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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 3951–3975 of 9051 papers

TitleStatusHype
Experimental Validation of Coherent Joint Transmission in a Distributed-MIMO System with Analog Fronthaul for 6G—0
Experimental Study on the Effect of Synchronization Accuracy for Near-Field RF Wireless Power Transfer in Multi-Antenna Systems—0
Generating Diverse and Competitive Play-Styles for Strategy Games—0
Generating Diverse and Descriptive Image Captions Using Visual Paraphrases—0
Generating Diverse and High-Quality Abstractive Summaries with Variational Transformers—0
Experimental Study of RCS Diversity with Novel No-divergent OAM Beams—0
Experimental Results of a 3D Millimeter-Wave Compressive-Reflector-Antenna Imaging System—0
Generating Diverse Challenging Terrains for Legged Robots Using Quality-Diversity Algorithm—0
Compose Like Humans: Jointly Improving the Coherence and Novelty for Modern Chinese Poetry Generation—0
Expedition: A Time-Aware Exploratory Search System Designed for Scholars—0
Generating Diverse Hypotheses for Inductive Reasoning—0
Generating Diverse Indoor Furniture Arrangements—0
Composable Core-sets for Diversity Approximation on Multi-Dataset Streams—0
Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning—0
Generating Diverse Realistic Laughter for Interactive Art—0
Generating Diverse Story Continuations with Controllable Semantics—0
Expecting the Unexpected: Developing Autonomous-System Design Principles for Reacting to Unpredicted Events and Conditions—0
Generating Diverse Translation by Manipulating Multi-Head Attention—0
Expectation-Maximization for Learning Determinantal Point Processes—0
Generating Diverse Translations with Sentence Codes—0
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm—0
Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection—0
Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection—0
Generating Geological Facies Models with Fidelity to Diversity and Statistics of Training Images using Improved Generative Adversarial Networks—0
A Search for Improved Performance in Regular Expressions—0
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