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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 16011610 of 9051 papers

TitleStatusHype
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
Visualizing Temporal Topic Embeddings with a Compass0
Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language ModelsCode3
VAE-QWGAN: Addressing Mode Collapse in Quantum GANs via Autoencoding Priors0
Quantile Regression for Distributional Reward Models in RLHFCode0
Benchmarking Large Language Model Uncertainty for Prompt OptimizationCode0
Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science0
Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?0
Generalizing Alignment Paradigm of Text-to-Image Generation with Preferences through f-divergence Minimization0
Abnormal Event Detection In Videos Using Deep Embedding0
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