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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 3901–3925 of 9051 papers

TitleStatusHype
Phylogeny-informed fitness estimation—0
VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research—0
Dance Generation by Sound Symbolic Words—0
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization—0
Input-gradient space particle inference for neural network ensemblesCode0
Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures with Uncalibrated Stereo Data—0
Intermittent migration can induce pulses of speciation in a two-island system—0
Which Argumentative Aspects of Hate Speech in Social Media can be reliably identified?Code0
Generative Flow Network for Listwise RecommendationCode1
Learning on Bandwidth Constrained Multi-Source Data with MIMO-inspired DPP MAP Inference—0
Data Quality in Imitation Learning—0
GPT-FL: Generative Pre-trained Model-Assisted Federated LearningCode1
DOS: Diverse Outlier Sampling for Out-of-Distribution DetectionCode0
Knowledge of cultural moral norms in large language modelsCode0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
On Feature Diversity in Energy-based Models—0
KL-Divergence Guided Temperature SamplingCode0
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation—0
Some voices are too common: Building fair speech recognition systems using the Common Voice dataset—0
LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity OptimizationCode1
Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific DocumentsCode0
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior InferenceCode1
Conditioning Diffusion Models via Attributes and Semantic Masks for Face Generation—0
Survey of Trustworthy AI: A Meta Decision of AI—0
Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction—0
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