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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 4651–4675 of 9051 papers

TitleStatusHype
Designing a Robust Radiology Report Generation System—0
Designing Data: Proactive Data Collection and Iteration for Machine Learning—0
Designing Recommender Systems to Depolarize—0
Market Design with Distributional Objectives—0
DesnowNet: Context-Aware Deep Network for Snow Removal—0
Detail-Preserving Latent Diffusion for Stable Shadow Removal—0
Detecting Bone Lesions in X-Ray Under Diverse Acquisition Conditions—0
Detecting patterns of species diversification in the presence of both rate shifts and mass extinctions—0
Detecting Quality Problems in Data Models by Clustering Heterogeneous Data Values—0
Detecting Sockpuppets in Deceptive Opinion Spam—0
Detecting Trojaned DNNs Using Counterfactual Attributions—0
Detection and Measurement of Syntactic Templates in Generated Text—0
Determinantal Beam Search—0
Determinantal consensus clustering—0
Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis—0
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework—0
Development of Multi-level Linguistic Alignment in Child-adult Conversations—0
Deviations from universality in the fluctuation behavior of a heterogeneous complex system reveal intrinsic properties of components: The case of the international currency market—0
Device-Free Localization Using Multi-Link MIMO Channels in Distributed Antenna Networks—0
Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation—0
DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification—0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning—0
DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning—0
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception—0
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