution in scientific research |
EfficientNet : EfficientNet is a family of convolutional neural networks (CNNs) for computer vision published by researchers at Google AI in 2019. Its key innovation is compound scaling, which uniformly scales all dimensions of depth, width, and resolution using a single parameter. EfficientNet models have been adopted... |
EfficientNet : EfficientNet introduces compound scaling, which, instead of scaling one dimension of the network at a time, such as depth (number of layers), width (number of channels), or resolution (input image size), uses a compound coefficient ϕ to scale all three dimensions simultaneously. Specifically, given a ba... |
EfficientNet : EfficientNet has been adapted for fast inference on edge TPUs and centralized TPU or GPU clusters by NAS. EfficientNet V2 was published in June 2021. The architecture was improved by further NAS search with more types of convolutional layers. It also introduced a training method, which progressively incr... |
EfficientNet : Convolutional neural network SqueezeNet MobileNet You Only Look Once |
EfficientNet : EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling (Google AI Blog) |
Structured sparsity regularization : Structured sparsity regularization is a class of methods, and an area of research in statistical learning theory, that extend and generalize sparsity regularization learning methods. Both sparsity and structured sparsity regularization methods seek to exploit the assumption that the... |
Structured sparsity regularization : Structured sparsity regularization methods have been used in a number of settings where it is desired to impose an a priori input variable structure to the regularization process. Some such applications are: Compressive sensing in magnetic resonance imaging (MRI), reconstructing MR ... |
Structured sparsity regularization : Statistical learning theory Regularization Sparse approximation Proximal gradient methods Convex analysis Feature selection == References == |
Winner-take-all in action selection : Winner-take-all is a computer science concept that has been widely applied in behavior-based robotics as a method of action selection for intelligent agents. Winner-take-all systems work by connecting modules (task-designated areas) in such a way that when one action is performed i... |
Winner-take-all in action selection : In the 1980s and 1990s, many roboticists and cognitive scientists were attempting to find speedier and more efficient alternatives to the traditional world modeling method of action selection. In 1982, Jerome A. Feldman and D.H. Ballard published the "Connectionist Models and Their... |
Winner-take-all in action selection : Artificial Neural Network (ANN) Subsumption architecture Zero instruction set computer (ZISC) == References == |
LeNet : LeNet is a series of convolutional neural network architectures created by a research group in AT&T Bell Laboratories during the 1988 to 1998 period, centered around Yann LeCun. They were designed for reading small grayscale images of handwritten digits and letters, and were used in ATM for reading cheques. Con... |
LeNet : In 1988, LeCun joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, United States, headed by Lawrence D. Jackel.In 1988, LeCun et al. published a neural network design that recognize handwritten zip code. However, its convolutional kernels were hand-designed. In 1989... |
LeNet : LeNet has several common motifs of modern convolutional neural networks, such as convolutional layer, pooling layer and full connection layer. Every convolutional layer includes three parts: convolution, pooling, and nonlinear activation functions Using convolution to extract spatial features (Convolution was c... |
LeNet : Recognizing simple digit images is the most classic application of LeNet as it was created because of that. After the development of 1989 LeNet, as a demonstration for real-time application, they loaded the neural network into a AT&T DSP-32C digital signal processor with a peak performance of 12.5 million multi... |
LeNet : The LeNet-5 means the emergence of CNN and defines the basic components of CNN. But it was not popular at that time because of the lack of hardware, especially since GPUs and other algorithms, such as SVM, could achieve similar effects or even exceed LeNet. Since the success of AlexNet in 2012, CNN has become t... |
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