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Found 7 papers, 1 papers with code
Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks
These breadcrumbs are constructed by subtracting the weights from a pre-trained model before and after fine-tuning, followed by a sparsification process that eliminates weight outliers and negligible perturbations.
ToxBuster: In-game Chat Toxicity Buster with BERT
Detecting toxicity in online spaces is challenging and an ever more pressing problem given the increase in social media and gaming consumption.
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning
Class prototypes are evolved continually in the same latent space, enabling learning and prediction at any point.
Reliability of CKA as a Similarity Measure in Deep Learning
Comparing learned neural representations in neural networks is a challenging but important problem, which has been approached in different ways.
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning
Continual Learning research typically focuses on tackling the phenomenon of catastrophic forgetting in neural networks.
TIMBERT: Toponym Identifier For The Medical Domain Based on BERT
In this paper, we propose an approach to automate the process of place name detection in the medical domain to enable epidemiologists to better study and model the spread of viruses.
Toponym Identification in Epidemiology Articles - A Deep Learning Approach
These results underline the importance of domain specific embedding as well as specific linguistic features in toponym detection in medical journals.
