classification tasks
Machine learning tasks that involve categorizing input data into predefined classes or labels based on learned features.
- $\mu$PC: Scaling Predictive Coding to 100+ Layer Networks
- AION-1: Omnimodal Foundation Model for Astronomical Sciences
- Accelerating Feature Conformal Prediction via Taylor Approximation
- Adaptive Sigmoid Clipping for Balancing the Direction–Magnitude Mismatch Trade-off in Differentially Private Learning
- Adaptive Time Encoding for Irregular Multivariate Time-Series Classification
- Additive Models Explained: A Computational Complexity Approach
- BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection
- Conditional Representation Learning for Customized Tasks
- Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees
- Diffusing DeBias: Synthetic Bias Amplification for Model Debiasing
- Distance-informed Neural Processes
- FGBench: A Dataset and Benchmark for Molecular Property Reasoning at Functional Group-Level in Large Language Models
- FedIGL: Federated Invariant Graph Learning for Non-IID Graphs
- Learn2Mix: Training Neural Networks Using Adaptive Data Integration
- Learning from positive and unlabeled examples -Finite size sample bounds
- MolVision: Molecular Property Prediction with Vision Language Models
- Online Locally Differentially Private Conformal Prediction via Binary Inquiries
- Optimal Mistake Bounds for Transductive Online Learning
- PseuZO: Pseudo-Zeroth-Order Algorithm for Training Deep Neural Networks
- RoFt-Mol: Benchmarking Robust Fine-tuning with Molecular Graph Foundation Models
- Self Iterative Label Refinement via Robust Unlabeled Learning
- Split conformal classification with unsupervised calibration
- TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields
- Torch-Uncertainty: Deep Learning Uncertainty Quantification
- ToxicTextCLIP: Text-Based Poisoning and Backdoor Attacks on CLIP Pre-training
- Uncertainty Quantification with the Empirical Neural Tangent Kernel
- Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling
- Unifying Attention Heads and Task Vectors via Hidden State Geometry in In-Context Learning