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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

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Showing 451–460 of 9051 papers

TitleStatusHype
BLEU might be Guilty but References are not InnocentCode1
Diverse Generative Perturbations on Attention Space for Transferable Adversarial AttacksCode1
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary SpaceCode1
Diverse Image Captioning with Context-Object Split Latent SpacesCode1
De novo Drug Design using Reinforcement Learning with Multiple GPT AgentsCode1
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic DataCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
Difficulty-Aware Simulator for Open Set RecognitionCode1
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