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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 46814690 of 9051 papers

TitleStatusHype
Dialogue Language Model with Large-Scale Persona Data Engineering0
DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs0
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation0
DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning0
DIANES: A DEI Audit Toolkit for News Sources0
DIBS: Enhancing Dense Video Captioning with Unlabeled Videos via Pseudo Boundary Enrichment and Online Refinement0
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation0
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning0
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control0
Dictionary and Image Recovery from Incomplete and Random Measurements0
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