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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 7426–7450 of 9051 papers

TitleStatusHype
Algorithmic Scenario Generation as Quality Diversity Optimization—0
Semantic Map Guided Synthesis of Wireless Capsule Endoscopy Images using Diffusion Models—0
Semantic Neighborhood Ordering in Multi-objective Genetic Programming based on Decomposition—0
Semantic-Rearrangement-Based Multi-Level Alignment for Domain Generalized Segmentation—0
Semantic Scene Completion with Multi-Feature Data Balancing Network—0
Semantics Disentangling for Text-to-Image Generation—0
Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders—0
Semantics in Multi-objective Genetic Programming—0
Vagueness in Predicates and Objects—0
Semantic uncertainty guides the extension of conventions to new referents—0
Semantic WordRank: Generating Finer Single-Document Summarizations—0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding—0
Algorithmic Hiring and Diversity: Reducing Human-Algorithm Similarity for Better Outcomes—0
Algorithmically probable mutations reproduce aspects of evolution such as convergence rate, genetic memory, and modularity—0
A Learning Scheme for Microgrid Islanding and Reconnection—0
Semi-Implicit Generative Model—0
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models—0
Validation Free and Replication Robust Volume-based Data Valuation—0
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition—0
Learning distinct features helps, provably—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
Semi-supervised FusedGAN for Conditional Image Generation—0
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion—0
A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization—0
A Large Language Model for Feasible and Diverse Population Synthesis—0
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