**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.

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Attention Sinks

Efficient Streaming Language Models with Attention Sinks

Disentangled Attribution Curves

Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees

**SEER** is a self-supervised learning approach for training large models on random, uncurated images with no supervision. It trains [RegNet-Y](https://paperswithcode.com/method/regnet-y) architectures with the [SwAV](https://paperswithcode.com/method/swav). Several adjustments are made to self-supervised training to make it work at a larger scale, including using a [cosine learning schedule](https://paperswithcode.com/method/cosine-annealing)

Source	Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com