**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.

**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.

IB-BERT

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

Iterative Pseudo-Labeling for Speech Recognition

A scalable second order optimization algorithm for deep learning.

Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods, that involve second derivatives and/or second order statistics of the data, are far less prevalent despite strong theoretical properties, due to their prohibitive computation, memory and communication costs. In an attempt to bridge this gap between theoretical and practical optimization, we present a scalable implementation of a second-order preconditioned method (concretely, a variant of full-matrix Adagrad), that along with several critical algorithmic and numerical improvements, provides significant convergence and wall-clock time improvements compared to conventional first-order methods on state-of-the-art deep models. Our novel design effectively utilizes the prevalent heterogeneous hardware architecture for training deep models, consisting of a multicore CPU coupled with multiple accelerator units. We demonstrate superior performance compared to state-of-the-art on very large learning tasks such as machine translation with Transformers, language modeling with BERT, click-through rate prediction on Criteo, and image classification on ImageNet with ResNet-50.

Source	Iterative Pseudo-Labeling for Speech Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com