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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

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

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The Jumping Knowledge layer aggregation module from the "Representation Learning on Graphs with Jumping Knowledge Networks" paper. The topology adaptive graph convolutional networks operator from the "Topology Adaptive Graph Convolutional Networks" paper.

g., Dynamic Edge-Conditioned Filters in Convolutional Networks on Graphs paper, which overlays a regular grid of user-defined size over a point cloud and clusters all points within the same voxel.The Graph Neural Network from the "Principal Neighbourhood Aggregation for Graph Nets" paper, using the PNAConv operator for message passing. Applies a multi-layer Elman RNN with tanh ⁡ \tanh tanh or ReLU \text{ReLU} ReLU non-linearity to an input sequence. A sampling algorithm from the "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space" paper, which iteratively samples the most distant point with regard to the rest points. The self-attention pooling operator from the "Self-Attention Graph Pooling" and "Understanding Attention and Generalization in Graph Neural Networks" papers. The chebyshev spectral graph convolutional operator from the "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering" paper.

Applies batch normalization over a batch of heterogeneous features as described in the "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" paper. The Graph Neural Network Force Field (GNNFF) from the "Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture" paper. Convert the output of Captum attribution methods which is a tuple of attributions to two dictionaries with node and edge attribution tensors. Creates a criterion that measures the triplet loss given input tensors a a a, p p p, and n n n (representing anchor, positive, and negative examples, respectively), and a nonnegative, real-valued function ("distance function") used to compute the relationship between the anchor and positive example ("positive distance") and the anchor and negative example ("negative distance"). InstanceNorm3d module with lazy initialization of the num_features argument of the InstanceNorm3d that is inferred from the input.ConvTranspose1d module with lazy initialization of the in_channels argument of the ConvTranspose1d that is inferred from the input. The softmax aggregation operator based on a temperature term, as described in the "DeeperGCN: All You Need to Train Deeper GCNs" paper.

InstanceNorm1d module with lazy initialization of the num_features argument of the InstanceNorm1d that is inferred from the input. The RotatE model from the "RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space" paper. The ARMA graph convolutional operator from the "Graph Neural Networks with Convolutional ARMA Filters" paper.

The (translation-invariant) feature-steered convolutional operator from the "FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis" paper.

Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .

The Heterogeneous Graph Transformer (HGT) operator from the "Heterogeneous Graph Transformer" paper. g., the j j j-th channel of the i i i-th sample in the batched input is a 3D tensor input [ i , j ] \text{input}[i, j] input [ i , j ]). Applies layer normalization over each individual example in a batch of heterogeneous features as described in the "Layer Normalization" paper. The MetaPath2Vec model from the "metapath2vec: Scalable Representation Learning for Heterogeneous Networks" paper where random walks based on a given metapath are sampled in a heterogeneous graph, and node embeddings are learned via negative sampling optimization. The k-NN interpolation from the "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space" paper.



  • Fruugo ID: 258392218-563234582
  • EAN: 764486781913
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