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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

Showing 651660 of 9051 papers

TitleStatusHype
Metric Space Magnitude for Evaluating the Diversity of Latent RepresentationsCode1
Efficient Dataset Distillation via Minimax DiffusionCode1
Cerbero-7B: A Leap Forward in Language-Specific LLMs Through Enhanced Chat Corpus Generation and EvaluationCode1
Multi-modal In-Context Learning Makes an Ego-evolving Scene Text RecognizerCode1
Generating Progressive Images from Pathological Transitions via Diffusion ModelCode1
Multi-Task Reinforcement Learning with Mixture of Orthogonal ExpertsCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Safer-Instruct: Aligning Language Models with Automated Preference DataCode1
MC^2: Towards Transparent and Culturally-Aware NLP for Minority Languages in ChinaCode1
Towards Reasoning in Large Language Models via Multi-Agent Peer Review CollaborationCode1
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