Existing work has attempted to alleviate these problems by blurring faces, downsampling videos, or training on synthetic data. On the other hand, analysis on the transferability of privacy-preserving pre-trained models to downstream tasks has been limited.

For this reason, the ML community started focusing on non-monotone problems with extra structural properties.

Molecular Property Prediction (MPP) is a critical task in computational drug discovery, aimed at identifying molecules with desirable pharmacological and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Machine learning models have been widely used in this fast-growing field, with two types of models being commonly employed: traditional non-deep models and deep models.

Transformers may have sufficient supervision for generalization.

Since its inception in 1982, Oja's algorithm has become an established method for streaming principle component analysis (PCA).