Avanika Narayan

TL;DR: The authors provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e. g.g. model architectures, training procedures, data, systems, security, evaluation, theory) to their applications.

TL;DR: This paper develops an understanding of the effective prompt formats and proposes to use weak supervision, a procedure for combining the noisy predictions, to produce the final predictions of the GPT-Neo-6B model.

TL;DR: This paper has developed Rekall, a library that exposes a data model and programming model for compositional video event specification and provides operators for composing labels into queries that model new video events.

TL;DR: It is found that large FMs generalize and achieve SoTA performance on data cleaning and integration tasks, even though they are not trained for these data tasks.

TL;DR: This work proves that adding a weighted class-conditional InfoNCE loss to SupCon controls the degree of spread, and studies three mechanisms to break permutation invariance: using a constrained encoder, adding a class-Conditional autoencoder, and using data augmentation.