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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 1376–1400 of 9051 papers

TitleStatusHype
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense InferenceCode1
CamContextI2V: Context-aware Controllable Video GenerationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin TonesCode1
Contrastive Syn-to-Real GeneralizationCode1
Learning High-Quality and General-Purpose Phrase RepresentationsCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
Learning Semantic-Aligned Feature Representation for Text-based Person SearchCode1
Learning Semantic Latent Directions for Accurate and Controllable Human Motion PredictionCode1
Can 3D Vision-Language Models Truly Understand Natural Language?Code1
Learning to Generate Novel Scene Compositions from Single Images and VideosCode1
Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled DataCode1
Learning to Regrasp by Learning to PlaceCode1
Controllable Multi-Interest Framework for RecommendationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Length-Controllable Image CaptioningCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection BiasCode1
Leveraging Knowledge Bases And Parallel Annotations For Music Genre TranslationCode1
Lila: A Unified Benchmark for Mathematical ReasoningCode1
Can LLMs Patch Security Issues?Code1
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