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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 28612870 of 9051 papers

TitleStatusHype
On Provable Length and Compositional GeneralizationCode0
AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based PoliciesCode2
What is "Typological Diversity" in NLP?Code0
On Practical Diversified Recommendation with Controllable Category Diversity FrameworkCode0
Learning immune receptor representations with protein language models0
INSIDE: LLMs' Internal States Retain the Power of Hallucination DetectionCode1
Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing0
Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting0
SISP: A Benchmark Dataset for Fine-grained Ship Instance Segmentation in Panchromatic Satellite ImagesCode1
Improved Generalization of Weight Space Networks via AugmentationsCode0
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