32 L6.2 - Basics of Deep Learning 29:31 L6.3 - Deep Learning for Graphs 35:41 L7.1 - A General Perspective on GNN 05:51 L7.2 - A Single Layer of a GNN 40:09 L7.3 - Stacking layers of a GNN 18:11 L8.1 - Graph Augmentation for GNNs 27:50 L8.2 - Training Graph Neural ...
These sorts of graphs come up all the time in computer science, especially in talking about functional programs. They are very closely related to the notions of dependency graphs and call graphs. They’re also the core abstraction behind the popular deep learning frameworkTheano. We can evaluate ...
These sorts of graphs come up all the time in computer science, especially in talking about functional programs. They are very closely related to the notions of dependency graphs and call graphs. They’re also the core abstraction behind the popular deep learning frameworkTheano. We can evaluate ...
Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applications. Here we rep
Many state-of-the-art Deep Neural Networks (DNNs) have substantial memory requirements. Limited device memory becomes a bottleneck when training those models. We propose ParDNN, an automatic, generic, and non-intrusive partitioning strategy for DNNs that are represented as computational graphs. Par...
http://www.deeplearningbook.org/section 6.5.1 for more information on computational graphs and the rest of the book for more details about ML/deep learning. Python3.5 or above numpy- linear algebra for Python scipy- Scientific Python library, here used for utilities ...
Graph neural networks (GNNs) process large-scale graphs consisting of a hundred billion edges. In contrast to traditional deep learning, unique behaviors of the emerging GNNs are engaged with a large set of graphs and embedding data on storage, which exhibits complex and irregular preprocessing. We...
Enabling efficient analysis of biobank-scale data with genotype representation graphs The genotype representation graph (GRG) is a compact data structure that encodes 200,000 human genomes in just 5–26 gigabytes per chromosome. Computation on GRG via graph traversal greatly accelerates genome-wide...
Our library allows automatic bound derivation and computation for general computational graphs, in a similar manner that gradients are obtained in modern deep learning frameworks -- users only define the computation in a forward pass, andauto_LiRPAtraverses through the computational graph and derives ...
Symmetry properties of chemical graphs. V. Internal rotation in XY3XY2XY3 Journal of Computational ChemistryRandic, M.; Davis, M. I. Symmetry properties of chemical graphs. VI. isomerizations of octahedral complexes. Int. J. ... M Randi? - 《Journal of Computational Chemistry》 被引量: 58...