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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 5326–5350 of 9051 papers

TitleStatusHype
Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data—0
SNR-Based Features and Diverse Training Data for Robust DNN-Based Speech Enhancement—0
SOA_NLP@LT-EDI-ACL2022: An Ensemble Model for Hope Speech Detection from YouTube Comments—0
SoccerSynth-Detection: A Synthetic Dataset for Soccer Player Detection—0
Social and Semantic Diversity: Socio-semantic Representation of a Scientific Corpus—0
Social diversity and social preferences in mixed-motive reinforcement learning—0
Social diversity for reducing the impact of information cascades on social learning—0
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
Softlog-Softmax Layers and Divergences Contribute to a Computationally Dependable Ensemble Learning—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
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
Something for (almost) nothing: Improving deep ensemble calibration using unlabeled data—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
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
Sound-to-imagination: Unsupervised cross- modal translation using deep dense network architecture.—0
Source-free Domain Adaptation Requires Penalized Diversity—0
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