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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 3751–3775 of 9051 papers

TitleStatusHype
FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimalityCode0
GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image—0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning—0
Intelligent Reflecting Surfaces assisted Laser-based Optical Wireless Communication Networks—0
Diffusion Deepfake—0
Voice EHR: Introducing Multimodal Audio Data for Health—0
GI-Free Pilot-Aided Channel Estimation for Affine Frequency Division Multiplexing Systems—0
DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations—0
Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation—0
A Simple Yet Effective Approach for Diversified Session-Based RecommendationCode0
Rationale-based Opinion SummarizationCode0
FairRAG: Fair Human Generation via Fair Retrieval Augmentation—0
Advancing the Arabic WordNet: Elevating Content Quality—0
Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics—0
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control—0
GANTASTIC: GAN-based Transfer of Interpretable Directions for Disentangled Image Editing in Text-to-Image Diffusion Models—0
Echo-chambers and Idea Labs: Communication Styles on Twitter—0
Instruction-based Hypergraph Pretraining—0
Uncertainty-Aware Deep Video Compression with Ensembles—0
Towards Multimodal Video Paragraph Captioning Models Robust to Missing ModalityCode0
SteinGen: Generating Fidelitous and Diverse Graph SamplesCode0
PLOT-TAL -- Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization—0
Since the Scientific Literature Is Multilingual, Our Models Should Be Too—0
Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting—0
Language Plays a Pivotal Role in the Object-Attribute Compositional Generalization of CLIP—0
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