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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 4176–4200 of 9051 papers

TitleStatusHype
Controllable Exploration of a Design Space via Interactive Quality Diversity—0
SLPerf: a Unified Framework for Benchmarking Split LearningCode1
PODIA-3D: Domain Adaptation of 3D Generative Model Across Large Domain Gap Using Pose-Preserved Text-to-Image Diffusion—0
Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study—0
A Guide for Practical Use of ADMG Causal Data AugmentationCode0
Ensemble prosody prediction for expressive speech synthesis—0
MetaHead: An Engine to Create Realistic Digital Head—0
DivClust: Controlling Diversity in Deep ClusteringCode1
ReMoDiffuse: Retrieval-Augmented Motion Diffusion ModelCode2
The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web ArchivesCode1
MMT: A Multilingual and Multi-Topic Indian Social Media Dataset—0
DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking TasksCode1
Progressive Random Convolutions for Single Domain Generalization—0
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?—0
The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR—0
FONT: Flow-guided One-shot Talking Head Generation with Natural Head Motions—0
Utilizing Reinforcement Learning for de novo Drug DesignCode0
Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical StudyCode0
All You Need Is Sex for Diversity—0
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions—0
Asymmetric Image Retrieval with Cross Model Compatible Ensembles—0
Online Camera-to-ground Calibration for Autonomous Driving—0
SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and ModelingCode2
KD-DLGAN: Data Limited Image Generation via Knowledge Distillation—0
A View From Somewhere: Human-Centric Face RepresentationsCode1
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