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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 4901–4925 of 9051 papers

TitleStatusHype
Exploiting Feature Diversity for Make-up Temporal Video Grounding—0
Diverse Generative Perturbations on Attention Space for Transferable Adversarial AttacksCode1
Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery—0
Reducing Exploitability with Population Based TrainingCode0
Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning—0
Adaptive Learning Rates for Faster Stochastic Gradient Methods—0
r/K selection of GC content in prokaryotes—0
Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation—0
Deep Billboards towards Lossless Real2Sim in Virtual Reality—0
A Map of Diverse Synthetic Stable Roommates InstancesCode1
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation—0
Preserving Fine-Grain Feature Information in Classification via Entropic RegularizationCode0
DeepGen: Diverse Search Ad Generation and Real-Time Customization—0
Contrastive Positive Mining for Unsupervised 3D Action Representation Learning—0
Mathematical Modeling Analysis and Optimization of Fungal Diversity Growth—0
Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding—0
Evolutionary bagging for ensemble learningCode0
Rethinking the Evaluation of Unbiased Scene Graph Generation—0
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization—0
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence—0
COMET: Coverage-guided Model Generation For Deep Learning Library TestingCode0
Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship—0
Towards Psychologically-Grounded Dynamic Preference Models—0
A Deep Generative Model for Feasible and Diverse Population Synthesis—0
Safe Perception -- A Hierarchical Monitor Approach—0
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