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Found 8 papers, 5 papers with code
PG$^2$Net: Personalized and Group Preferences Guided Network for Next Place Prediction
Targeting this, we propose an end-to-end framework named personalized and group preference guided network (PG$^2$Net), considering the users' preferences to various places at both individual and collective levels.
Playing Technique Detection by Fusing Note Onset Information in Guzheng Performance
Because each Guzheng playing technique is applied to a note, a dedicated onset detector is trained to divide an audio into several notes and its predictions are fused with frame-wise IPT predictions.
Frame-Level Multi-Label Playing Technique Detection Using Multi-Scale Network and Self-Attention Mechanism
Instrument playing technique (IPT) is a key element of musical presentation.
Instrument Playing Technique Detection
Multi-Label Classification
Source-free Active Domain Adaptation for Diabetic Retinopathy Grading Based on Ultra-wide-field Fundus Image
Domain adaptation (DA) has been widely applied in the diabetic retinopathy (DR) grading of unannotated ultra-wide-field (UWF) fundus images, which can transfer annotated knowledge from labeled color fundus images.
MERTech: Instrument Playing Technique Detection Using Self-Supervised Pretrained Model With Multi-Task Finetuning
Recognizing the significance of pitch in capturing the nuances of IPTs and the importance of onset in locating IPT events, we investigate multi-task finetuning with pitch and onset detection as auxiliary tasks.
Instrument Playing Technique Detection
Self-Supervised Learning
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
When an ML algorithm interacts with its environment, the algorithm can affect the data-generating mechanism and be a major source of bias when evaluating its standalone performance, an issue known as performativity.
A Brief Tutorial on Sample Size Calculations for Fairness Audits
In fairness audits, a standard objective is to detect whether a given algorithm performs substantially differently between subgroups.
A hierarchical decomposition for explaining ML performance discrepancies
Machine learning (ML) algorithms can often differ in performance across domains.
