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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 6476–6500 of 9051 papers

TitleStatusHype
Augmenting High-dimensional Nonlinear Optimization with Conditional GANsCode0
Fading Margins for Large-Scale Antenna Systems—0
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample PredictionCode1
Corporate Social Responsibility and Corporate Governance: A cognitive approach—0
Data-Aware Device Scheduling for Federated Edge Learning—0
Learning Invariant Representation of Tasks for Robust Surgical State Estimation—0
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative ModelsCode1
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual ConceptsCode1
Data-Driven Logistic Regression Ensembles With Applications in Genomics—0
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning—0
Improving speech recognition models with small samples for air traffic control systems—0
Crowdsourcing Parallel Corpus for English-Oromo Neural Machine Translation using Community Engagement Platform—0
A Unified Batch Selection Policy for Active Metric Learning—0
Developing parsimonious ensembles using predictor diversity within a reinforcement learning frameworkCode0
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems—0
Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methods—0
A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic MeasuresCode0
Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection—0
LTL2Action: Generalizing LTL Instructions for Multi-Task RLCode1
Toward Improving Coherence and Diversity of Slogan GenerationCode0
Searching for Designs in-between—0
"What's in the box?!": Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint Models—0
AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language ModelsCode1
Enhancing Audio Augmentation Methods with Consistency Learning—0
Diverse Single Image Generation with Controllable Global Structure—0
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