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Found 4 papers, 1 papers with code
Deep Reinforcement Learning for Resource Allocation in Business Processes
We compare those results with two popular strategies that are widely used in the industry.
Applying SoftTriple Loss for Supervised Language Model Fine Tuning
We introduce a new loss function TripleEntropy, to improve classification performance for fine-tuning general knowledge pre-trained language models based on cross-entropy and SoftTriple loss.
Revisiting Distance Metric Learning for Few-Shot Natural Language Classification
Our systematic experiments have shown that under few-shot learning settings, particularly proxy-based DML losses can positively affect the fine-tuning and inference of a supervised language model.
Distance Metric Learning Loss Functions in Few-Shot Scenarios of Supervised Language Models Fine-Tuning
This paper presents an analysis regarding an influence of the Distance Metric Learning (DML) loss functions on the supervised fine-tuning of the language models for classification tasks.
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