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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 5226–5250 of 9051 papers

TitleStatusHype
Local adaptation, phenotypic plasticity, and species coexistence—0
Mind the gap: how multiracial individuals get left behind when we talk about race, ethnicity, and ancestry in genomic research—0
Where in the World is this Image? Transformer-based Geo-localization in the WildCode1
User-controllable Recommendation Against Filter BubblesCode1
RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive LearningCode1
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning ModelsCode0
On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference—0
CapOnImage: Context-driven Dense-Captioning on Image—0
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning—0
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response GenerationCode1
Probing Simile Knowledge from Pre-trained Language ModelsCode0
Towards assessing agricultural land suitability with causal machine learning—0
Bias-Variance Decompositions for Margin Losses—0
Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition—0
EmpHi: Generating Empathetic Responses with Human-like IntentsCode1
A Robust Contrastive Alignment Method For Multi-Domain Text Classification—0
Efficient Neural Neighborhood Search for Pickup and Delivery ProblemsCode1
Loss-based Sequential Learning for Active Domain Adaptation—0
Determinantal Point Process Likelihoods for Sequential RecommendationCode1
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text ClassificationCode1
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of ExplanationsCode1
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling ProblemCode1
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles—0
Selective clustering ensemble based on kappa and F-score—0
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation—0
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