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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 3601–3625 of 9051 papers

TitleStatusHype
Exploring the Potentials and Challenges of Using Large Language Models for the Analysis of Transcriptional Regulation of Long Non-coding RNAs—0
CONDEN-FI: Consistency and Diversity Learning-based Multi-View Unsupervised Feature and In-stance Co-Selection—0
Faster and Smarter AutoAugment: Augmentation Policy Search Based on Dynamic Data-Clustering—0
Ask to Understand: Question Generation for Multi-hop Question Answering—0
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity—0
Fast inverse lithography based on a model-driven block stacking convolutional neural network—0
Fast Re-Optimization via Structural Diversity—0
A Holistic Evaluation of Piano Sound Quality—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Fatal errors and misuse of mathematics in the Hong-Page Theorem and Landemore's epistemic argument—0
Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features—0
Fault Detection in Mobile Networks Using Diffusion Models—0
FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition—0
FCBoost-Net: A Generative Network for Synthesizing Multiple Collocated Outfits via Fashion Compatibility Boosting—0
Exploring the Law of Numbers: Evidence from China's Real Estate—0
Exploring the influence of fine-tuning data on wav2vec 2.0 model for blind speech quality prediction—0
Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models—0
Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations—0
Exploring the Feature Space of TSP Instances Using Quality Diversity—0
Conceptual Mapping of Controversies—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation—0
Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training—0
Feature-based Evolutionary Diversity Optimization of Discriminating Instances for Chance-constrained Optimization Problems—0
Conceptual Content in Deep Convolutional Neural Networks: An analysis into multi-faceted properties of neurons—0
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