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Unsupervised learning with stochastic gradient
2005
Neurocomputing
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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 when simulated ...
A stochastic gradient was successfully applied to solve inverse space-variant imaging problems on a concurrent pixel-bypixel basis with the unknown mixing matrix (imaging point spread function) varying ...
That motivated our efforts to formulate stochastic gradient descent learning as a combination of the gradient descent and stochastic Cauchy annealing. ...
doi:10.1016/j.neucom.2004.11.010
fatcat:gkmkkmfkujhrjlzze5ygyugpsm
Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning
[article]
2020
arXiv
pre-print
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When the mathematical model of the environment is incomplete, we introduce reinforced-unsupervised learning algorithms that learn the model by interacting with the environment. ...
In this article, we introduce unsupervised and reinforced-unsupervised learning frameworks for solving both variable and functional optimization problems without the supervision of the optimal solutions ...
We note that for the stochastic policy, there is no substantial difference between the model-free and the modelbased unsupervised learning, since the gradients of the OF and CFs are not required for the ...
arXiv:2001.00784v1
fatcat:c7f4kqg5vrb6leofxk6ixaeorm
Why Does Unsupervised Pre-training Help Deep Learning?
2010
Journal of machine learning research
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The best results obtained on supervised learning tasks involve an unsupervised learning component, usually in an unsupervised pre-training phase. ...
Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants, with impressive results obtained in several areas, ...
This is a natural effect of stochastic gradient descent with a constant learning rate (which gives exponentially more weight to recent examples). ...
dblp:journals/jmlr/ErhanBCMVB10
fatcat:x5qjobqpsfhy3ndz452vzwwz5y
Application of Stochastic Gradient Descent Algorithm in Evaluating the Performance Contribution of Employees
2014
IOSR Journal of Business and Management
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This paper presents the applicability of stochastic gradient descent algorithm as an approach for evaluation of performance contribution. ...
Under that, performance evaluation is established in corporate, department and team levels with the intention of creating an employee incentive scheme. ...
Training data of an unsupervised learning problem does not consist such labelled inputs corresponded with the targets. ...
doi:10.9790/487x-16637780
fatcat:nkritehgdzchzpykcvcspld2by
Unsupervised Sequence Classification using Sequential Output Statistics
[article]
2017
arXiv
pre-print
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., language models) could be obtained independently of input data and thus with low or no cost. To address the problem, we propose an unsupervised learning cost function and study its properties. ...
Although it is harder to optimize in its functional form, a stochastic primal-dual gradient method is developed to effectively solve the problem. ...
Specifically, we minimize L with respect to the primal variable θ by stochastic gradient descent and maximize L with respect to the dual variable V by stochastic gradient ascent. ...
arXiv:1702.07817v2
fatcat:zwrwooo5dbhmlontkwvvqsqova
Ladder Networks: Learning under Massive Label Deficit
2017
International Journal of Advanced Computer Science and Applications
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Advancement in deep unsupervised learning are finally bringing machine learning close to natural learning, which happens with as few as one labeled instance. ...
This work discusses how the ladder network model successfully combines supervised and unsupervised learning taking it beyond the pre-training realm. ...
Ladder networks combine supervised learning with unsupervised learning in deep neural networks. ...
doi:10.14569/ijacsa.2017.080769
fatcat:ix4dlifjc5hwjka6hohdrswz2m
Doc2hash: Learning Discrete Latent variables for Documents Retrieval
2019
North American Chapter of the Association for Computational Linguistics
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However, the discrete stochastic layer is usually incompatible with the backpropagation in the training stage and thus causes a gradient flow problem. ...
In this paper, we propose a method, Doc2hash, that solves the gradient flow problem of the discrete stochastic layer by using continuous relaxation on priors, and trains the generative model in an end-to-end ...
Therefore, it produces low-variance biased gradients of the stochastic layer in the backpropagation. ...
doi:10.18653/v1/n19-1232
dblp:conf/naacl/ZhangZ19
fatcat:2a6b2orh4zghpd7gesjjt76hle
Comparing Unsupervised Word Translation Methods Step by Step
2019
Neural Information Processing Systems
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, and transfer learning. ...
In the unsupervised regime, an initial seed dictionary is learned in the absence of any known correspondences between words, through distribution matching, and the seed dictionary is then used to supervise ...
