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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 6176–6200 of 9051 papers

TitleStatusHype
Cash versus Kind: Benchmarking a Child Nutrition Program against Unconditional Cash Transfers in Rwanda—0
Discovering Diverse Nearly Optimal Policies with Successor Features—0
Decoupling Shape and Density for Liver Lesion Synthesis Using Conditional Generative Adversarial Networks—0
Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task—0
Data Fusion for Deep Learning on Transport Mode Detection: A Case StudyCode0
Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training—0
Representation Learning Beyond Linear Prediction Functions—0
Pixel super-resolved lensless on-chip sensor with scattering multiplexing—0
SNIPS: Solving Noisy Inverse Problems StochasticallyCode1
A Novel Automatic Modulation Classification Scheme Based on Multi-Scale Networks—0
Towards Diverse Paragraph Captioning for Untrimmed VideosCode1
Diversifying Dialog Generation via Adaptive Label SmoothingCode1
Transforming the Latent Space of StyleGAN for Real Face EditingCode1
Greedy Bayesian Posterior Approximation with Deep EnsemblesCode1
A Simulation-Optimization Technique for Service Level Analysis in Conjunction with Reorder Point Estimation and Lead-Time consideration: A Case Study in Sea Port—0
About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data AnnotationsCode0
THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses—0
ResT: An Efficient Transformer for Visual RecognitionCode1
Early Exiting with Ensemble Internal Classifiers—0
The Herbarium 2021 Half-Earth Challenge DatasetCode1
IRS-Assisted Massive MIMO-NOMA Networks with Polarization Diversity—0
How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation?—0
Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption ModelsCode1
Deconditional Downscaling with Gaussian ProcessesCode0
ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learningCode1
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