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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 1951–1975 of 9051 papers

TitleStatusHype
Conformity bias in the cultural transmission of music sampling traditions—0
Configuring Antenna System to Enhance the Downlink Performance of High Velocity Users in 5G MU-MIMO Networks—0
Assessing fish abundance from underwater video using deep neural networks—0
Cross-modal Face- and Voice-style Transfer—0
Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation—0
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation—0
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech—0
ConfigTron: Tackling network diversity with heterogeneous configurations—0
Assessing Distractors in Multiple-Choice Tests—0
Crowd Access Path Optimization: Diversity Matters—0
Augmented Data Science: Towards Industrialization and Democratization of Data Science—0
Crowded trades, market clustering, and price instability—0
A blindspot of AI ethics: anti-fragility in statistical prediction—0
Denoising Attention for Query-aware User Modeling in Personalized Search—0
Augmented Message Passing Stein Variational Gradient Descent—0
Crowdsourcing Diverse Paraphrases for Training Task-oriented Bots—0
Confidence-Guided Semi-supervised Learning in Land Cover Classification—0
Crowdsourcing Lexical Diversity—0
Crowdsourcing Multiple Choice Science Questions—0
Crowdsourcing Parallel Corpus for English-Oromo Neural Machine Translation using Community Engagement Platform—0
Crowdsourcing Speech Data for Low-Resource Languages from Low-Income Workers—0
Crowdsourcing the Perception of Machine Teaching—0
Crowdsourcing with Fairness, Diversity and Budget Constraints—0
CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
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