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Found 6 papers, 1 papers with code
Robust feature knowledge distillation for enhanced performance of lightweight crack segmentation models
RFKD distils knowledge from a teacher model's logit layers and intermediate feature maps while leveraging mixed clean and noisy images to transfer robust patterns to the student model, improving its precision, generalisation, and anti-noise performance.
A BiRGAT Model for Multi-intent Spoken Language Understanding with Hierarchical Semantic Frames
Previous work on spoken language understanding (SLU) mainly focuses on single-intent settings, where each input utterance merely contains one user intent.
Exploiting Global Contextual Information for Document-level Named Entity Recognition
To this end, we propose a model called Global Context enhanced Document-level NER (GCDoc) to leverage global contextual information from two levels, i. e., both word and sentence.
Observation of Magnetic Droplets in Magnetic Tunnel Junctions
Magnetic droplets, a class of highly non-linear magnetodynamical solitons, can be nucleated and stabilized in nanocontact spin-torque nano-oscillators where they greatly increase the microwave output power.
Applied Physics
A Survey on Recent Advances in Sequence Labeling from Deep Learning Models
Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks, e. g., part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc.
Position-Aware Self-Attention based Neural Sequence Labeling
Sequence labeling is a fundamental task in natural language processing and has been widely studied.
