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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 371380 of 9051 papers

TitleStatusHype
Augmenting Sequential Recommendation with Balanced Relevance and DiversityCode1
HyperMARL: Adaptive Hypernetworks for Multi-Agent RLCode1
Interpreting single-cell and spatial omics data using deep neural network training dynamicsCode1
U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMsCode1
Towards Rich Emotions in 3D Avatars: A Text-to-3D Avatar Generation BenchmarkCode1
A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency LossesCode1
T2Vid: Translating Long Text into Multi-Image is the Catalyst for Video-LLMsCode1
DELT: A Simple Diversity-driven EarlyLate Training for Dataset DistillationCode1
Global Tensor Motion PlanningCode1
Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image ClassificationCode1
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