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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 8901–8925 of 9051 papers

TitleStatusHype
Automatic difficulty management and testing in games using a framework based on behavior trees and genetic algorithmsCode0
ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSACode0
Deep Surrogate Assisted MAP-Elites for Automated Hearthstone DeckbuildingCode0
Success is in the Details: Evaluate and Enhance Details Sensitivity of Code LLMs through CounterfactualsCode0
On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative ComprehensionCode0
On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for Fintech ApplicationsCode0
Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity AlignmentCode0
Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion ModelsCode0
Sudoku-Bench: Evaluating creative reasoning with Sudoku variantsCode0
AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion ModelsCode0
Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data CollectionCode0
Token Manipulation Generative Adversarial Network for Text GenerationCode0
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight SharingCode0
Adaptation of olfactory receptor abundances for efficient codingCode0
Evaluating Neural Language Models as Cognitive Models of Language AcquisitionCode0
Gradient based sample selection for online continual learningCode0
Evaluating Fairness in Argument RetrievalCode0
Robust Few-Shot Ensemble Learning with Focal Diversity-Based PruningCode0
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement LearningCode0
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract DescriptionsCode0
Camera Style Adaptation for Person Re-identificationCode0
Evaluating Diversity in Automatic Poetry GenerationCode0
On Measuring the Diversity of Organizational NetworksCode0
Calibration-Free Driver Drowsiness Classification based on Manifold-Level AugmentationCode0
An Electoral Approach to Diversify LLM-based Multi-Agent Collective Decision-MakingCode0
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