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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 5001–5025 of 9051 papers

TitleStatusHype
What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations—0
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection—0
Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method—0
Bayesian support for Evolution: detecting phylogenetic signal in a subset of the primate family—0
Learned Region Sparsity and Diversity Also Predicts Visual Attention—0
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations—0
Learning 3D Semantic Segmentation with only 2D Image Supervision—0
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables—0
Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration—0
Action Understanding with Multiple Classes of Actors—0
Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation—0
Learning an evolved mixture model for task-free continual learning—0
Learning a Non-Redundant Collection of Classifiers—0
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo—0
Learning-Based Biharmonic Augmentation for Point Cloud Classification—0
Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness—0
Learning Camera Movement Control from Real-World Drone Videos—0
Learning Canonical Transformations—0
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification—0
Learning Collective Action under Risk Diversity—0
Learning Compact Reward for Image Captioning—0
Learning Continually by Spectral Regularization—0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Learning Debiased and Disentangled Representations for Semantic Segmentation—0
Learning Deep Features for Scene Recognition using Places Database—0
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