Canonical Tensor Decomposition, trained with N3 regularizer

**DistDGL** is a system for training GNNs in a mini-batch fashion on a cluster of machines. It is is based on the Deep Graph Library (DGL), a popular GNN development framework. DistDGL distributes the graph and its associated data (initial features and embeddings) across the machines and uses this distribution to derive a computational decomposition by following an owner-compute rule. DistDGL follows a synchronous training approach and allows ego-networks forming the mini-batches to include non-local nodes. To minimize the overheads associated with distributed computations, DistDGL uses a high-quality and light-weight mincut graph partitioning algorithm along with multiple balancing constraints. This allows it to reduce communication overheads and statically balance the computations. It further reduces the communication by replicating halo nodes and by using sparse embedding updates. The combination of these design choices allows DistDGL to train high-quality models while achieving high parallel efficiency and memory scalability

DistDGL

DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs

CP-N3

Canonical Tensor Decomposition for Knowledge Base Completion

**LTLS** is a technique for multiclass and multilabel prediction that can perform training and inference in logarithmic time and space. LTLS embeds large classification problems into simple structured prediction problems and relies on efficient dynamic programming algorithms for inference. It tackles extreme multi-class and multi-label classification problems where the size $C$ of the output space is extremely large.

Source	Canonical Tensor Decomposition for Knowledge Base Completion
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com