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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 791800 of 9051 papers

TitleStatusHype
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewardsCode1
Value Functions are Control Barrier Functions: Verification of Safe Policies using Control TheoryCode1
Generative Flow Network for Listwise RecommendationCode1
GPT-FL: Generative Pre-trained Model-Assisted Federated LearningCode1
LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity OptimizationCode1
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior InferenceCode1
Spontaneous Symmetry Breaking in Generative Diffusion ModelsCode1
Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate DiffusionCode1
DiffSketching: Sketch Control Image Synthesis with Diffusion ModelsCode1
Rethinking Masked Language Modeling for Chinese Spelling CorrectionCode1
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