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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 6326–6350 of 9051 papers

TitleStatusHype
Ego-Exo: Transferring Visual Representations from Third-person to First-person VideosCode1
Fast ABC with joint generative modelling and subset simulation—0
First the worst: Finding better gender translations during beam searchCode0
Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks—0
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative LearningCode1
Toward Deconfounding the Influence of Entity Demographics for Question Answering Accuracy—0
Sentence-Permuted Paragraph GenerationCode1
Sparse Attention with Linear UnitsCode1
Aligning Latent and Image Spaces to Connect the UnconnectableCode1
SPARK: SPAcecraft Recognition leveraging Knowledge of Space Environment—0
Inference of cell dynamics on perturbation data using adjoint sensitivityCode0
Few-shot Image Generation via Cross-domain CorrespondenceCode1
Disentangled Motif-aware Graph Learning for Phrase Grounding—0
Level Generation for Angry Birds with Sequential VAE and Latent Variable EvolutionCode0
On the Linear Ordering Problem and the Rankability of Data—0
Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning—0
Towards Algorithmic Transparency: A Diversity Perspective—0
Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithms—0
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
Population genetics in microchannels—0
Co-optimising Robot Morphology and Controller in a Simulated Open-Ended EnvironmentCode0
Variational Transformer Networks for Layout Generation—0
Contrastive Syn-to-Real GeneralizationCode1
A hybrid ensemble method with negative correlation learning for regressionCode0
DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine ModelCode0
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