Difference between revisions of "Directed Acyclic Graph (DAG)"
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[http://www.google.com/search?q=Directed+Acyclic+Graph+DAG+pipeline+program+machine+learning ...Google search] | [http://www.google.com/search?q=Directed+Acyclic+Graph+DAG+pipeline+program+machine+learning ...Google search] | ||
| + | * [http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yang_Multi-Scale_Recognition_With_ICCV_2015_paper.pdf Multi-scale recognition with DAG-CNNs | Songfan Yang & Deva Ramanan] | ||
* [http://www.qubole.com/blog/the-key-to-building-data-pipelines-for-machine-learning-support-for-multiple-engines/ The Key to Building Data Pipelines for Machine Learning: Support for Multiple Engines | Jorge Villamariona - Qubole] | * [http://www.qubole.com/blog/the-key-to-building-data-pipelines-for-machine-learning-support-for-multiple-engines/ The Key to Building Data Pipelines for Machine Learning: Support for Multiple Engines | Jorge Villamariona - Qubole] | ||
* [http://www.ebayinc.com/stories/blogs/tech/running-arbitrary-dag-based-workflows-in-the-cloud/ Running Arbitrary DAG-based Workflows in the Cloud | Sachin Tilloo] | * [http://www.ebayinc.com/stories/blogs/tech/running-arbitrary-dag-based-workflows-in-the-cloud/ Running Arbitrary DAG-based Workflows in the Cloud | Sachin Tilloo] | ||
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* [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]] | * [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]] | ||
* [[Platforms: Machine Learning as a Service (MLaaS)]] | * [[Platforms: Machine Learning as a Service (MLaaS)]] | ||
| + | * [http://stats.stackexchange.com/questions/234694/can-all-neural-network-with-dag-topology-be-trained-by-back-prop Can all neural network having directed acyclic graph (DAG) topology be trained by back propagation methods? | StackExchange] | ||
A DAG network is a neural network for deep learning with layers arranged as a directed acyclic graph. A DAG network can have a more complex architecture in which layers have inputs from multiple layers and outputs to multiple layers. [http://www.mathworks.com/help/deeplearning/ref/dagnetwork.html DAGNetwork | MathWorks] | A DAG network is a neural network for deep learning with layers arranged as a directed acyclic graph. A DAG network can have a more complex architecture in which layers have inputs from multiple layers and outputs to multiple layers. [http://www.mathworks.com/help/deeplearning/ref/dagnetwork.html DAGNetwork | MathWorks] | ||
Revision as of 10:17, 3 March 2019
YouTube search... ...Google search
- Multi-scale recognition with DAG-CNNs | Songfan Yang & Deva Ramanan
- The Key to Building Data Pipelines for Machine Learning: Support for Multiple Engines | Jorge Villamariona - Qubole
- Running Arbitrary DAG-based Workflows in the Cloud | Sachin Tilloo
- Data Flow Graph (DFG)
- Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning
- Platforms: Machine Learning as a Service (MLaaS)
- Can all neural network having directed acyclic graph (DAG) topology be trained by back propagation methods? | StackExchange
A DAG network is a neural network for deep learning with layers arranged as a directed acyclic graph. A DAG network can have a more complex architecture in which layers have inputs from multiple layers and outputs to multiple layers. DAGNetwork | MathWorks
DAG workflow are set of tasks written with dependencies between the tasks rather than linearly chaining. 4 Reasons Why Your Machine Learning Code is Probably Bad - Norman Niemer - KDnuggets
DAG Technology: 'DAG Coins' as an Alternative to Blockchain/Cryptocurrencies
