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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 14511460 of 9051 papers

TitleStatusHype
Bahasa Harmony: A Comprehensive Dataset for Bahasa Text-to-Speech Synthesis with Discrete Codec Modeling of EnGen-TTS0
On the Modeling Capabilities of Large Language Models for Sequential Decision Making0
Diversity and Inclusion Index with Networks and Similarity: Analysis and its Application0
Diversity-Rewarded CFG Distillation0
Does RoBERTa Perform Better than BERT in Continual Learning: An Attention Sink PerspectiveCode0
Quality Diversity Imitation Learning0
Sparse Repellency for Shielded Generation in Text-to-image Diffusion Models0
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
Fill In The Gaps: Model Calibration and Generalization with Synthetic Data0
Task Diversity Shortens the ICL PlateauCode0
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