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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 821–830 of 9051 papers

TitleStatusHype
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP ModelsCode1
Harvesting Event Schemas from Large Language ModelsCode1
Device-Robust Acoustic Scene Classification via Impulse Response AugmentationCode1
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution EstimationCode1
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackersCode1
Multi-Robot Coordination and Layout Design for Automated WarehousingCode1
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized DiversityCode1
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent LearningCode1
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