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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 4251–4275 of 9051 papers

TitleStatusHype
A Generalized Space-Frequency Index Modulation Scheme for Downlink MIMO Transmissions with Improved Diversity—0
MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models—0
EpiCoder: Encompassing Diversity and Complexity in Code Generation—0
COLLAGE: Collaborative Human-Agent Interaction Generation using Hierarchical Latent Diffusion and Language Models—0
A review of TinyML—0
ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts—0
Entropy Rectifying Guidance for Diffusion and Flow Models—0
Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation—0
Entropy of Overcomplete Kernel Dictionaries—0
Collaborative Perception Datasets in Autonomous Driving: A Survey—0
A Review of the Marathi Natural Language Processing—0
Entropy, Ergodicity and Stem Cell Multipotency—0
Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem—0
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems—0
Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint—0
Entropy and type-token ratio in gigaword corpora—0
Collaborative Machine Learning Model Building with Families Using Co-ML—0
Entropy and Energy Detection-based Spectrum Sensing over F Composite Fading Channels—0
Entropy and Diversity: The Axiomatic Approach—0
Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems—0
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity—0
Entity Identifier: A Natural Text Parsing-based Framework For Entity Relation Extraction—0
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants—0
A review of approaches to modeling applied vehicle routing problems—0
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition—0
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