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Found 6 papers, 2 papers with code
SUPClust: Active Learning at the Boundaries
Active learning is a machine learning paradigm designed to optimize model performance in a setting where labeled data is expensive to acquire.
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
This study addresses the integration of diversity-based and uncertainty-based sampling strategies in active learning, particularly within the context of self-supervised pre-trained models.
What Determines the Price of NFTs?
In the evolving landscape of digital art, Non-Fungible Tokens (NFTs) have emerged as a groundbreaking platform, bridging the realms of art and technology.
Abstract Visual Reasoning Enabled by Language
While artificial intelligence (AI) models have achieved human or even superhuman performance in many well-defined applications, they still struggle to show signs of broad and flexible intelligence.
DAVA: Disentangling Adversarial Variational Autoencoder
We compare DAVA to models with optimal hyperparameters.
Robust Disentanglement of a Few Factors at a Time
Disentanglement is at the forefront of unsupervised learning, as disentangled representations of data improve generalization, interpretability, and performance in downstream tasks.
