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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 7851–7875 of 9051 papers

TitleStatusHype
Visualizing Temporal Topic Embeddings with a Compass—0
Cross-Layer Discrete Concept Discovery for Interpreting Language Models—0
Cross-Layer Strategic Ensemble Defense Against Adversarial Examples—0
Cross-Lingual Transfer of Cultural Knowledge: An Asymmetric Phenomenon—0
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control—0
Cross-modal Face- and Voice-style Transfer—0
Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation—0
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals—0
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech—0
Counting and optimising maximum phylogenetic diversity sets—0
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks—0
Crowd Access Path Optimization: Diversity Matters—0
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks—0
Crowded trades, market clustering, and price instability—0
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization—0
CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos—0
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets—0
Crowdsourcing Diverse Paraphrases for Training Task-oriented Bots—0
Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments—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
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