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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 20912100 of 9051 papers

TitleStatusHype
Hyperparameter Auto-tuning in Self-Supervised Robotic LearningCode0
How Far Can We Extract Diverse Perspectives from Large Language Models?Code0
How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RECode0
Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric MaterialsCode0
Closed-Form Information Capacity of Canonical Signaling ModelsCode0
A Corpus-free State2Seq User Simulator for Task-oriented DialogueCode0
How Does A Text Preprocessing Pipeline Affect Ontology Syntactic Matching?Code0
How Inclusively do LMs Perceive Social and Moral Norms?Code0
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent LossCode0
A Corpus for Reasoning About Natural Language Grounded in PhotographsCode0
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