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Found 6 papers, 1 papers with code
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation
Large pretrained language models (LMs) like BERT have improved performance in many disparate natural language processing (NLP) tasks.
Speaker attribution with voice profiles by graph-based semi-supervised learning
Speaker attribution is required in many real-world applications, such as meeting transcription, where speaker identity is assigned to each utterance according to speaker voice profiles.
Speaker diarization with session-level speaker embedding refinement using graph neural networks
Deep speaker embedding models have been commonly used as a building block for speaker diarization systems; however, the speaker embedding model is usually trained according to a global loss defined on the training data, which could be sub-optimal for distinguishing speakers locally in a specific meeting session.
Training without training data: Improving the generalizability of automated medical abbreviation disambiguation
Abbreviation disambiguation is important for automated clinical note processing due to the frequent use of abbreviations in clinical settings.
Centroid-based deep metric learning for speaker recognition
Speaker embedding models that utilize neural networks to map utterances to a space where distances reflect similarity between speakers have driven recent progress in the speaker recognition task.
A mixture model for the evolution of gene expression in non-homogeneous datasets
We address the challenge of assessing conservation of gene expression in complex, non-homogeneous datasets.
