Conventus Law

US – DOL Proposes New Rule On Worker Classification: Key Changes And Implications For Employers.

Key Changes And Implications For Employers. Legal News and Analysis - USA - Labour & Employment - Conventus Law ...
IEEE

Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function

Abstract: Multi-label emotion classification (MLEC) for low-resource languages like Arabic faces significant challenges due to class imbalance and label correlations, particularly in accurately ...
IEEE

Learning Compact Semantic Information and Reliable Pseudo-labels for Incomplete Multi-View Multi-Label Classification

Abstract: Multi-view data encompasses various data types, including multi-feature, multi-sequence, and multi-modal data. Multi-view multi-label classification aims to leverage the rich semantic ...
EurekAlert!

Label attention meets deep learning: a novel model for ICD-11 automatic classification of Chinese EMRs

Electronic medical records (EMRs) enable healthcare institutions to digitally document patients’ clinical conditions, treatment processes, and diagnostic outcomes, supporting paperless clinical ...
GitHub

Single-label multi-classification or multi-label multi-classification?

I have read the paper and it seemed to be a single-label multi-classification problem. But the code use BCE and sigmoid instead of crossEntropy and softmax. So does it mean that the patient may have ...
GitHub

Label smoothing for multi-label classification

I tried applying label smoothing to my multi-label classification problem—given that my dataset is noisy and unbalanced, I thought it might help—but I ran into issue #40258 ...
FoodNavigator

‘Clean food’ classification created

As consumers are becoming more conscious of their health and wellbeing, many are turning towards natural, ‘clean’ foods - foods with few additives, made up largely of natural ingredients. Driven in ...
Frontiers

Dual consistent pseudo label generation for multi-source domain adaptation without source data for medical image segmentation

Unsupervised domain adaptation (UDA) aims to adapt a model learned from the source domain to the target domain. Thus, the model can obtain transferable knowledge even in target domain that does not ...