Search Results for author:
Found 3 papers, 0 papers with code
Wasserstein Distributional Learning
To overcome this fundamental issue, we propose Wasserstein Distributional Learning (WDL), a flexible density-on-scalar regression modeling framework that starts with the Wasserstein distance $W_2$ as a proper metric for the space of density outcomes.
Weakly Supervised Learning Creates a Fusion of Modeling Cultures
The past two decades have witnessed the great success of the algorithmic modeling framework advocated by Breiman et al. (2001).
Cultural Vocal Bursts Intensity Prediction
Weakly-supervised Learning
Artificial Perceptual Learning: Image Categorization with Weak Supervision
In this paper, we propose a novel machine learning framework, artificial perceptual learning (APL), to tackle the problem of weakly supervised image categorization.
