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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 12611270 of 9051 papers

TitleStatusHype
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill DiversityCode1
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection BiasCode1
Amortizing intractable inference in large language modelsCode1
Diverse Topology Optimization using Modulated Neural FieldsCode1
DeepHuman: 3D Human Reconstruction from a Single ImageCode1
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
AutoMix: Automatically Mixing Language ModelsCode1
Automating Rigid Origami DesignCode1
Cerbero-7B: A Leap Forward in Language-Specific LLMs Through Enhanced Chat Corpus Generation and EvaluationCode1
GenPlot: Increasing the Scale and Diversity of Chart Derendering DataCode1
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