clrs enhancement

[danielpatrickhug]

PGN with Jax implementation and NeurIPS 2020 paper____
├─Message-Passing Neural Network (MPNN) for Graph Convolutional Networks (GCNs)____
│ ├─■──"Applying Triplet Message Passing with HK Transforms in MPNN for Graph Neural Networks"____ ── Topic: 20
│ └─■──Implementation of Deep Sets (Zaheer et al., NeurIPS 2017) using adjacency matrices and memory networ ── Topic: 13
└─GATv2 Graph Attention Network with adjustable sizes of multi-head attention and residual connections
├─■──Graph Attention Network v2 architecture with adjustable head number and output size.____ ── Topic: 36
└─■──Processor factory with various models and configurations____ ── Topic: 25

[danielpatrickhug]

Postprocessing Decoder Output with Sinkhorn Algorithm and Hard Categorization____
     │         ├─Node Feature Decoding with Encoders and Decoders____
     │         │    ├─■──Position Encoding Function for Natural Language Processing____ ── Topic: 23
     │         │    └─Node feature decoding using decoders and edge features____
     │         │         ├─■──Creating Encoders with Xavier Initialization and Truncated Normal Distribution for Encoding Categori ── Topic: 33
     │         │         └─Node feature decoding with decoders and edge features____
     │         │              ├─■──Node feature decoding and encoding with decoders and edge features____ ── Topic: 2
     │         │              └─■──Graph diff decoders____ ── Topic: 32
     │         └─Postprocessing of decoder output in graph neural networks.____
     │              ├─Decoder Output Postprocessing with Sinkhorn Algorithm and Cross-Entropy Loss____
     │              │    ├─Message Passing Net with Time-Chunked Data Processing____
     │              │    │    ├─■──Python Class for Message Passing Model with Selectable Algorithm____ ── Topic: 26
     │              │    │    └─■──NetChunked message passing operation with LSTM states for time-chunked data____ ── Topic: 7
     │              │    └─Loss calculation for time-chunked training with scalar truth data.____
     │              │         ├─Loss calculation function for time-chunked training with scalar truth data.____
     │              │         │    ├─■──Loss calculation for time-chunked training data____ ── Topic: 4
     │              │         │    └─■──Logarithmic Sinkhorn Operator for Permutation Pointer Logits____ ── Topic: 10
     │              │         └─■──Decoder postprocessing with Sinkhorn operator____ ── Topic: 28
     │              └─Gradient Filtering for Optimizer Updates____
     │                   ├─■──Filtering processor parameters in Haiku models____ ── Topic: 30
     │                   └─■──Filtering null gradients for untrained parameters during optimization