Abstract: Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advances in ...

Powered by our Opposite-Score embeddings and three rigorously curated datasets (Debate ︎ Defeasible NLI ︎ Causal Reasoning). Efficient embeddings and scoring mechanism for detecting contrasting or ...

Abstract: Uncertainty quantification in spatiotemporal forecasting is crucial for decision-making. Quantile regression has been proposed as a computationally efficient and assumption-free approach for ...