The proliferation of generative models, combined with pretraining on web-scale data, raises
a timely question: what happens when these models are trained on their own generated …

Emerging marketplaces for large language models and other large-scale machine learning
(ML) models appear to exhibit market concentration, which has raised concerns about …

Researchers in empirical machine learning recently spotlighted their fears of so-called
Model Collapse. They imagined a discard workflow, where an initial generative model is …

Empirically, large-scale deep learning models often satisfy a neural scaling law: the test
error of the trained model improves polynomially as the model size and data size grow …

Transfer learning is an attractive framework for problems where there is a paucity of data, or
where data collection is costly. One common approach to transfer learning is referred to as" …

Imbalanced data and spurious correlations are common challenges in machine learning
and data science. Oversampling, which artificially increases the number of instances in the …

Training on mixtures of data distributions is now common in many modern machine learning
pipelines, useful for performing well on several downstream tasks. Group distributionally …

Качество обучающих данных широко признаётся важнейшим условием для успешного
создания моделей машинного обучения (ML), однако конкретные составляющие этого …

Training data quality is widely recognized as the main pre-requisite for constructing
successful Machine Learning (ML) models. However, the concrete aspects of the data …

The proliferation of generative models, combined with pretraining on web-scale data, raises
a timely question: what happens when these models are trained on their own generated …