Black Forest Labs' new Self-Flow technique makes training multimodal AI models 2.8x more efficient

This efficiency makes it viable for enterprises to move beyond generic off-the-shelf solutions and develop specialized models ...
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 ...
GitHub

aziz-abibulaiev/ml-nlp-risk-pipeline

Download and merge network / log datasets (e.g. CSIC 2010, Attack Simulation Lab) into /data/ml_numeric.csv with our numeric feature schema. Build a rules-supervised dataset ml_rules_silver.csv from ...
IEEE

IA-KNNR: A Novel Imbalance-Aware Approach for Handling Multi-Label Class Imbalance Problem

Abstract: Multi-label learning (MLL) is a supervised learning where the classifier needs to learn from the data where one instance can belong to more than one class (label). Due to its wide ...