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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 7601–7625 of 9051 papers

TitleStatusHype
Social Diversity Reduces the Complexity and Cost of Fostering Fairness—0
Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future—0
Social Media, Topic Modeling and Sentiment Analysis in Municipal Decision Support—0
Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast—0
Social Style Characterization from Egocentric Photo-streams—0
Socio-Culturally Aware Evaluation Framework for LLM-Based Content Moderation—0
A Global Nucleic Acid Observatory for Biodefense and Planetary Health—0
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing—0
3D Shape Generation: A Survey—0
vec2text with Round-Trip Translations—0
Softlog-Softmax Layers and Divergences Contribute to a Computationally Dependable Ensemble Learning—0
Aggregation of Classifiers: A Justifiable Information Granularity Approach—0
Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration—0
SoK: Data Reconstruction Attacks Against Machine Learning Models: Definition, Metrics, and Benchmark—0
Aggregating Dependent Gaussian Experts in Local Approximation—0
Solution for Emotion Prediction Competition of Workshop on Emotionally and Culturally Intelligent AI—0
Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model—0
Vector Autoregressive Evolution for Dynamic Multi-Objective Optimisation—0
Something for (almost) nothing: Improving deep ensemble calibration using unlabeled data—0
Vector Quantized-Elites: Unsupervised and Problem-Agnostic Quality-Diversity Optimization—0
Some voices are too common: Building fair speech recognition systems using the Common Voice dataset—0
SO-NeRF: Active View Planning for NeRF using Surrogate Objectives—0
Vector-Quantized Prompt Learning for Paraphrase Generation—0
Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants—0
Sorting Big Data by Revealed Preference with Application to College Ranking—0
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