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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 7301–7325 of 9051 papers

TitleStatusHype
Managing Diversity in Airbnb Search—0
Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation—0
A Swiss German Dictionary: Variation in Speech and Writing—0
Stochastic single-particle based simulations of cellular signaling embedded into computational models of cellular morphology—0
Streaming Networks: Increase Noise Robustness and Filter Diversity via Hard-wired and Input-induced Sparsity—0
Learning Memory-guided Normality for Anomaly DetectionCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
Re-purposing Heterogeneous Generative Ensembles with Evolutionary ComputationCode0
Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests—0
Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods—0
Gradient-based Data Augmentation for Semi-Supervised Learning—0
Variational Transformers for Diverse Response GenerationCode1
Sorting Big Data by Revealed Preference with Application to College Ranking—0
Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient SituationsCode1
Lightweight Photometric Stereo for Facial Details RecoveryCode1
FFR V1.0: Fon-French Neural Machine TranslationCode1
Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning modelsCode1
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression—0
Ecological communities from random generalised Lotka-Volterra dynamics with non-linear feedback—0
Auto-Ensemble: An Adaptive Learning Rate Scheduling based Deep Learning Model Ensembling—0
Two-stage Discriminative Re-ranking for Large-scale Landmark RetrievalCode1
Learning Compact Reward for Image Captioning—0
Palm-GAN: Generating Realistic Palmprint Images Using Total-Variation Regularized GAN—0
Utilizing Differential Evolution into optimizing targeted cancer treatments—0
BoostTree and BoostForest for Ensemble LearningCode1
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