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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 7051–7100 of 9051 papers

TitleStatusHype
Regularizing Dialogue Generation by Imitating Implicit Scenarios—0
Deep Generative Modelling of Human Reach-and-Place Action—0
Diversity-Preserving K-Armed Bandits, Revisited—0
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets—0
MGD-GAN: Text-to-Pedestrian generation through Multi-Grained Discrimination—0
Online Knowledge Distillation via Multi-branch Diversity Enhancement—0
Deep Incomplete Multi-View Multiple Clusterings—0
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models—0
A Niching Indicator-Based Multi-modal Many-objective Optimizer—0
A new dataset of dog breed images and a benchmark for fine-grained classification—0
Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection—0
Plan-CVAE: A Planning-based Conditional Variational Autoencoder for Story Generation—0
ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts—0
Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Multiple Word Embeddings for Increased Diversity of RepresentationCode0
A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms—0
CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos—0
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks—0
Discriminative Representation Loss (DRL): A More Efficient Approach than Gradient Re-Projection in Continual Learning—0
Cuid: A new study of perceived image quality and its subjective assessment—0
Data Instance Prior for Transfer Learning in GANs—0
Simultaneous Relevance and Diversity: A New Recommendation Inference Approach—0
Controllable Text Generation with Focused Variation—0
Towards General Purpose Geometry-Preserving Single-View Depth Estimation—0
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue—0
Cosine Similarity of Multimodal Content Vectors for TV Programmes—0
Behavioral Repertoires for Soft Tensegrity Robots—0
Curriculum Learning with Diversity for Supervised Computer Vision Tasks—0
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial RobustnessCode0
div2vec: Diversity-Emphasized Node Embedding—0
Target Conditioning for One-to-Many Generation—0
Modeling the Evolution of Networks as Shrinking Structural Diversity—0
F^2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax—0
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference—0
Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction—0
Label-Based Diversity Measure Among Hidden Units of Deep Neural Networks: A Regularization Method—0
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement—0
Multi-source Data Mining for e-Learning—0
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent LossCode0
Unsupervised Summarization by Jointly Extracting Sentences and Keywords—0
Using Known Words to Learn More Words: A Distributional Analysis of Child Vocabulary Development—0
Understanding Deformable Alignment in Video Super-Resolution—0
A Systematic Characterization of Sampling Algorithms for Open-ended Language GenerationCode0
Improving Inversion and Generation Diversity in StyleGAN using a Gaussianized Latent Space—0
Robust Deep Learning Ensemble against Deception—0
Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach—0
Fair and accurate age prediction using distribution aware data curation and augmentationCode0
Diversified Mutual Learning for Deep Metric Learning—0
Black Box to White Box: Discover Model Characteristics Based on Strategic Probing—0
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