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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 8176–8200 of 9051 papers

TitleStatusHype
PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages—0
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and DiversityCode0
Training for Diversity in Image Paragraph CaptioningCode0
Bayesian Prediction of Future Street Scenes using Synthetic LikelihoodsCode0
Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation AbilityCode0
S-System, Geometry, Learning, and Optimization: A Theory of Neural Networks—0
Deep Probabilistic Video Compression—0
microGAN: Promoting Variety through Microbatch Discrimination—0
Model-Agnostic Meta-Learning for Multimodal Task Distributions—0
Teaching to Teach by Structured Dark Knowledge—0
Discrete Structural Planning for Generating Diverse Translations—0
Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation—0
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation LearningCode0
How does geographical distance translate into genetic distance?—0
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation LearningCode0
A Neural Compositional Paradigm for Image Captioning—0
DPPy: Sampling DPPs with PythonCode0
Better Conversations by Modeling,Filtering,and Optimizing for Coherence and DiversityCode0
Albumentations: fast and flexible image augmentationsCode0
Generating Informative and Diverse Conversational Responses via Adversarial Information MaximizationCode0
Maximum-Entropy Fine-Grained Classification—0
Skeleton-to-Response: Dialogue Generation Guided by Retrieval MemoryCode0
Personalizing Fairness-aware Re-ranking—0
Automatic, Personalized, and Flexible Playlist Generation using Reinforcement Learning—0
Semantic WordRank: Generating Finer Single-Document Summarizations—0
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