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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 5001–5050 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
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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