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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 7101–7125 of 9051 papers

TitleStatusHype
The Bures Metric for Generative Adversarial Networks—0
Personalized Federated Learning with Moreau EnvelopesCode1
A Note on the Global Convergence of Multilayer Neural Networks in the Mean Field Regime—0
Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization—0
Neural Ensemble Search for Uncertainty Estimation and Dataset ShiftCode1
Geodesic-HOF: 3D Reconstruction Without Cutting Corners—0
Non-local Policy Optimization via Diversity-regularized Collaborative Exploration—0
Language-Conditioned Goal Generation: a New Approach to Language Grounding in RL—0
Learning Diverse Representations for Fast Adaptation to Distribution Shift—0
Information Extraction of Clinical Trial Eligibility CriteriaCode1
Attribute analysis with synthetic dataset for person re-identification—0
Towards control of opinion diversity by introducing zealots into a polarised social groupCode0
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
Language-Conditioned Goal Generation: a New Approach to Language Grounding for RL—0
Mental Workload and Language Production in Non-Native Speaker IPA Interaction—0
PeopleMap: Visualization Tool for Mapping Out Researchers using Natural Language ProcessingCode1
Fully Unsupervised Diversity Denoising with Convolutional Variational AutoencodersCode1
Hypergraph Clustering for Finding Diverse and Experienced GroupsCode0
HausaMT v1.0: Towards English-Hausa Neural Machine Translation—0
Self-Distillation as Instance-Specific Label SmoothingCode1
Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research—0
EDCompress: Energy-Aware Model Compression for Dataflows—0
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study—0
Skill Discovery of Coordination in Multi-agent Reinforcement Learning—0
Optimally Combining Classifiers for Semi-Supervised LearningCode0
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