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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 5551–5575 of 9051 papers

TitleStatusHype
Towards Federated Learning on Time-Evolving Heterogeneous Data—0
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence—0
Lyapunov Exponents for Diversity in Differentiable Games—0
LAME: Layout Aware Metadata Extraction Approach for Research Articles—0
Multi-speaker Multi-style Text-to-speech Synthesis With Single-speaker Single-style Training Data Scenarios—0
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis—0
Maximum Entropy Population-Based Training for Zero-Shot Human-AI CoordinationCode1
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion GenerationCode1
Cross-Part Learning for Fine-Grained Image ClassificationCode0
Black-Box Testing of Deep Neural Networks Through Test Case DiversityCode0
Spiral Language Modeling—0
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsCode2
Effects of discordance between species and gene trees on phylogenetic diversity conservation—0
Turbo-Sim: a generalised generative model with a physical latent space—0
Taming Repetition in Dialogue Generation—0
Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarization—0
On the Use of Quality Diversity Algorithms for The Traveling Thief Problem—0
Multi-modal Networks Reveal Patterns of Operational Similarity of Terrorist OrganizationsCode0
Counting and optimising maximum phylogenetic diversity sets—0
Cultural Diversity and Its Impact on Governance—0
Tackling the Generative Learning Trilemma with Denoising Diffusion GANsCode1
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed ClassificationCode1
Text Classification Models for Form Entity LinkingCode1
An Informative Tracking BenchmarkCode1
GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANsCode0
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