Oct 21, 2023 · This paper presents three contributions. First, we introduce a scenario where the embedding of a pre-trained model is served through a gated API.
Few-shot classification involves training a model to perform a new classification task with a handful of labeled data. This paper presents three contributions.
Our proposed regularization is widely applicable and model-agnostic, and boosts the performance of any few-shot learning model, including fine-tuning, metric- ...
Mar 1, 2024 · Few-shot classification involves training a model to perform a new classification task with a handful of labeled data.
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models. Colombo, P., Pellegrain, V., Boudiaf, M., Tami, M., Storchan, V., ...
Mar 1, 2024 · Few-shot classification involves training a model to perform a new classification task with a handful of labeled data.
Our paper proposes a transductive few-shot learning algorithm that utilizes a novel parameter-free Fisher-Rao based loss. By leveraging only the network's ...
Transductive learning for textual few-shot classification in API-based ...
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Mar 4, 2024 · Transductive learning for textual few-shot classification in API-based embedding models. Colombo, Pierre, Pellegrain, Victor, Boudiaf, Malik ...
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Oct 21, 2023 · This pa- per is centered on the fundamental task of few- shot text classification, specifically focusing on cloud-based/API access. Specifically ...
Oct 24, 2023 · It consists of six steps based on the threat modeling framework from cybersecurity. We analyze cybersecurity risk and the risk of non-consensual ...