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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 4976–5000 of 9051 papers

TitleStatusHype
Efficient Quality-Diversity Optimization through Diverse Quality SpeciesCode0
Towards Large-Scale Simulations of Open-Ended Evolution in Continuous Cellular Automata—0
Wild Face Anti-Spoofing Challenge 2023: Benchmark and ResultsCode0
Mutation enhances cooperation in direct reciprocity—0
LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversity—0
Learning to exploit z-Spatial Diversity for Coherent Nonlinear Optical Fiber Communication—0
Continual Semantic Segmentation with Automatic Memory Sample Selection—0
Collaborative Machine Learning Model Building with Families Using Co-ML—0
Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection—0
Who are the gatekeepers of economics? Geographic diversity, gender composition, and interlocking editorship of journal boards—0
AGAD: Adversarial Generative Anomaly Detection—0
RD-DPP: Rate-Distortion Theory Meets Determinantal Point Process to Diversify Learning Data Samples—0
Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning—0
GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation—0
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack—0
Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems—0
On the Suitability of Representations for Quality Diversity Optimization of Shapes—0
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains—0
Pragmatically Appropriate Diversity for Dialogue Evaluation—0
DITTO-NeRF: Diffusion-based Iterative Text To Omni-directional 3D Model—0
Source-free Domain Adaptation Requires Penalized Diversity—0
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields—0
Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation—0
Revolutionizing Single Cell Analysis: The Power of Large Language Models for Cell Type Annotation—0
Controllable Exploration of a Design Space via Interactive Quality Diversity—0
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