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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

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Showing 4211–4220 of 9051 papers

TitleStatusHype
A Closer Look at Few-shot Image Generation—0
Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning—0
Discovering Generalizable Skills via Automated Generation of Diverse Tasks—0
Discovering Evolutionary Stepping Stones through Behavior Domination—0
Beyond Static Models and Test Sets: Benchmarking the Potential of Pre-trained Models Across Tasks and Languages—0
Discovering Diverse Nearly Optimal Policies with Successor Features—0
Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks—0
3D-Aware Indoor Scene Synthesis with Depth Priors—0
Imagine yourself: Tuning-Free Personalized Image Generation—0
Img2Vec: A Teacher of High Token-Diversity Helps Masked AutoEncoders—0
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