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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 5301–5325 of 9051 papers

TitleStatusHype
Loss-based Sequential Learning for Active Domain Adaptation—0
Loss function based second-order Jensen inequality and its application to particle variational inference—0
Loss-Guided Auxiliary Agents for Overcoming Mode Collapse in GFlowNets—0
Lost in Back-Translation: Emotion Preservation in Neural Machine Translation—0
Automatic Keyboard Layout Design for Low-Resource Latin-Script Languages—0
Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation—0
Low-Complexity Linear Diversity-Combining Detector for MIMO-OTFS—0
Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning—0
Low-Rank and Sparse Model Merging for Multi-Lingual Speech Recognition and Translation—0
Towards Robust Multimodal Prompting With Missing Modalities—0
Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation service—0
Towards Robustness and Diversity: Continual Learning in Dialog Generation with Text-Mixup and Batch Nuclear-Norm Maximization—0
LPS@LT-EDI-ACL2022:An Ensemble Approach about Hope Speech Detection—0
LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics—0
LSD-StructureNet: Modeling Levels of Structural Detail in 3D Part Hierarchies—0
Automatic image-based identification and biomass estimation of invertebrates—0
Automatic Generation of News Comments Based on Gated Attention Neural Networks—0
LSTM Networks for Online Cross-Network Recommendations—0
Automatic Generation of Grounded Visual Questions—0
LTP: Lane-Based Trajectory Prediction for Autonomous Driving—0
Towards Simple and Accurate Human Pose Estimation with Stair Network—0
Automatic Feature Selection in Markov State Models Using Genetic Algorithm—0
LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Movie Recommendation—0
Lyapunov Exponents for Diversity in Differentiable Games—0
M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models—0
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