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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 791800 of 9051 papers

TitleStatusHype
Dyadic Interaction Modeling for Social Behavior GenerationCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
High-Fidelity Pluralistic Image Completion with TransformersCode1
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document SummarizationCode1
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn InteractionCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
AfriSenti: A Twitter Sentiment Analysis Benchmark for African LanguagesCode1
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
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