**Boost-GNN** is an architecture that trains GBDT and GNN jointly to get the best of both worlds: the GBDT model deals with heterogeneous features, while GNN accounts for the graph structure. The model benefits from end-to-end optimization by allowing new trees to fit the gradient updates of GNN.

**1-bit Adam** is a [stochastic optimization](https://paperswithcode.com/methods/category/stochastic-optimization) technique that is a variant of [ADAM](https://paperswithcode.com/method/adam) with error-compensated 1-bit compression, based on finding that Adam's variance term becomes stable at an early stage. First vanilla Adam is used for a few epochs as a warm-up. After the warm-up stage, the compression stage starts and we stop updating the variance term $\mathbf{v}$ and use it as a fixed precondition. At the compression stage, we communicate based on the momentum applied with error-compensated 1-bit compression. The momentums are quantized into 1-bit representation (the sign of each element). Accompanying the vector, a scaling factor is computed as $\frac{\text { magnitude of compensated gradient }}{\text { magnitude of quantized gradient }}$. This scaling factor ensures that the compressed momentum has the same magnitude as the uncompressed momentum. This 1-bit compression could reduce the communication cost by $97 \%$ and $94 \%$ compared to the original float 32 and float 16 training, respectively.

1-bit Adam

1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed

Boost-GNN

Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

RMSNorm regularizes the summed inputs to a neuron in one layer according to root mean square (RMS), giving the model re-scaling invariance property and implicit learning rate adaptation ability. RMSNorm is computationally simpler and thus more efficient than LayerNorm.

Source	Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com