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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 5826–5850 of 9051 papers

TitleStatusHype
TSO: Self-Training with Scaled Preference Optimization—0
Multiplayer Battle Game-Inspired Optimizer for Complex Optimization Problems—0
Multiple Captions Embellished Multilingual Multi-Modal Neural Machine Translation—0
Multiple-Choice Question Generation: Towards an Automated Assessment Framework—0
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes—0
Multiple Embeddings for Quantum Machine Learning—0
Multiple Fairness and Cardinality constraints for Students-Topics Grouping Problem—0
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems—0
Multiple Phase Transitions Shape Biodiversity of a Migrating Population—0
Multiple Population Alternate Evolution Neural Architecture Search—0
Multiple Realizability and the Rise of Deep Learning—0
When Harry Meets Superman: The Role of The Interlocutor in Persona-Based Dialogue Generation—0
Multi-population GAN Training: Analyzing Co-Evolutionary Algorithms—0
Tuning-Free Amodal Segmentation via the Occlusion-Free Bias of Inpainting Models—0
Multi-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesis—0
Multi-Resolution Generative Modeling of Human Motion from Limited Data—0
A Survey on 3D Skeleton Based Person Re-Identification: Approaches, Designs, Challenges, and Future Directions—0
A survey of part-of-speech tagging approaches applied to K’iche’—0
Multi-Sample ζ-mixup: Richer, More Realistic Synthetic Samples from a p-Series Interpolant—0
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration—0
Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion—0
Multi-Scale Implicit Transformer with Re-parameterize for Arbitrary-Scale Super-Resolution—0
Multi-scale Latent Point Consistency Models for 3D Shape Generation—0
Multi-scale Template Matching with Scalable Diversity Similarity in an Unconstrained Environment—0
MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input—0
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