How can uncertainty estimation be incorporated into fine-tuned models?

We present GOODFIT, one of the first optimizers that has been specifically designed to operate on converged models that need to be incrementally fine-tuned ... Apr 15, 2020 ... The most common incarnation of transfer learning in the context of deep learning is the following workflow: Take layers from a previously ... Nov 21, 2017 ... Fine-tuning is a way of applying or utilizing transfer learning. Specifically, fine-tuning is a process that takes a model that has already been ... May 24, 2019 ... It integrates the huggingface library with the fastai library to fine-tune the BERT model, with an application on an old Kaggle competition. Mar 15, 2024 ... Fine-tuning in machine learning is the process of adapting a pre-trained model for specific tasks or use cases. Jun 15, 2024 ... Many methods have been proven effective in improving model quality, efficiency, and resource consumption in Deep Learning. The distinction ... only fine-tune the last layer of the deep neural network: here, the last layer of the source model is kept but serves as an initialization of the target Machine ... Apr 2, 2025 ... Generative AI models or foundation models are large ML models pre-trained with the intention that they are to be fine-tuned for more specific ... ... Fine-tuning, distillation, and prompt engineering in the Machine Learning Crash Course. How fine-tuning works. The goal of fine-tuning is to further improve ... Feb 13, 2025 ... Fine-tuning is a form of transfer learning where the pre-trained model is adjusted to better fit the new data. There are several steps involved ...