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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 7651–7675 of 9051 papers

TitleStatusHype
Speech Recognition with Augmented Synthesized Speech—0
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical SpaceCode0
In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point ProcessesCode0
Generating Geological Facies Models with Fidelity to Diversity and Statistics of Training Images using Improved Generative Adversarial Networks—0
Shadow Transfer: Single Image Relighting For Urban Road Scenes—0
Variational Conditional GAN for Fine-grained Controllable Image Generation—0
Particle Smoothing Variational ObjectivesCode0
Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis—0
DAOC: Stable Clustering of Large NetworksCode0
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder—0
Self-Training for End-to-End Speech Recognition—0
Fitts' Law for speed-accuracy trade-off describes a diversity-enabled sweet spot in sensorimotor control—0
Diversified Arbitrary Style Transfer via Deep Feature PerturbationCode0
BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation—0
A*3D Dataset: Towards Autonomous Driving in Challenging EnvironmentsCode0
Stacking Models for Nearly Optimal Link Prediction in Complex NetworksCode1
Graph-guided Architecture Search for Real-time Semantic SegmentationCode0
Beyond BLEU: Training Neural Machine Translation with Semantic SimilarityCode0
Constrained Pseudo-market Equilibrium—0
Augmented Data Science: Towards Industrialization and Democratization of Data Science—0
Eligibility traces provide a data-inspired alternative to backpropagation through time—0
Data-Driven Discovery of Functional Cell Types that Improve Models of Neural Activity—0
AFP-CKSAAP: Prediction of Antifreeze Proteins Using Composition of k-Spaced Amino Acid Pairs with Deep Neural Network—0
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators—0
Sampling Strategies for GAN Synthetic Data—0
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