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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 1701–1725 of 9051 papers

TitleStatusHype
Anchor-Controlled Generative Adversarial Network for High-Fidelity Electromagnetic and Structurally Diverse Metasurface Design—0
Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce ClassificationCode1
PDSR: A Privacy-Preserving Diversified Service Recommendation Method on Distributed Data—0
Merging and Splitting Diffusion Paths for Semantically Coherent PanoramasCode1
Leveraging Open Knowledge for Advancing Task Expertise in Large Language ModelsCode0
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology—0
DiffSurf: A Transformer-based Diffusion Model for Generating and Reconstructing 3D Surfaces in Pose—0
DualSpeech: Enhancing Speaker-Fidelity and Text-Intelligibility Through Dual Classifier-Free Guidance—0
EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory—0
Text3DAug -- Prompted Instance Augmentation for LiDAR PerceptionCode1
ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty—0
SwiftBrush v2: Make Your One-step Diffusion Model Better Than Its TeacherCode0
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
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
Is Functional Correctness Enough to Evaluate Code Language Models? Exploring Diversity of Generated CodesCode0
Segment Any Mesh: Zero-shot Mesh Part Segmentation via Lifting Segment Anything 2 to 3DCode2
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text GenerationCode1
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
T3M: Text Guided 3D Human Motion Synthesis from SpeechCode1
Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation—0
MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification—0
Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning—0
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
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