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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 5301–5325 of 9051 papers

TitleStatusHype
Multi-Sample ζ-mixup: Richer, More Realistic Synthetic Samples from a p-Series Interpolant—0
Defending Active Directory by Combining Neural Network based Dynamic Program and Evolutionary Diversity Optimisation—0
A Novel 3D Non-Stationary Channel Model for 6G Indoor Visible Light Communication Systems—0
Evolutionary Diversity Optimisation for The Traveling Thief Problem—0
High-Quality Pluralistic Image Completion via Code Shared VQGAN—0
Exploring the influence of fine-tuning data on wav2vec 2.0 model for blind speech quality prediction—0
Failed Disruption Propagation in Integer Genetic Programming—0
Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences—0
Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization—0
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review—0
Introduction to the Artificial Intelligence that can be applied to the Network Automation Journey—0
End-to-end Learnable Diversity-aware News Recommendation—0
Socratic Models: Composing Zero-Shot Multimodal Reasoning with LanguageCode0
Filter-based Discriminative Autoencoders for Children Speech Recognition—0
SimVQA: Exploring Simulated Environments for Visual Question Answering—0
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset—0
Rethinking Video Salient Object Ranking—0
Leverage Your Local and Global Representations: A New Self-Supervised Learning StrategyCode1
Artificial Intelligence: Framework of driving triggers to past, present and future applications and influencers of industry sector adoption—0
Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword SpottingCode1
MAT: Mask-Aware Transformer for Large Hole Image InpaintingCode2
On Decoding Strategies for Neural Text Generators—0
Online Continual Learning on a Contaminated Data Stream with Blurry Task BoundariesCode1
Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images—0
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