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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 3076–3100 of 9051 papers

TitleStatusHype
LLMs Prompted for Graphs: Hallucinations and Generative Capabilities—0
Sparse Uncertainty-Informed Sampling from Federated Streaming DataCode0
Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning—0
From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMsCode0
DiverseDialogue: A Methodology for Designing Chatbots with Human-Like Diversity—0
Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling—0
PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification—0
MSLIQA: Enhancing Learning Representations for Image Quality Assessment through Multi-Scale Learning—0
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity—0
United in Diversity? Contextual Biases in LLM-Based Predictions of the 2024 European Parliament Elections—0
Iterative Graph AlignmentCode0
Illuminating the Diversity-Fitness Trade-Off in Black-Box OptimizationCode0
Anchor-Controlled Generative Adversarial Network for High-Fidelity Electromagnetic and Structurally Diverse Metasurface Design—0
PDSR: A Privacy-Preserving Diversified Service Recommendation Method on Distributed Data—0
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology—0
Leveraging Open Knowledge for Advancing Task Expertise in Large Language ModelsCode0
DiffSurf: A Transformer-based Diffusion Model for Generating and Reconstructing 3D Surfaces in Pose—0
EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory—0
SwiftBrush v2: Make Your One-step Diffusion Model Better Than Its TeacherCode0
ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty—0
DualSpeech: Enhancing Speaker-Fidelity and Text-Intelligibility Through Dual Classifier-Free Guidance—0
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing—0
Bridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systems—0
Diversity and Multiplexing for Continuous Aperture Array (CAPA)-Based Communications—0
Localization and Expansion: A Decoupled Framework for Point Cloud Few-shot Semantic Segmentation—0
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