Results 261 to 270 of about 136,861 (303)

An explainable unsupervised learning approach for anomaly detection on corneal <i>in vivo</i> confocal microscopy images. [PDF]

Front Bioeng Biotechnol
Tang N, Chen Q, Meng Y, Lei D, Jiang L, Qin Y, Huang X, Tang F, Huang S, Lan Q, Chen Q, Huang L, Lan R, Pan X, Wang H, Xu F, He W.   +16 more
europepmc   +1 more source

Cross-device federated unsupervised learning for the detection of anomalies in single-lead electrocardiogram signals. [PDF]

PLOS Digit Health
Kapsecker M, Jonas SM.
europepmc   +1 more source

Unsupervised learning reveals rapid gait adaptation after leg loss and regrowth in spiders.

J Exp Biol
Kane SA, Quinn BL, Wu XK, Xi SY, Ochs MF, Hsieh ST.   +5 more
europepmc   +1 more source

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Fairness in Unsupervised Learning

Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020
Data in digital form is expanding at an exponential rate, far outpacing any chance of getting any significant fraction labelled manually. This has resulted in heightened research emphasis on unsupervised learning, learning in the absence of labels. In fact, unsupervised learning has been often dubbed as the next frontier of AI. Unsupervised learning is
Deepak P 0001, Joemon M. Jose, Sanil V
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Unsupervised Learning

Neural Computation, 1989
What use can the brain make of the massive flow of sensory information that occurs without any associated rewards or punishments? This question is reviewed in the light of connectionist models of unsupervised learning and some older ideas, namely the cognitive maps and working models of Tolman and Craik, and the idea that redundancy is important for ...
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Unsupervised Learning: Clustering

2019
In this article an introduction on unsupervised cluster analysis is provided. Clustering is the organisation of unlabelled data into similarity groups called clusters. A cluster is a collection of data items which are similar between them, and dissimilar to data items in other clusters.
Serra A., Tagliaferri R.
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Unsupervised Reinforcement Learning

International Joint Conference on Autonomous Agents and Multiagent Systems, 2020
Conventionally, reinforcement learning algorithms are goal-directed: they aim to acquire policies that most effectively maximize a given reward signal. However, if we consider agents that must master very large repertoires of behaviors -- such as general-purpose robots that must perform a diverse array of tasks in the real world -- then it makes sense ...
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Spicules for Unsupervised Learning

2009
We present a new model of unsupervised competitive neural network, based on spicules. This model is capable of detecting topological information of an input space, determining its orientation and, in most case, its skeleton.
José Antonio Gómez-Ruiz, José Muñoz-Pérez, M. Angeles García-Bernal   +2 more
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Generalization in Unsupervised Learning

2015
We are interested in the following questions. Given a finite data set S, with neither labels nor side information, and an unsupervised learning algorithm A, can the generalization of A be assessed on S? Similarly, given two unsupervised learning algorithms, A1 and A2, for the same learning task, can one assess whether one will generalize "better" on ...
Karim T. Abou-Moustafa, Dale Schuurmans
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Unsupervised learning with stochastic gradient

Neurocomputing, 2005
A stochastic gradient is formulated based on deterministic gradient augmented with Cauchy simulated annealing capable to reach a global minimum with a convergence speed significantly faster then when simulated annealing is used alone. In order to solve space-time variant inverse problems known as blind source separation, a novel Helmholtz free energy ...
Harold Szu, Ivica Kopriva
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