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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 7701–7750 of 9051 papers

TitleStatusHype
Deep Hierarchical-Hyperspherical Learning (DH^2L)—0
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
Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis—0
Particle Smoothing Variational ObjectivesCode0
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder—0
DAOC: Stable Clustering of Large NetworksCode0
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
A*3D Dataset: Towards Autonomous Driving in Challenging EnvironmentsCode0
BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation—0
Graph-guided Architecture Search for Real-time Semantic SegmentationCode0
Beyond BLEU: Training Neural Machine Translation with Semantic SimilarityCode0
Augmented Data Science: Towards Industrialization and Democratization of Data Science—0
Constrained Pseudo-market Equilibrium—0
AFP-CKSAAP: Prediction of Antifreeze Proteins Using Composition of k-Spaced Amino Acid Pairs with Deep Neural Network—0
Eligibility traces provide a data-inspired alternative to backpropagation through time—0
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators—0
Data-Driven Discovery of Functional Cell Types that Improve Models of Neural Activity—0
The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale—0
Automatic difficulty management and testing in games using a framework based on behavior trees and genetic algorithmsCode0
Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation—0
An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue—0
A Corpus-free State2Seq User Simulator for Task-oriented DialogueCode0
Sampling Strategies for GAN Synthetic Data—0
Unsupervised Paraphrasing by Simulated Annealing—0
Learning to Sample: an Active Learning Framework—0
On the clustering of correlated random variables—0
An Algorithm for Multi-Attribute Diverse Matching—0
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks—0
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion AnalysisCode0
The Benefits of Diversity: Permutation Recovery in Unlabeled Sensing from Multiple Measurement Vectors—0
The Diversity-Innovation Paradox in ScienceCode0
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation SystemsCode0
Mixture Content Selection for Diverse Sequence GenerationCode0
Adversarial Bootstrapping for Dialogue Model Training—0
Cross-Cutting Political Awareness through Diverse News Recommendations—0
A Tool for Super-Resolving Multimodal Clinical MRICode0
Relationship-Aware Spatial Perception Fusion for Realistic Scene Layout Generation—0
Investigating the Relationship between Multi-Party Linguistic Entrainment, Team Characteristics, and the Perception of Team Social Outcomes—0
Twitter Bot Detection using Diversity Measures—0
Dialect-Specific Models for Automatic Speech Recognition of African American Vernacular English—0
Unsupervised Data Augmentation for Less-Resourced Languages with no Standardized Spelling—0
Heterogeneous Collaborative Filtering—0
Energy Clustering for Unsupervised Person Re-identification—0
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization—0
(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple PersonasCode0
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