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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 2001–2025 of 9051 papers

TitleStatusHype
ArchCAD-400K: An Open Large-Scale Architectural CAD Dataset and New Baseline for Panoptic Symbol Spotting—0
Multi-Objective Quality-Diversity in Unstructured and Unbounded SpacesCode0
Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback—0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Multiple Embeddings for Quantum Machine Learning—0
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI RecommendationCode0
Barrier-Free Microhabitats: Self-Organized Seclusion in Microbial CommunitiesCode0
Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic SystemsCode0
Dynamics of Algorithmic Content Amplification on TikTok—0
MLLM-Selector: Necessity and Diversity-driven High-Value Data Selection for Enhanced Visual Instruction Tuning—0
The economics of global personality diversity—0
Exploring Textual Semantics Diversity for Image Transmission in Semantic Communication Systems using Visual Language Model—0
Recover from Horcrux: A Spectrogram Augmentation Method for Cardiac Feature Monitoring from Radar Signal Components—0
Writing as a testbed for open ended agents—0
Beyond Relevance: An Adaptive Exploration-Based Framework for Personalized Recommendations—0
VisualQuest: A Diverse Image Dataset for Evaluating Visual Recognition in LLMs—0
Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across LanguagesCode0
Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges—0
Zero-Shot Human-Object Interaction Synthesis with Multimodal Priors—0
Information-Seeking Decision Strategies Mitigate Risk in Dynamic, Uncertain EnvironmentsCode0
Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation—0
Evolutionary Policy Optimization—0
Generative Dataset Distillation using Min-Max Diffusion Model—0
Enhancing Symbolic Regression with Quality-Diversity and Physics-Inspired ConstraintsCode0
Tuning-Free Amodal Segmentation via the Occlusion-Free Bias of Inpainting Models—0
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