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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 84768500 of 9051 papers

TitleStatusHype
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation ModelsCode0
Spectrum-Diverse Neuroevolution with Unified Neural ModelsCode0
Ranking with Fairness ConstraintsCode0
First the worst: Finding better gender translations during beam searchCode0
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger designCode0
Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic EmbeddingsCode0
Moments in Time Dataset: one million videos for event understandingCode0
Monitoring Diversity of AI Conferences: Lessons Learnt and Future Challenges in the DivinAI ProjectCode0
Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling Clustering Algorithms on NUMA ArchitecturesCode0
RareGAN: Generating Samples for Rare ClassesCode0
FireFly A Synthetic Dataset for Ember Detection in WildfireCode0
Bags of Projected Nearest Neighbours: Competitors to Random Forests?Code0
Diverse Image Captioning with Grounded StyleCode0
Training language GANs from ScratchCode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
Rationale-based Opinion SummarizationCode0
Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit ProblemCode0
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation LearningCode0
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular MaximizationCode0
Fine-Grained Detoxification via Instance-Level Prefixes for Large Language ModelsCode0
The MiniPile Challenge for Data-Efficient Language ModelsCode0
Learning Diverse Options via InfoMax Termination CriticCode0
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent LossCode0
CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe GenerationCode0
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation LearningCode0
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