Neural Structured Learning (NSL) - Revision history

BPeat at 14:20, 23 February 2020

2020-02-23T14:20:20Z

BPeat at 02:48, 22 December 2019

2019-12-22T02:48:44Z

BPeat at 09:36, 5 November 2019

2019-11-05T09:36:05Z

BPeat at 01:21, 5 November 2019

2019-11-05T01:21:14Z

BPeat at 01:19, 5 November 2019

2019-11-05T01:19:54Z

BPeat at 01:13, 5 November 2019

2019-11-05T01:13:11Z

BPeat: Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M..."

2019-11-05T01:07:02Z

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[http://www.youtube.com/results?search_query=Neural+Structured+Learning+NSL+Graph Youtube search...]
[http://www.google.com/search?q=Neural+Structured+Learning+NSL+Graph ...Google search]

* [[Knowledge Graphs]]
* [[Self-Organizing]]

<img src="http://zdnet2.cbsistatic.com/hub/i/r/2018/10/20/39b41e82-91f1-4c46-acbd-bf6ffdbb7010/resize/370xauto/5747e621e6954148703aeb29e39399e9/image.jpg" width="600" height="350">

<youtube>2Ucq7a8CY94</youtube>
<youtube>LvmjbXZyoP0</youtube>

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 [http://www.google.com/search?q=Neural+Structured+Learning+NSL+Graph ...Google search]
 * [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
 * [http://medium.com/tensorflow/introducing-neural-structured-learning-in-tensorflow-5a802efd7afd Introducing Neural Structured Learning in TensorFlow | Da-Cheng Juan and Sujith Ravi - Medium]

@@ Line 11: / Line 11: @@
 * [http://medium.com/tensorflow/introducing-neural-structured-learning-in-tensorflow-5a802efd7afd Introducing Neural Structured Learning in TensorFlow | Da-Cheng Juan and Sujith Ravi - Medium]
 * [http://cs.stanford.edu/people/esteva/nature/ Skin Cancer Classification With Deep Learning | Andre Esteva, B. Kuprel, R. Novoa, J. Ko, S. Swetter, H. Blau & S.Thrun]
 a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph or implicit as induced by adversarial perturbation. Structured signals are commonly used to represent relations or similarity among samples that may be labeled or unlabeled. Therefore, leveraging these signals during neural network training harnesses both labeled and unlabeled data, which can improve model accuracy, particularly when the amount of labeled data is relatively small. Additionally, models trained with samples that are generated by adding adversarial perturbation have been shown to be robust against malicious attacks, which are designed to mislead a model's prediction or classification. [http://www.tensorflow.org/neural_structured_learning Neural Structured Learning | TensorFlow.org]

@@ Line 10: / Line 10: @@
 * [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
 * [http://medium.com/tensorflow/introducing-neural-structured-learning-in-tensorflow-5a802efd7afd Introducing Neural Structured Learning in TensorFlow | Da-Cheng Juan and Sujith Ravi - Medium]
 a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph or implicit as induced by adversarial perturbation. Structured signals are commonly used to represent relations or similarity among samples that may be labeled or unlabeled. Therefore, leveraging these signals during neural network training harnesses both labeled and unlabeled data, which can improve model accuracy, particularly when the amount of labeled data is relatively small. Additionally, models trained with samples that are generated by adding adversarial perturbation have been shown to be robust against malicious attacks, which are designed to mislead a model's prediction or classification. [http://www.tensorflow.org/neural_structured_learning Neural Structured Learning | TensorFlow.org]

@@ Line 9: / Line 9: @@
 * [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
-* [http://medium.com/tensorflow/introducing-neural-structured-learning-in-tensorflow-5a802efd7afd Introducing Neural Structured Learning in TensorFlow | Da-Cheng Juan and Sujith Ravi]
+* [http://medium.com/tensorflow/introducing-neural-structured-learning-in-tensorflow-5a802efd7afd Introducing Neural Structured Learning in TensorFlow | Da-Cheng Juan and Sujith Ravi - Medium]
 a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph or implicit as induced by adversarial perturbation. Structured signals are commonly used to represent relations or similarity among samples that may be labeled or unlabeled. Therefore, leveraging these signals during neural network training harnesses both labeled and unlabeled data, which can improve model accuracy, particularly when the amount of labeled data is relatively small. Additionally, models trained with samples that are generated by adding adversarial perturbation have been shown to be robust against malicious attacks, which are designed to mislead a model's prediction or classification. [http://www.tensorflow.org/neural_structured_learning Neural Structured Learning | TensorFlow.org]
-<img src="http://www.tensorflow.org/neural_structured_learning/images/nsl_overview.png" width="600" height="350">
+<img src="http://www.tensorflow.org/neural_structured_learning/images/nsl_overview.png" width="700" height="350">
 <img src="http://miro.medium.com/max/1600/0*WnRpPeVuEu3TpL8X" width="1000" height="550">
 <youtube>2Ucq7a8CY94</youtube>

@@ Line 9: / Line 9: @@
 * [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
 a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph or implicit as induced by adversarial perturbation. Structured signals are commonly used to represent relations or similarity among samples that may be labeled or unlabeled. Therefore, leveraging these signals during neural network training harnesses both labeled and unlabeled data, which can improve model accuracy, particularly when the amount of labeled data is relatively small. Additionally, models trained with samples that are generated by adding adversarial perturbation have been shown to be robust against malicious attacks, which are designed to mislead a model's prediction or classification. [http://www.tensorflow.org/neural_structured_learning Neural Structured Learning | TensorFlow.org]
-<img src="https://www.tensorflow.org/neural_structured_learning/images/nsl_overview.png" width="600" height="350">
+<img src="http://www.tensorflow.org/neural_structured_learning/images/nsl_overview.png" width="600" height="350">
 <youtube>2Ucq7a8CY94</youtube>