A new kind of implicit models, where the output of the network is defined as the solution to an "infinite-level" fixed point equation. Thanks to this we can compute the gradient of the output without activations and therefore with a significantly reduced memory footprint.

An effective approach to the quantification of patchiness in biomedical images according to their local edge densities.

PALED

Segmentation of patchy areas in biomedical images based on local edge density estimation

Deep Equilibrium Models

**XLNet** is an autoregressive [Transformer](https://paperswithcode.com/method/transformer) that leverages the best of both autoregressive language modeling and autoencoding while attempting to avoid their limitations. Instead of using a fixed forward or backward factorization order as in conventional autoregressive models, XLNet maximizes the expected log likelihood of a sequence w.r.t. all possible permutations of the factorization order. Thanks to the permutation operation, the context for each position can consist of tokens from both left and right. In expectation, each position learns to utilize contextual information from all positions, i.e., capturing bidirectional context.

Additionally, inspired by the latest advancements in autogressive language modeling, XLNet integrates the segment recurrence mechanism and relative encoding scheme of [Transformer-XL](https://paperswithcode.com/method/transformer-xl) into pretraining, which empirically improves the performance especially for tasks involving a longer text sequence.

Source	Deep Equilibrium Models
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com