**BART** is a [denoising autoencoder](https://paperswithcode.com/method/denoising-autoencoder) for pretraining sequence-to-sequence models. It is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard [Transformer](https://paperswithcode.com/method/transformer)-based neural machine translation architecture. It uses a standard seq2seq/NMT architecture with a bidirectional encoder (like [BERT](https://paperswithcode.com/method/bert)) and a left-to-right decoder (like [GPT](https://paperswithcode.com/method/gpt)). This means the encoder's attention mask is fully visible, like BERT, and the decoder's attention mask is causal, like [GPT2](https://paperswithcode.com/method/gpt-2).

_**Independent component analysis** (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals._

_ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples. In the model, the data variables are assumed to be linear mixtures of some unknown latent variables, and the mixing system is also unknown. The latent variables are assumed nongaussian and mutually independent, and they are called the independent components of the observed data. These independent components, also called sources or factors, can be found by ICA._

_ICA is superficially related to principal component analysis and factor analysis. ICA is a much more powerful technique, however, capable of finding the underlying factors or sources when these classic methods fail completely._


Extracted from (https://www.cs.helsinki.fi/u/ahyvarin/whatisica.shtml)

**Source papers**:

[Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture](https://doi.org/10.1016/0165-1684(91)90079-X)

[Independent component analysis, A new concept?](https://doi.org/10.1016/0165-1684(94)90029-9)

[Independent component analysis: algorithms and applications](https://doi.org/10.1016/S0893-6080(00)00026-5)

BART

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

**Confidence Calibration with an Auxiliary Class**, or **CCAC**, is a post-hoc confidence calibration method for DNN classifiers on OOD datasets. The key feature of CCAC is an auxiliary class in the calibration model which separates mis-classified samples from correctly classified ones, thus effectively mitigating the target DNN’s being confidently wrong. It also reduces the number of free parameters in CCAC to reduce free parameters and facilitate transfer to a new unseen dataset.

Source	BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com