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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 40914100 of 9051 papers

TitleStatusHype
Importance Weighted Expectation-Maximization for Protein Sequence DesignCode0
Class-Balancing Diffusion ModelsCode1
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels0
Political Strategies to Overcome Climate Policy Obstructionism0
Controllable Data Augmentation for Context-Dependent Text-to-SQL0
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale InstructionsCode2
Generating images of rare concepts using pre-trained diffusion modelsCode1
IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers0
Motion-Conditioned Diffusion Model for Controllable Video Synthesis0
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation ProcessingCode0
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