276°
Posted 20 hours ago

NN/A Amuse-MIUMIU Girls' Bikini Swimsuits for Children Cow Print Two Piece Swimwear Adjustable Shoulder Strap Bandeau Top Swimwear with Swimming Floors 8-12 Years

£3.14£6.28Clearance
ZTS2023's avatar
Shared by
ZTS2023
Joined in 2023
82
63

About this deal

The Neural Fingerprint model from the "Convolutional Networks on Graphs for Learning Molecular Fingerprints" paper to generate fingerprints of molecules.

The Heterogeneous Graph Transformer (HGT) operator from the "Heterogeneous Graph Transformer" paper. The dynamic edge convolutional operator from the "Dynamic Graph CNN for Learning on Point Clouds" paper (see torch_geometric. Conv2d module with lazy initialization of the in_channels argument of the Conv2d that is inferred from the input.The Graph Neural Network from the "Semi-supervised Classification with Graph Convolutional Networks" paper, using the GCNConv operator for message passing. The crystal graph convolutional operator from the "Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties" paper. Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.

Performs GRU aggregation in which the elements to aggregate are interpreted as a sequence, as described in the "Graph Neural Networks with Adaptive Readouts" paper. Furthermore, an interesting discussion concerns the trade-off between representational power (usually gained through learnable functions implemented as neural networks) and the formal property of permutation invariance ( Buterez et al. Applies the Exponential Linear Unit (ELU) function, element-wise, as described in the paper: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). Conv1d module with lazy initialization of the in_channels argument of the Conv1d that is inferred from the input.Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . The chebyshev spectral graph convolutional operator from the "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering" paper. The dynamic neighborhood aggregation operator from the "Just Jump: Towards Dynamic Neighborhood Aggregation in Graph Neural Networks" paper.

The graph convolutional operator with initial residual connections and identity mapping (GCNII) from the "Simple and Deep Graph Convolutional Networks" paper. The Graph Neural Network from the "Principal Neighbourhood Aggregation for Graph Nets" paper, using the PNAConv operator for message passing. The Weisfeiler Lehman (WL) operator from the "A Reduction of a Graph to a Canonical Form and an Algebra Arising During this Reduction" paper.The LightGCN model from the "LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation" paper. Applies a multi-layer Elman RNN with tanh ⁡ \tanh tanh or ReLU \text{ReLU} ReLU non-linearity to an input sequence. Applies graph normalization over individual graphs as described in the "GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training" paper. The PointGNN operator from the "Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud" paper. The general, powerful, scalable (GPS) graph transformer layer from the "Recipe for a General, Powerful, Scalable Graph Transformer" paper.

Asda Great Deal

Free UK shipping. 15 day free returns.
Community Updates
*So you can easily identify outgoing links on our site, we've marked them with an "*" symbol. Links on our site are monetised, but this never affects which deals get posted. Find more info in our FAQs and About Us page.
New Comment