regression tasks
Regression tasks are a type of supervised learning problem where the goal is to predict continuous numerical values based on input features. In AI, regression tasks often involve modeling relationships between variables to inform predictions, such as estimating prices or forecasting outcomes.
- AION-1: Omnimodal Foundation Model for Astronomical Sciences
- Accelerating Feature Conformal Prediction via Taylor Approximation
- Additive Models Explained: A Computational Complexity Approach
- Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees
- Distance-informed Neural Processes
- Enhancing Deep Batch Active Learning for Regression with Imperfect Data Guided Selection
- Epistemic Uncertainty Estimation in Regression Ensemble Models with Pairwise Epistemic Estimators
- FGBench: A Dataset and Benchmark for Molecular Property Reasoning at Functional Group-Level in Large Language Models
- Learn2Mix: Training Neural Networks Using Adaptive Data Integration
- MolVision: Molecular Property Prediction with Vision Language Models
- Online Locally Differentially Private Conformal Prediction via Binary Inquiries
- RoFt-Mol: Benchmarking Robust Fine-tuning with Molecular Graph Foundation Models
- Torch-Uncertainty: Deep Learning Uncertainty Quantification
- Uncertainty Quantification with the Empirical Neural Tangent Kernel