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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 951–975 of 9051 papers

TitleStatusHype
LatentCRF: Continuous CRF for Efficient Latent Diffusion—0
IITR-CIOL@NLU of Devanagari Script Languages 2025: Multilingual Hate Speech Detection and Target Identification in Devanagari-Scripted Languages—0
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models—0
DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak—0
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation—0
Boosting LLM via Learning from Data Iteratively and SelectivelyCode0
BEE: Metric-Adapted Explanations via Baseline Exploration-ExploitationCode0
Detail-Preserving Latent Diffusion for Stable Shadow Removal—0
Optimal signal transmission and timescale diversity in a model of human brain operating near criticalityCode1
A diversity-enhanced genetic algorithm for efficient exploration of parameter spacesCode0
DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network—0
Acquisition of Recursive Possessives and Recursive Locatives in Mandarin—0
Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?—0
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
Segmentation-based Extraction of Key Components from ECG Images: A Framework for Precise Classification and Digitization—0
Adversarial Robustness through Dynamic Ensemble Learning—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
A Review of the Marathi Natural Language Processing—0
Ethics and Technical Aspects of Generative AI Models in Digital Content Creation—0
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement LearningCode0
AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning—0
Watertox: The Art of Simplicity in Universal Attacks A Cross-Model Framework for Robust Adversarial Generation—0
DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization—0
The First Multilingual Model For The Detection of Suicide Texts—0
The impact of behavioral diversity in multi-agent reinforcement learning—0
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