Summary of Transformer In-context Learning For Categorical Data, by Aaron T. Wang and Ricardo Henao and Lawrence Carin
Transformer In-Context Learning for Categorical Data
by Aaron T. Wang, Ricardo Henao, Lawrence Carin
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an extension to in-context learning with functional data, focusing on moving closer to language models by considering categorical outcomes and nonlinear underlying models. The approach uses contextual data, where each instance is associated with a categorical label drawn from a distribution dependent on covariates. A latent function model is introduced, with the probability of observing a class modeled as the output components of the function via softmax. The Transformer parameters are trained using multiple contextual examples and then applied to new data for few-shot learning. The goal is to estimate the probability of each category for a new query. The paper assumes each component of the latent function resides in a reproducing kernel Hilbert space, specifying the functional class. Analysis and experiments suggest that the Transformer implements gradient descent on the underlying function during its forward pass, connected to the latent vector function associated with softmax. This few-shot-learning methodology is demonstrated using the ImageNet dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how a type of artificial intelligence called Transformers can learn new things quickly by looking at examples in context. The researchers want to make this learning process more like what humans do when we learn language. They’re trying to figure out how the Transformer’s attention mechanism works and how it can be used for tasks like image classification. To do this, they use a special type of data that has categorical labels (like 0 or 1) associated with each instance. They show that their approach can be very effective by testing it on a large dataset called ImageNet. |
Keywords
» Artificial intelligence » Attention » Few shot » Gradient descent » Image classification » Probability » Softmax » Transformer
