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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 5026–5050 of 9051 papers

TitleStatusHype
Exploring Novel Quality Diversity Methods For Generalization in Reinforcement Learning—0
Guiding AI-Generated Digital Content with Wireless Perception—0
Application-Driven AI Paradigm for Person Counting in Various Scenarios—0
Paraphrase Detection: Human vs. Machine ContentCode0
Optimizing the Procedure of CT Segmentation Labeling—0
SAOR: Single-View Articulated Object Reconstruction—0
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection—0
Controllable Inversion of Black-Box Face Recognition Models via Diffusion—0
Is ChatGPT A Good Keyphrase Generator? A Preliminary StudyCode0
Improving Generalization with Domain Convex GameCode0
On the Efficacy of Generalization Error Prediction Scoring Functions—0
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
More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking—0
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out—0
Diffusion-based Target Sampler for Unsupervised Domain Adaptation—0
Leaping Into Memories: Space-Time Deep Feature SynthesisCode0
SigVIC: Spatial Importance Guided Variable-Rate Image Compression—0
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity—0
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