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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 6801–6825 of 9051 papers

TitleStatusHype
Extracting Signals of Higgs Boson From Background Noise Using Deep Neural Networks—0
Input-Aware Dynamic Backdoor AttackCode1
Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes—0
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding—0
Deep Submodular Networks for Extractive Data Summarization—0
Generating Diverse Translation from Model Distribution with Dropout—0
Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation—0
Hyperparameter Auto-tuning in Self-Supervised Robotic LearningCode0
Performance Analysis and Optimization of RIS-Assisted Networks in Nakagami-m Environment—0
Ensembles of Spiking Neural Networks—0
Non-saturating GAN training as divergence minimization—0
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards—0
Diverse Keyphrase Generation with Neural Unlikelihood TrainingCode0
Probabilistic Time Series Forecasting with Structured Shape and Temporal DiversityCode1
Deep Ensembles for Low-Data Transfer Learning—0
Single-Antenna-Based GPS Antijamming Method Exploiting Polarization Diversity—0
Random Network Distillation as a Diversity Metric for Both Image and Text Generation—0
Deep Delay Loop Reservoir Computing for Specific Emitter Identification—0
ATF: Towards Robust Face Alignment via Leveraging Similarity and Diversity across Different DatasetsCode1
Channel Type Recognition in Wireless Communications: A Deep Learning Approach—0
Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 NetworkCode1
A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph—0
Complexity-based speciation and genotype representation for neuroevolution—0
Characterizing Policy Divergence for Personalized Meta-Reinforcement Learning—0
Token-level Adaptive Training for Neural Machine TranslationCode1
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