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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 211220 of 9051 papers

TitleStatusHype
DiffusionLight: Light Probes for Free by Painting a Chrome BallCode2
Boosting Latent Diffusion with Flow MatchingCode2
Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction ModelCode2
ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single ImageCode2
ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot CoordinationCode2
GenSim: Generating Robotic Simulation Tasks via Large Language ModelsCode2
Grasp-Anything: Large-scale Grasp Dataset from Foundation ModelsCode2
RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose EstimationCode2
Residual Denoising Diffusion ModelsCode2
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language ModelsCode2
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