Exponential Linear Units (ELUs) are a useful rectifier for constructing deep learning architectures, as they may speed up and otherwise improve learning by virtue of not have vanishing gradients and by having mean activations near zero. However, the ELU activation as parametrized in [1] is not continuously differentiable with respect to its input when the shape parameter alpha is not equal to 1. We present an alternative parametrization which is C1 continuous for all values of alpha, making the rectifier easier to reason about and making alpha easier to tune. This alternative parametrization has several other useful properties that the original parametrization of ELU does not: 1) its derivative with respect to x is bounded, 2) it contains both the linear transfer function and ReLU as special cases, and 3) it is scale-similar with respect to alpha.
$$\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))$$

**ReasonBERT** is a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid, contexts. It utilizes distant supervision to automatically connect multiple pieces of text and tables to create pre-training examples that require long-range reasoning. Different types of reasoning are simulated, including intersecting multiple pieces of evidence, bridging from one piece of evidence to another, and detecting unanswerable cases. 

Specifically, given a query sentence containing an entity pair, if we mask one of the entities, another sentence or table that contains the same pair of entities can likely be used as evidence to recover the masked entity. Moreover, to encourage deeper reasoning, multiple pieces of evidence are collected that are jointly used to recover the masked entities in the query sentence, allowing for the scattering of the masked entities among different pieces of evidence to mimic different types of reasoning. 

The Figure illustrates several examples using such distant supervision. In Ex. 1, a model needs to check multiple constraints (i.e., intersection reasoning type) and find “the beach soccer competition that is established in 1998.” In Ex. 2, a model needs to find “the type of the band that released Awaken the Guardian,” by first inferring the name of the band “Fates Warning” (i.e., bridging reasoning type). 

The masked entities in a query sentence are replaced with the [QUESTION] tokens. The new pre-training objective, span reasoning, then extracts the masked entities from the provided evidence. Existing LMs like [BERT](https://paperswithcode.com/method/bert) and [RoBERTa](https://paperswithcode.com/method/roberta) are augmented by continuing to train them with the new objective, which leads to ReasonBERT. Then query sentence and textual evidence are encoded via the LM. When tabular evidence is present, the structure-aware [transformer](https://paperswithcode.com/method/transformer) [TAPAS](https://paperswithcode.com/method/tapas) is used as the encoder to capture the table structure.

ReasonBERT

ReasonBERT: Pre-trained to Reason with Distant Supervision

CELU

CAGAM is a form of spatial attention mechanism that propagates attention from a known to an unknown context features thereby enhancing the unknown context for relevant pattern discovery. Usually the known context feature is a class activation map ([CAM](https://paperswithcode.com/method/cam)).

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