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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 2426–2450 of 9051 papers

TitleStatusHype
Multi-scale Latent Point Consistency Models for 3D Shape Generation—0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration—0
Focusing Image Generation to Mitigate Spurious Correlations—0
Learning states enhanced knowledge tracing: Simulating the diversity in real-world learning process—0
Diverse Rare Sample Generation with Pretrained GANsCode0
UniAvatar: Taming Lifelike Audio-Driven Talking Head Generation with Comprehensive Motion and Lighting Control—0
Enhanced Recommendation Combining Collaborative Filtering and Large Language Models—0
Experimental Study of RCS Diversity with Novel No-divergent OAM Beams—0
Dissecting CLIP: Decomposition with a Schur Complement-based ApproachCode0
LatentCRF: Continuous CRF for Efficient Latent Diffusion—0
Survey of Pseudonymization, Abstractive Summarization & Spell Checker for Hindi and Marathi—0
DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation—0
Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning—0
IITR-CIOL@NLU of Devanagari Script Languages 2025: Multilingual Hate Speech Detection and Target Identification in Devanagari-Scripted Languages—0
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation—0
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models—0
Boosting LLM via Learning from Data Iteratively and SelectivelyCode0
DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak—0
Detail-Preserving Latent Diffusion for Stable Shadow Removal—0
BEE: Metric-Adapted Explanations via Baseline Exploration-ExploitationCode0
DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network—0
A diversity-enhanced genetic algorithm for efficient exploration of parameter spacesCode0
Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?—0
Acquisition of Recursive Possessives and Recursive Locatives in Mandarin—0
AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning—0
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