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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 3981–3990 of 9051 papers

TitleStatusHype
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database—0
A Critical Reexamination of Intra-List Distance and Dispersion—0
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning—0
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference—0
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLPCode0
GrACE: Generation using Associated Code Edits—0
Active Learning Principles for In-Context Learning with Large Language Models—0
Co-Learning Empirical Games and World Models—0
Enhancing Chat Language Models by Scaling High-quality Instructional ConversationsCode4
Sensing Diversity and Sparsity Models for Event Generation and Video Reconstruction from EventsCode0
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