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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 351–375 of 9051 papers

TitleStatusHype
Latent Video Dataset Distillation—0
Instruction-Tuning Data Synthesis from Scratch via Web ReconstructionCode1
Computational Typology—0
A species of Coprococcus is related to BMI in patients who underwent malabsorptive bariatric surgery and its abundance is modified by magnesium and thiamin intake—0
Agricultural Economics and Innovation in the Inca Empire—0
RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity SearchCode1
Bringing Diversity from Diffusion Models to Semantic-Guided Face Asset Generation—0
DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party DialogueCode0
Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions—0
Surrogate Fitness Metrics for Interpretable Reinforcement Learning—0
DeepPD: Joint Phase and Object Estimation from Phase Diversity with Neural Calibration of a Deformable Mirror—0
A Multimodal Recaptioning Framework to Account for Perceptual Diversity in Multilingual Vision-Language Modeling—0
Diverse Prompts: Illuminating the Prompt Space of Large Language Models with MAP-Elites—0
Adaptation Method for Misinformation Identification—0
Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling—0
Exploring Language Patterns of Prompts in Text-to-Image Generation and Their Impact on Visual Diversity—0
PipeWeaver: Addressing Data Dynamicity in Large Multimodal Model Training with Dynamic Interleaved Pipeline—0
Entropy Rectifying Guidance for Diffusion and Flow Models—0
MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space—0
MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation—0
Information Gain-Guided Causal Intervention for Autonomous Debiasing Large Language Models—0
Enhancing Explainability and Reliable Decision-Making in Particle Swarm Optimization through Communication Topologies—0
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research—0
NoisyRollout: Reinforcing Visual Reasoning with Data AugmentationCode2
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled DataCode0
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