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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 23012310 of 9051 papers

TitleStatusHype
Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question AnsweringCode0
CT-Eval: Benchmarking Chinese Text-to-Table Performance in Large Language Models0
Perturbing the Gradient for Alleviating Meta OverfittingCode0
Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in MammographyCode2
PT43D: A Probabilistic Transformer for Generating 3D Shapes from Single Highly-Ambiguous RGB ImagesCode1
Asymptotic theory of in-context learning by linear attentionCode0
Adversarially Diversified Rehearsal Memory (ADRM): Mitigating Memory Overfitting Challenge in Continual LearningCode0
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation0
Preparing for Black Swans: The Antifragility Imperative for Machine Learning0
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