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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 7351–7375 of 9051 papers

TitleStatusHype
MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space—0
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation—0
Customized Video QoE Estimation with Algorithm-Agnostic Transfer Learning—0
A Multi-criteria Approach for Fast and Outlier-aware Representative Selection from Manifolds—0
A Simulation Study of Bandit Algorithms to Address External Validity of Software Fault Prediction—0
RNE: A Scalable Network Embedding for Billion-scale Recommendation—0
Rainy screens: Collecting rainy datasets, indoors—0
Diversity inducing Information Bottleneck in Model EnsemblesCode0
Dispersal-induced instability in complex ecosystems—0
Discovering Representations for Black-box OptimizationCode0
Quality Diversity for Multi-task OptimizationCode1
An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue GenerationCode1
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world—0
ProGen: Language Modeling for Protein GenerationCode1
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity SamplingCode0
Learning the Designer's Preferences to Drive Evolution—0
Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature DimensionsCode0
On the Role of Conceptualization in Commonsense Knowledge Graph ConstructionCode1
Diverse and Admissible Trajectory Forecasting through Multimodal Context UnderstandingCode1
Hierarchical Modes Exploring in Generative Adversarial Networks—0
Stochastic Linear Contextual Bandits with Diverse Contexts—0
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
QED: using Quality-Environment-Diversity to evolve resilient robot swarmsCode0
Rethinking Parameter Counting in Deep Models: Effective Dimensionality RevisitedCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
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