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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 4276–4300 of 9051 papers

TitleStatusHype
Emergent competition shapes the ecological properties of multi-trophic ecosystems—0
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer LearningCode0
Streaming Active Learning with Deep Neural NetworksCode2
SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic DataCode1
Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization—0
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Data Augmentation for Generating Synthetic Electrogastrogram Time SeriesCode0
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing SymmetriesCode0
Effective Visualization and Analysis of Recommender Systems—0
pyribs: A Bare-Bones Python Library for Quality Diversity Optimization—0
Neural Video Compression with Diverse ContextsCode1
Toward Robust Uncertainty Estimation with Random Activation FunctionsCode0
DREAM: Efficient Dataset Distillation by Representative MatchingCode1
Can We Use Diffusion Probabilistic Models for 3D Motion Prediction?—0
Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image AnalysisCode1
DEff-GAN: Diverse Attribute Transfer for Few-Shot Image SynthesisCode0
Finding Support Examples for In-Context Learning—0
Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets—0
Cross-modal Face- and Voice-style Transfer—0
Diversity matters: Robustness of bias measurements in WikidataCode0
Tailoring Language Generation Models under Total Variation DistanceCode1
Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric MaterialsCode0
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition—0
Speech Corpora Divergence Based Unsupervised Data Selection for ASR—0
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