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A model adaptation procedure then utilizes the training data to conduct a directed search through the space of possible models. The search requires that ...
It is argued that it is necessary to combine these two classes of adaptation algorithms, using evidence collected through theoretical analysis and simulated ...
Traditional model selection techniques involve training all candidate models in order to select the one that best balances training performance and expected ...
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We present a method to automatically explore a large space of models of varying complexities, organized based on the structure of the example space. ... This ...
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Sep 20, 2010 · We present a method to automatically explore a large space of models of varying complexities, organized based on the structure of the example ...
In adapting models, it is paramount to reduce the danger of overfitting. ... Our work on learning model structure is related to previous work on graphical-model.
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In this paper we tackle the problem of unsupervised domain adaptation for the task of semantic segmentation, where we attempt to transfer the knowledge ...
In this article, we introduce an innovative masking approach leveraging genre and topicality information to tailor language models to specialized domains.
May 23, 2024 · We propose an unsupervised adaptation framework, Self-TAught Recognizer (STAR), which leverages unlabeled data to enhance the robustness of automatic speech ...
Domain adaptive text classification is a challeng- ing problem for the large-scale pretrained language models because they often require expensive addi-.