scaled conjugate gradient backpropagation algorithm Search Results


empirical signature of backpropagation-like deep learning  (Greene gmbh)
90
Greene gmbh empirical signature of backpropagation-like deep learning
Empirical Signature Of Backpropagation Like Deep Learning, supplied by Greene gmbh, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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first-order feedforward backpropagator  (BlindSight GmbH)
90
BlindSight GmbH first-order feedforward backpropagator
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
First Order Feedforward Backpropagator, supplied by BlindSight GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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backpropagation  (Teknik Hizmetler)
86
Teknik Hizmetler backpropagation
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Backpropagation, supplied by Teknik Hizmetler, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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backpropagation neural network  (Teknik Hizmetler)
86
Teknik Hizmetler backpropagation neural network
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Backpropagation Neural Network, supplied by Teknik Hizmetler, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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backpropagation  (Verlag GmbH)
90
Verlag GmbH backpropagation
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Backpropagation, supplied by Verlag GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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backpropagation extract relevant features  (Kaggle Inc)
86
Kaggle Inc backpropagation extract relevant features
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Backpropagation Extract Relevant Features, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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spectrophotometric method  (Optik GmbH)
90
Optik GmbH spectrophotometric method
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Spectrophotometric Method, supplied by Optik GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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mnist dataset  (National Institute of Standards and Technology)
90
National Institute of Standards and Technology mnist dataset
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Mnist Dataset, supplied by National Institute of Standards and Technology, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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quickprop backpropagation algorithm  (NeuroDimension Inc)
90
NeuroDimension Inc quickprop backpropagation algorithm
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Quickprop Backpropagation Algorithm, supplied by NeuroDimension Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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neural networks  (SoftMax Inc)
90
SoftMax Inc neural networks
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Neural Networks, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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mcp-regularized backpropagation neural network  (SoftMax Inc)
90
SoftMax Inc mcp-regularized backpropagation neural network
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Mcp Regularized Backpropagation Neural Network, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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feed forward-backpropagation model  (Lianbo Technology Co Ltd)
90
Lianbo Technology Co Ltd feed forward-backpropagation model
(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order <t>feedforward</t> <t>backpropagator,</t> of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.
Feed Forward Backpropagation Model, supplied by Lianbo Technology Co Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


(a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order feedforward backpropagator, of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.

Journal: Philosophical Transactions of the Royal Society B: Biological Sciences

Article Title: Higher order thoughts in action: consciousness as an unconscious re-description process

doi: 10.1098/rstb.2011.0421

Figure Lengend Snippet: (a) Network architecture for the Iowa Gambling Task simulation (see [2], simulation 3). The network consists of a first-order feedforward backpropagator, of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. (b) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [2], simulations 1 and 2). The network consists of a first-order feedforward backpropagation autoassociator, of which the input and output units are connected through fixed weights to a second-order comparator, which in turn feeds forward into two wagering units.

Article Snippet: The network consists of a first-order feedforward backpropagator, of which the hidden units feed forward into a set of second-order hidden units, which in turn feed forward into two wagering units. ( b ) Network architecture for the Blindsight and artificial grammar learning (AGL) simulations (see [ 2 ], simulations 1 and 2).

Techniques: