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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 3626–3650 of 9051 papers

TitleStatusHype
Feature diversity in self-supervised learning—0
Feature Diversity Learning with Sample Dropout for Unsupervised Domain Adaptive Person Re-identification—0
Feature Extraction via Recurrent Random Deep Ensembles and its Application in Gruop-level Happiness Estimation—0
Feature Fusion Detector for Semantic Cognition of Remote Sensing—0
Exploring the Feature Space of TSP Instances Using Quality Diversity—0
Conceptual Mapping of Controversies—0
Feature Selection Based on Term Frequency and T-Test for Text Categorization—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training—0
Feature Statistics Guided Efficient Filter Pruning—0
Feature Weighted Non-negative Matrix Factorization—0
FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment—0
FedAvgen: Metadata for Model Aggregation In Communication Systems—0
Conceptual Content in Deep Convolutional Neural Networks: An analysis into multi-faceted properties of neurons—0
Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task—0
FedDAR: Federated Domain-Aware Representation Learning—0
FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning—0
Conceptual capacity and effective complexity of neural networks—0
Federated Data Model—0
Federated Learning Empowered by Generative Content—0
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
A high fidelity synthetic face framework for computer vision—0
Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence—0
Federated Learning for Personalized Humor Recognition—0
Exploring the Design Space of Diffusion Bridge Models via Stochasticity Control—0
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