machine learning models
Mathematical frameworks or algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed for each specific task.
- AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench
- Competitive Advantage Attacks to Decentralized Federated Learning
- DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models
- Epistemic Uncertainty for Generated Image Detection
- Faithful Group Shapley Value
- FedEL: Federated Elastic Learning for Heterogeneous Devices
- FlashMD: long-stride, universal prediction of molecular dynamics
- Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning
- Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding
- Let Brain Rhythm Shape Machine Intelligence for Connecting Dots on Graphs
- Localized Data Shapley: Accelerating Valuation for Nearest Neighbor Algorithms
- Point Cloud Synthesis Using Inner Product Transforms
- ProfiX: Improving Profile-Guided Optimization in Compilers with Graph Neural Networks
- Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets
- SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelism
- STARC-9: A Large-scale Dataset for Multi-Class Tissue Classification for CRC Histopathology
- Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution Detection
- Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness