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A scalable second order optimization algorithm for deep learning.

Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods, that involve second derivatives and/or second order statistics of the data, are far less prevalent despite strong theoretical properties, due to their prohibitive computation, memory and communication costs. In an attempt to bridge this gap between theoretical and practical optimization, we present a scalable implementation of a second-order preconditioned method (concretely, a variant of full-matrix Adagrad), that along with several critical algorithmic and numerical improvements, provides significant convergence and wall-clock time improvements compared to conventional first-order methods on state-of-the-art deep models. Our novel design effectively utilizes the prevalent heterogeneous hardware architecture for training deep models, consisting of a multicore CPU coupled with multiple accelerator units. We demonstrate superior performance compared to state-of-the-art on very large learning tasks such as machine translation with Transformers, language modeling with BERT, click-through rate prediction on Criteo, and image classification on ImageNet with ResNet-50.

Distributed Shampoo

Towards Practical Second Order Optimization for Deep Learning

Attention Sinks

Efficient Streaming Language Models with Attention Sinks

**Disentangled Attribution Curves (DAC)** provide interpretations of tree ensemble methods in the form of (multivariate) feature importance curves. For a given variable, or group of variables, [DAC](https://paperswithcode.com/method/dac) plots the importance of a variable(s) as their value changes.

The Figure to the right shows an example. The tree depicts a decision tree which performs binary classification using two features (representing the XOR function). In this problem, knowing the value of one of the features without knowledge of the other feature yields no information - the classifier still has a 50% chance of predicting either class. As a result, DAC produces curves which assign 0 importance to either feature on its own. Knowing both features yields perfect information about the classifier, and thus the DAC curve for both features together correctly shows that the interaction of the features produces the model’s predictions.

Source	Efficient Streaming Language Models with Attention Sinks
Year	2000
Data Source	Papers with Code archive (CC BY-SA)