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Found 5 papers, 4 papers with code
Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters
Bias mitigation of Language Models has been the topic of many studies with a recent focus on learning separate modules like adapters for on-demand debiasing.
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale
We introduce ScaLearn, a simple and highly parameter-efficient two-stage MTL method that capitalizes on the knowledge of the source tasks by learning a minimal set of scaling parameters that enable effective knowledge transfer to a target task.
Domain Information Control at Inference Time for Acoustic Scene Classification
In the Acoustic Scene Classification task (ASC), domain shift is mainly caused by different recording devices.
Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers
Furthermore, we will show that transformers trained on Audioset can be extremely effective representation extractors for a wide range of downstream tasks.
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks
Societal biases are reflected in large pre-trained language models and their fine-tuned versions on downstream tasks.
