Neural Execution of Graph AlgorithmsPetar VelikoviRex YingMatilde PadovanoRaia HadsellCharles BlundellInternational Conference on Learning Representations
Neural Execution of Graph Algorithms. ICLR 2020. paper Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell. GraphSAINT: Graph Sampling Based Inductive Learning Method. ICLR 2020. paper Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna. ...
Stack-NMN, N2NMN + differentiable memory stack + soft program execution 2018 Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding arXiv Paper Code NS-VQA, N2NMN + scene graph 2018 Compositional Models for VQA: Can Neural Module Networks Really Count? BICA Paper intere...
A typical approach is to rely on Graph Neural Network (GNN) architectures, which encode inputs in high-dimensional latent spaces that are repeatedly transformed during the execution of the algorithm. In this work we perform a detailed analysis of the structure of the latent space induced by the...
A special hardware solution for decreasing execution time When neural networks are used with fewer processing units and weights, software simulation is performed directly on the computer, e.g., voice recognition. When Neural Network Algorithms develop to the point where useful things can be done ...
Choose Graph to view the pipeline steps. Run the SageMaker pipeline locally for ogbn-arxiv The ogbn-arxiv dataset is small enough that you can run the pipeline locally. Run the following command to start a local execution of the pipeline: # Allow the local containers to in...
It involves the use of algorithms that can be trained (i.e., learn) to make predictions based on current observations. Among several different algorithms, the use of deep neural networks (DNN) has been the dominant approach for a wide range of data problems10. Specifically, efforts in both...
The algorithmic execution of the model is then represented as iterative forward and backward propagation on such a computation graph. Node features consist of, for example, the operator type (e.g., Conv2D), hyperparameters (e.g., the kernel size), number of floating-point operations (FLOPs)...
Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing framework exhibit a shallow structure, ...
2019.05-Dynamic Neural Network Channel Execution for Efficient Training 2019.06-AutoGrow: Automatic Layer Growing in Deep Convolutional Networks 2019.06-BasisConv: A method for compressed representation and learning in CNNs 2019.06-BlockSwap: Fisher-guided Block Substitution for Network Compression 2019.06-Sepa...