**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.

Canonical Tensor Decomposition, trained with N3 regularizer

CP-N3

Canonical Tensor Decomposition for Knowledge Base Completion

LTLS

Log-time and Log-space Extreme Classification

There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success. However, network training becomes more difficult with increasing depth and training of very deep networks remains an open problem. In this extended abstract, we introduce a new architecture designed to ease gradient-based training of very deep networks. We refer to networks with this architecture as highway networks, since they allow unimpeded information flow across several layers on "information highways". The architecture is characterized by the use of gating units which learn to regulate the flow of information through a network. Highway networks with hundreds of layers can be trained directly using stochastic gradient descent and with a variety of activation functions, opening up the possibility of studying extremely deep and efficient architectures.

Source	Log-time and Log-space Extreme Classification
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com