**Residual Shuffle-Exchange Network** is an efficient alternative to models using an attention mechanism that allows the modelling of long-range dependencies in sequences in O(n log n) time. This model achieved state-of-the-art performance on the MusicNet dataset for music transcription while being able to run inference on a single GPU fast enough to be suitable for real-time audio processing.

ERNIE is a transformer-based model consisting of two stacked modules: 1) textual encoder and 2) knowledgeable encoder, which is responsible to integrate extra token-oriented knowledge information into textual information. This layer consists of stacked aggregators, designed for encoding both tokens and entities as well as fusing their heterogeneous features. To integrate this layer of enhancing representations via knowledge, a special pre-training task is adopted for ERNIE - it involves randomly masking token-entity alignments and training the model to predict all corresponding entities based on aligned tokens (aka denoising entity auto-encoder).

ERNIE

ERNIE: Enhanced Representation through Knowledge Integration

Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences

**Neural Additive Models (NAMs)** make restrictions on the structure of neural networks, which yields a family of models that are inherently interpretable while suffering little loss in prediction accuracy when applied to tabular data. Methodologically, NAMs belong to a larger model family called Generalized Additive Models (GAMs). 

NAMs learn a linear combination of networks that each attend to a single input feature: each $f\_{i}$ in the traditional GAM formulationis parametrized by a neural network. These networks are trained jointly using backpropagation and can learn arbitrarily complex shape functions. Interpreting NAMs is easy as the impact of a feature on the prediction does not rely on the other features and can be understood by visualizing its corresponding shape function (e.g., plotting $f\_{i}\left(x\_{i}\right)$ vs. $x\_{i}$).

Source	Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com