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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 4226–4250 of 9051 papers

TitleStatusHype
TAPS3D: Text-Guided 3D Textured Shape Generation from Pseudo SupervisionCode1
Is ChatGPT A Good Keyphrase Generator? A Preliminary StudyCode0
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection—0
Improving Generalization with Domain Convex GameCode0
Re-thinking Federated Active Learning based on Inter-class DiversityCode1
Distribution-restrained Softmax Loss for the Model Robustness—0
Experimental Validation of Single BS 5G mmWave Positioning and Mapping for Intelligent Transport—0
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images—0
Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning—0
EPiC: Ensemble of Partial Point Clouds for Robust ClassificationCode0
Pluralistic Aging Diffusion Autoencoder—0
Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain AdaptationCode0
Two Kinds of Recall—0
Diffusion-based Target Sampler for Unsupervised Domain Adaptation—0
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out—0
Leaping Into Memories: Space-Time Deep Feature SynthesisCode0
More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking—0
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
SigVIC: Spatial Importance Guided Variable-Rate Image Compression—0
Taming Diffusion Models for Audio-Driven Co-Speech Gesture GenerationCode2
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity—0
Active Teacher for Semi-Supervised Object DetectionCode1
An End-to-End Multi-Task Learning Model for Image-based Table RecognitionCode1
Redrawing attendance boundaries to promote racial and ethnic diversity in elementary schools—0
Diversity-Aware Meta Visual PromptingCode1
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