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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 7601–7625 of 9051 papers

TitleStatusHype
Parallel Exploration via Negatively Correlated Search—0
CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup SegmentationCode0
Optimal Transport Based Generative Autoencoders—0
Co-existence of Micro, Pico and Atto Cells in Optical Wireless Communication—0
Optical Wireless cabin communication system—0
Infrared uplink design for visible light communication (VLC) systems with beam steering—0
Transmitter Diversity with Beam Steering—0
Target-Oriented Deformation of Visual-Semantic Embedding SpaceCode1
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentCode1
DISCERN: Diversity-based Selection of Centroids for k-Estimation and Rapid Non-stochastic ClusteringCode0
Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method—0
A simple and effective hybrid genetic search for the job sequencing and tool switching problemCode0
A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddingsCode0
Adversarial Training: embedding adversarial perturbations into the parameter space of a neural network to build a robust system—0
Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity—0
Self-Paced Multi-Label Learning with DiversityCode0
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translation—0
Kidney Recognition in CT Using YOLOv3—0
Face Manifold: Manifold Learning for Synthetic Face GenerationCode0
On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization—0
Expanding functional protein sequence space using generative adversarial networksCode0
Identity From Here, Pose From There: Self-Supervised Disentanglement and Generation of Objects Using Unlabeled Videos—0
Learning a Mixture of Granularity-Specific Experts for Fine-Grained CategorizationCode0
Gaussian Affinity for Max-Margin Class Imbalanced Learning—0
Fast and Practical Neural Architecture SearchCode0
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