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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 6676–6700 of 9051 papers

TitleStatusHype
SentiRec: Sentiment Diversity-aware Neural News Recommendation—0
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs—0
HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion—0
Tregs self-organize into a "computing ecosystem" and implement a sophisticated optimization algorithm for mediating immune responseCode0
Towards a Universal Features Set for IoT Botnet Attacks Detection—0
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition—0
TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language GenerationCode1
Diversity-Guided Multi-Objective Bayesian Optimization With Batch EvaluationsCode1
Probabilistic Time Series Forecasting with Shape and Temporal DiversityCode1
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient SpaceCode1
Boom-bust population dynamics can increase diversity in evolving competitive communities—0
S2FGAN: Semantically Aware Interactive Sketch-to-Face TranslationCode1
Offline Reinforcement Learning Hands-On—0
Is Support Set Diversity Necessary for Meta-Learning?—0
TaylorGAN: Neighbor-Augmented Policy Update for Sample-Efficient Natural Language GenerationCode1
Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identificationCode1
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient—0
Decoding and Diversity in Machine Translation—0
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
Predictive PER: Balancing Priority and Diversity towards Stable Deep Reinforcement Learning—0
Rethinking conditional GAN training: An approach using geometrically structured latent manifoldsCode1
How to Train Neural Networks for Flare RemovalCode0
CircleGAN: Generative Adversarial Learning across Spherical CirclesCode0
Enhanced 3DMM Attribute Control via Synthetic Dataset Creation Pipeline—0
Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample—0
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