GANs with stochastic dictionary induction provides a new state of the art for unsupervised word translation. ...
dblp:conf/nips/HartmannKS19
fatcat:jbnapc2la5fptb5oa5ffplsvuy
Hierarchical Deep Learning Architecture For 10K Objects Classification
[article]
2015
arXiv
pre-print
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2015 Computer Science & Information Technology ( CS & IT ) unpublished doi:10.5121/csit.2015.51408 fatcat:qhsk75kb75eldde3bvdiminkmy
Also we propose a blend of leaf level models trained with either supervised or unsupervised learning approaches. ...
These architectures extract & learn the real world hierarchical visual features utilizing supervised & unsupervised learning approaches respectively. ...
supervised gradient descent.Utilizing unsupervised learning we have trained a leaf model Leaf-4 that consists of man-made artifacts with 235 categories. ...
arXiv:1509.01951v1
fatcat:zm6636eulfgcpgn3qgai36tpke
Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings
[article]
2020
arXiv
pre-print
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We propose a novel manifold based geometric approach for learning unsupervised alignment of word embeddings between the source and the target languages. ...
The rich geometry of the doubly stochastic manifold allows to employ efficient Riemannian conjugate gradient algorithm for the proposed formulation. ...
Learning unsupervised cross-lingual mapping may be viewed as an instance of the more general unsupervised domain adaptation problem (Ben-David et al., 2007; Gopalan et al., 2011; Sun et al., 2016; Mahadevan ...
arXiv:2004.08243v2
fatcat:icp7v4fvwvgwlmpobhxxov5zsq
Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991)
[article]
2020
arXiv
pre-print
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One network learns to generate a probability distribution over outputs, the other learns to predict effects of the outputs. Each network minimizes the objective function maximized by the other. ...
I review unsupervised or self-supervised neural networks playing minimax games in game-theoretic settings: (i) Artificial Curiosity (AC, 1990) is based on two such networks. ...
The 1990 paper [61] describes gradient-based learning methods for both C and M. ...
arXiv:1906.04493v3
fatcat:rigrb7az65hjvpvwt6kcbldh3y
Deep learning and face recognition: the state of the art
2019
arXiv
pre-print
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2015
Biometric and Surveillance Technology for Human and Activity Identification XII
unpublished
doi:10.1117/12.2181526 fatcat:rwxe6pkgjbearboauj66rfhzem
doi:10.1117/12.2181526 fatcat:rwxe6pkgjbearboauj66rfhzem
Deep Neural Networks (DNNs) have established themselves as a dominant technique in machine learning. ...
In this talk and accompanying paper, I attempt to provide a review and summary of the deep learning techniques used in the state-of-the-art. ...
Online stochastic gradient descent: B = 1. 2. Mini-batch stochastic gradient descent: B > 1 but B < |x|. 3. "Batch" gradient descent, B = |x|. ...
arXiv:1902.03524v1
fatcat:6z2n44siwbht3a22c4kgc6mghu
The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training
2009
Journal of machine learning research
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The experiments confirm and clarify the advantage of unsupervised pre-training. ...
We empirically show the influence of pre-training with respect to architecture depth, model capacity, and number of training examples. ...
Stochastic gradient descent θ ⇐ θ − • ∂KL(x||x)/∂θ is then performed with learning rate , for a fixed number of pre-training iterations. ...
dblp:journals/jmlr/ErhanMBBV09
fatcat:zbjsmisy7nhgtl5g62uoovy6ju
Bayesian Optimization for Machine Learning : A Practical Guidebook
[article]
2016
arXiv
pre-print
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machine learning applications. ...
We outline four example machine learning problems that can be solved using open source machine learning libraries, and highlight the benefits of using Bayesian optimization in the context of these common ...
Unsupervised Learning Unsupervised learning algorithms are designed with the hope of capturing some useful latent structure in data. ...
arXiv:1612.04858v1
fatcat:cncz6ky4zrakjitnh7o5ed6fju
Heavy-Tailed Symmetric Stochastic Neighbor Embedding
2009
Neural Information Processing Systems
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Moreover, it uses a gradient descent algorithm that may require users to tune parameters such as the learning step size, momentum, etc., in finding its optimum. ...
With this generalization, we are presented with two difficulties. ...
Compared with an earlier method Stochastic Neighbor Embedding (SNE) [6] , SSNE uses a symmetrized cost function with simpler gradients. ...
dblp:conf/nips/YangKXO09
fatcat:iotx2rzsgbegtean6gpc4b4jmu
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