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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 8651–8700 of 9051 papers

TitleStatusHype
Diversity Induced Environment Design via Self-Play—0
Diversity-Induced Multi-View Subspace Clustering—0
Diversity in Evolutionary Dynamics—0
Diversity in Faces—0
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning—0
Diversity in immunogenomics: the value and the challenge—0
Diversity in Kemeny Rank Aggregation: A Parameterized Approach—0
Diversity in Machine Learning—0
Diversity in Object Proposals—0
Diversity in Spectral Learning for Natural Language Parsing—0
Diversity in the Music Listening Experience: Insights from Focus Group Interviews—0
Diversity in the radiation-induced transcriptomic temporal response of mouse brain tissue regions—0
Complex Network Construction for Interactive Image Segmentation using Particle Competition and Cooperation: A New Approach—0
Diversity is Strength: Mastering Football Full Game with Interactive Reinforcement Learning of Multiple AIs—0
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection—0
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors—0
Diversity Matters When Learning From Ensembles—0
Complexity of Symbolic Representation in Working Memory of Transformer Correlates with the Complexity of a Task—0
Diversity Measurement and Subset Selection for Instruction Tuning Datasets—0
Taming Latent Diffusion Model for Neural Radiance Field Inpainting—0
Taming Repetition in Dialogue Generation—0
A deep-learning-based approach for fast and robust steel surface defects classification—0
Diversity of Ensembles for Data Stream Classification—0
Diversity of growth rates maximizes phytoplankton productivity in an eddying ocean—0
Diversity of immune strategies explained by adaptation to pathogen statistics—0
Diversity of meso-scale architecture in human and non-human connectomes—0
Diversity of preferences can increase collective welfare in sequential exploration problems—0
Diversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate Frameworks—0
Diversity of Thought Improves Reasoning Abilities of LLMs—0
Diversity of Transformer Layers: One Aspect of Parameter Scaling Laws—0
Diversity of What? On the Different Conceptualizations of Diversity in Recommender Systems—0
A Deep Generative Model for Feasible and Diverse Population Synthesis—0
Diversity-Oriented Data Augmentation with Large Language Models—0
Diversity Over Quantity: A Lesson From Few Shot Relation Classification—0
TANGLED: Generating 3D Hair Strands from Images with Arbitrary Styles and Viewpoints—0
Diversity Preference-Aware Link Recommendation for Online Social Networks—0
Diversity Preferences, Affirmative Action and Choice Rules—0
Diversity-Preserving K-Armed Bandits, Revisited—0
Diversity Progress for Goal Selection in Discriminability-Motivated RL—0
Diversity-Promoting Bayesian Learning of Latent Variable Models—0
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation—0
Diversity-Promoting Ensemble for Medical Image Segmentation—0
A Decomposition-Based Many-Objective Evolutionary Algorithm with Local Iterative Update—0
Diversity-Promoting Human Motion Interpolation via Conditional Variational Auto-Encoder—0
Diversity Promoting Online Sampling for Streaming Video Summarization—0
Diversity regularization in deep ensembles—0
Diversity Regularized Adversarial Learning—0
Diversity Regularized Interests Modeling for Recommender Systems—0
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification—0
Diversity-Rewarded CFG Distillation—0
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