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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 4051–4075 of 9051 papers

TitleStatusHype
Large-scale Reinforcement Learning for Diffusion Models—0
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient—0
Quality-Diversity Algorithms Can Provably Be Helpful for Optimization—0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning—0
QoS-Aware 3D Coverage Deployment of UAVs for Internet of Vehicles in Intelligent Transportation—0
Hierarchical Federated Learning in Multi-hop Cluster-Based VANETsCode0
Divide and not forget: Ensemble of selectively trained experts in Continual LearningCode0
BPDO:Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection—0
Generalized Face Liveness Detection via De-fake Face Generator—0
SM^3: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects—0
Learning to detect cloud and snow in remote sensing images from noisy labels—0
Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentation—0
Evolutionary Multi-Objective Diversity Optimization—0
CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense ReasoningCode0
XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese—0
Microphone Conversion: Mitigating Device Variability in Sound Event ClassificationCode0
Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain—0
Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentationCode0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—0
R-BI: Regularized Batched Inputs enhance Incremental Decoding Framework for Low-Latency Simultaneous Speech Translation—0
MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems—0
Generating Diverse and High-Quality Texts by Minimum Bayes Risk DecodingCode0
Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks—0
Diversity-aware clustering: Computational Complexity and Approximation Algorithms—0
Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN—0
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