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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 6476–6500 of 9051 papers

TitleStatusHype
Learning Diverse Skills for Local Navigation under Multi-constraint Optimality—0
Learning Efficient Image Representation for Person Re-Identification—0
Learning Efficient Representations for Enhanced Object Detection on Large-scene SAR Images—0
Learning efficient structured dictionary for image classification—0
Learning Enriched Illuminants for Cross and Single Sensor Color Constancy—0
Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation—0
Learning from diversity: jati fractionalization, social expectations and improved sanitation practices in India—0
Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classification—0
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision—0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense Inference—0
Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks—0
Learning from Neighbors about a Changing State—0
Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training—0
Learning General World Models in a Handful of Reward-Free Deployments—0
Learning High-Resolution Domain-Specific Representations with a GAN Generator—0
Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation—0
Learning immune receptor representations with protein language models—0
Learning Compact and Robust Representations for Anomaly Detection—0
Learning in Sparse Rewards settings through Quality-Diversity algorithms—0
Learning Invariant Representation of Tasks for Robust Surgical State Estimation—0
Learning k-Determinantal Point Processes for Personalized Ranking—0
Learning Latent Space Models with Angular Constraints—0
Learning Mixtures of Submodular Functions for Image Collection Summarization—0
Learning Model-Blind Temporal Denoisers without Ground Truths—0
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