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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 901925 of 9051 papers

TitleStatusHype
Evaluating the Evaluation of Diversity in Natural Language GenerationCode1
Evaluating Logical Generalization in Graph Neural NetworksCode1
Distributed speech separation in spatially unconstrained microphone arraysCode1
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill DiversityCode1
Leveraging Knowledge Bases And Parallel Annotations For Music Genre TranslationCode1
LGD-GCN: Local and Global Disentangled Graph Convolutional NetworksCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
DivClust: Controlling Diversity in Deep ClusteringCode1
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity MaximizationCode1
Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog ArchitecturesCode1
Diverse and Admissible Trajectory Prediction through Multimodal Context UnderstandingCode1
Diverse and Admissible Trajectory Forecasting through Multimodal Context UnderstandingCode1
Diverse Image-to-Image Translation via Disentangled RepresentationsCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior InferenceCode1
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline GenerationCode1
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal AnchorsCode1
Diverse Image Generation via Self-Conditioned GANsCode1
Diverse Generative Perturbations on Attention Space for Transferable Adversarial AttacksCode1
Everyone Deserves A Reward: Learning Customized Human PreferencesCode1
Active Teacher for Semi-Supervised Object DetectionCode1
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary SpaceCode1
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition ChallengesCode1
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