diversity
Diversity in AI generally refers to the range of characteristics represented in the training data, models, or algorithmic approaches. Ensuring diversity is crucial for creating equitable AI systems that perform well across various demographic groups and conditions.
- 3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processes
- A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective
- Adaptive Divergence Regularized Policy Optimization for Fine-tuning Generative Models
- Alchemist: Turning Public Text-to-Image Data into Generative Gold
- DiCoFlex: Model-Agnostic Diverse Counterfactuals with Flexible Control
- Entropy Rectifying Guidance for Diffusion and Flow Models
- Feedback Guidance of Diffusion Models
- From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review
- Generating Creative Chess Puzzles
- MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
- Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models
- Preserving LLM Capabilities through Calibration Data Curation: From Analysis to Optimization
- Salient Concept-Aware Generative Data Augmentation