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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 41914200 of 9051 papers

TitleStatusHype
FONT: Flow-guided One-shot Talking Head Generation with Natural Head Motions0
Utilizing Reinforcement Learning for de novo Drug DesignCode0
Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical StudyCode0
All You Need Is Sex for Diversity0
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions0
Asymmetric Image Retrieval with Cross Model Compatible Ensembles0
Online Camera-to-ground Calibration for Autonomous Driving0
SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and ModelingCode2
KD-DLGAN: Data Limited Image Generation via Knowledge Distillation0
A View From Somewhere: Human-Centric Face RepresentationsCode1
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