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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 8726–8750 of 9051 papers

TitleStatusHype
Addressing materials' microstructure diversity using transfer learning—0
Complementary Evidence Identification in Open-Domain Question Answering—0
Complementary Ensemble Learning—0
Complementary Boundary Generator with Scale-Invariant Relation Modeling for Temporal Action Localization: Submission to ActivityNet Challenge 2020—0
Doc2Token: Bridging Vocabulary Gap by Predicting Missing Tokens for E-commerce Search—0
Do City Borders Constrain Ethnic Diversity?—0
Document Image Binarization with Fully Convolutional Neural Networks—0
Do diverse and inclusive workplaces benefit investors? An Empirical Analysis on Europe and the United States—0
Does RLHF Scale? Exploring the Impacts From Data, Model, and Method—0
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch—0
Does the dataset meet your expectations? Explaining sample representation in image data—0
Graph based Entropy for Detecting Explanatory Signs of Changes in Market—0
VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder—0
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport—0
DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization—0
Dolphin: A Challenging and Diverse Benchmark for Arabic NLG—0
Domain-Adaptable Hybrid Generation of RDF Entity Descriptions—0
Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection—0
Domain Adaptation for Learning Generator from Paired Few-Shot Data—0
Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks—0
Domain Adaptation Gaze Estimation by Embedding with Prediction Consistency—0
Domain Adaptation through Synthesis for Unsupervised Person Re-identification—0
Domain adaption and physical constrains transfer learning for shale gas production—0
Domain Adaptive Attention Learning for Unsupervised Person Re-Identification—0
Domain-Aware Dynamic Networks—0
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