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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 2761–2770 of 9051 papers

TitleStatusHype
SoFA: Shielded On-the-fly Alignment via Priority Rule FollowingCode0
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning—0
Federated Learning for Estimating Heterogeneous Treatment Effects—0
Image-Text Matching with Multi-View Attention—0
RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices—0
DivAvatar: Diverse 3D Avatar Generation with a Single Prompt—0
RECOST: External Knowledge Guided Data-efficient Instruction Tuning—0
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-TuningCode1
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward EncodingsCode2
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data—0
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