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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 5026–5050 of 9051 papers

TitleStatusHype
Learning Determinantal Point Processes by Corrective Negative Sampling—0
Towards Exploratory Quality Diversity Landscape Analysis—0
Learning Disentangled Representations for Image Translation—0
Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients—0
Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method—0
What Matters in Learning from Large-Scale Datasets for Robot Manipulation—0
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning—0
Learning Diverse Generations using Determinantal Point Processes—0
Towards Federated Learning on Time-Evolving Heterogeneous Data—0
Learning Diverse Policies in MOBA Games via Macro-Goals—0
Learning Diverse Policies with Soft Self-Generated Guidance—0
Learning diverse rankings with multi-armed bandits—0
Learning Diverse Representations for Fast Adaptation to Distribution Shift—0
BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation—0
Learning Diverse Skills for Local Navigation under Multi-constraint Optimality—0
Towards Foundation Models for Critical Care Time Series—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
Towards GAN Benchmarks Which Require Generalization—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
Towards General Purpose Geometry-Preserving Single-View Depth Estimation—0
Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classification—0
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