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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 931940 of 9051 papers

TitleStatusHype
SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-SpoofingCode1
Dataset Factorization for CondensationCode1
Lila: A Unified Benchmark for Mathematical ReasoningCode1
Tree Detection and Diameter Estimation Based on Deep LearningCode1
SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular ControlCode1
Space-time design for deep joint source channel coding of images Over MIMO channelsCode1
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification AlgorithmsCode1
Track2Vec: fairness music recommendation with a GPU-free customizable-driven frameworkCode1
How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?Code1
Towards standardizing Korean Grammatical Error Correction: Datasets and AnnotationCode1
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