**IB-BERT**, or **Inverted Bottleneck BERT**, is a [BERT](https://paperswithcode.com/method/bert) variant that uses an [inverted bottleneck](https://paperswithcode.com/method/inverted-residual-block) structure. It is used as a teacher network to train the [MobileBERT](https://paperswithcode.com/method/mobilebert) models.

**SAINT** is a hybrid deep learning approach to solving tabular data problems. SAINT performs attention over both rows and columns, and it includes an enhanced embedding method. The architecture, pre-training and training pipeline are as follows: 

- $L$ layers with 2 attention blocks each, one self-attention block, and a novel intersample attention blocks that computes attention across samples are used.
- For pre-training, this involves minimizing the contrastive and denoising losses between a given data point and its views generated by [CutMix](https://paperswithcode.com/method/cutmix) and [mixup](https://paperswithcode.com/method/mixup). During finetuning/regular training, data passes through an embedding layer and then the SAINT model. Lastly, the contextual embeddings from SAINT are used to pass only the embedding corresponding to the CLS token through an [MLP](https://paperswithcode.com/method/feedforward-network) to obtain the final prediction.

SAINT

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

IB-BERT

MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices

**Iterative Pseudo-Labeling** (IPL) is a semi-supervised algorithm for speech recognition which efficiently performs multiple iterations of pseudo-labeling on unlabeled data as the acoustic model evolves. In particular, IPL fine tunes an existing model at each iteration using both labeled data and a subset of unlabeled data.

Source	MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com