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Found 4 papers, 2 papers with code
Large Language Models at Work in China's Labor Market
We then aggregate occupation exposure to the industry level to obtain industry exposure scores.
It Ain't That Bad: Understanding the Mysterious Performance Drop in OOD Generalization for Generative Transformer Models
Specifically, these models map unseen OOD inputs to outputs with equivalence relations in the ID domain.
Deep Generative Modeling with Backward Stochastic Differential Equations
This paper proposes a novel deep generative model, called BSDE-Gen, which combines the flexibility of backward stochastic differential equations (BSDEs) with the power of deep neural networks for generating high-dimensional complex target data, particularly in the field of image generation.
GAMMT: Generative Ambiguity Modeling Using Multiple Transformers
We introduce a novel model called GAMMT (Generative Ambiguity Models using Multiple Transformers) for sequential data that is based on sets of probabilities.
