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Found 10 papers, 3 papers with code
Enhancing Job Salary Prediction with Disentangled Composition Effect Modeling: A Neural Prototyping Approach
Despite efforts on salary prediction based on job positions and talent demographics, there still lacks methods to effectively discern the set-structured skills' intricate composition effect on job salary.
A Comprehensive Survey on Self-Interpretable Neural Networks
Neural networks have achieved remarkable success across various fields.
Contrastive Conditional Alignment based on Label Shift Calibration for Imbalanced Domain Adaptation
Thus, we propose contrastive conditional alignment based on label shift calibration (CCA-LSC) for IDA, to address both covariate shift and label shift.
ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection
We propose the InstanceFusion module, which utilizes contrastive learning to generate similar instance features across heterogeneous modalities.
Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics
In the second stage, DFL-FS employs federated feature regeneration based on global feature statistics and utilizes resampling and weighted covariance to calibrate the global classifier to enhance the model's adaptability to long-tailed data distributions.
TaE: Task-aware Expandable Representation for Long Tail Class Incremental Learning
Existing methods mainly focus on preserving representative samples from previous classes to combat catastrophic forgetting.
centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation
Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that the label distribution is invariant across domains.
Dual-Branch Temperature Scaling Calibration for Long-Tailed Recognition
Moreover, we noticed that the traditional calibration evaluation metric, Excepted Calibration Error (ECE), gives a higher weight to low-confidence samples in the minority classes, which leads to inaccurate evaluation of model calibration.
Multi-Factors Aware Dual-Attentional Knowledge Tracing
In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism.
An Adaptive Oversampling Learning Method for Class-Imbalanced Fault Diagnostics and Prognostics
Data-driven fault diagnostics and prognostics suffers from class-imbalance problem in industrial systems and it raises challenges to common machine learning algorithms as it becomes difficult to learn the features of the minority class samples.
