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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 7476–7500 of 9051 papers

TitleStatusHype
Positive Diversity Tuning for Machine Translation System Combination—0
PostDoc: Generating Poster from a Long Multimodal Document Using Deep Submodular Optimization—0
Post-hoc loss-calibration for Bayesian neural networks—0
Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression—0
Post-Training Overfitting Mitigation in DNN Classifiers—0
Post-Training Quantization for Vision Transformer—0
Power Efficient Discontinuous Reception in THz and mmWave Wireless Systems—0
Power Scaling Law for Optical IRSs and Comparison with Optical Relays—0
PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators—0
PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving—0
PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity—0
PPT: Pretraining with Pseudo-Labeled Trajectories for Motion Forecasting—0
PQD: Post-training Quantization for Efficient Diffusion Models—0
PQuAD: A Persian Question Answering Dataset—0
Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis—0
Unsupervised Estimation of Ensemble Accuracy—0
Practical Insights of Repairing Model Problems on Image Classification—0
Taxonomy of Machine Learning Safety: A Survey and Primer—0
Pragmatically Appropriate Diversity for Dialogue Evaluation—0
Prague Dependency Treebank - Consolidated 1.0—0
Interactive Neural Style Transfer with ArtistsCode0
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence GenerationCode0
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and LimitationsCode0
Which Argumentative Aspects of Hate Speech in Social Media can be reliably identified?Code0
Synthesizing Audio from Silent Video using Sequence to Sequence ModelingCode0
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