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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 551560 of 9051 papers

TitleStatusHype
Toward Stable World Models: Measuring and Addressing World Instability in Generative Environments0
Oasis: One Image is All You Need for Multimodal Instruction Data SynthesisCode1
Preserving Product Fidelity in Large Scale Image Recontextualization with Diffusion Models0
Contrastive Speaker-Aware Learning for Multi-party Dialogue Generation with LLMs0
ESPnet-SDS: Unified Toolkit and Demo for Spoken Dialogue Systems0
DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics AwarenessCode3
An Exhaustive Evaluation of TTS- and VC-based Data Augmentation for ASR0
Additive Frequency Diverse Active Incoherent Millimeter-Wave Imaging0
ResBench: Benchmarking LLM-Generated FPGA Designs with Resource Awareness0
MaizeField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity PanelCode0
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