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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 601610 of 9051 papers

TitleStatusHype
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
A Diverse Corpus for Evaluating and Developing English Math Word Problem SolversCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
Controllable Video Captioning with an Exemplar SentenceCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D DetectionCode1
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