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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 2226–2250 of 9051 papers

TitleStatusHype
A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land?—0
Diverse Audio Captioning via Adversarial Training—0
Argument Quality Assessment in the Age of Instruction-Following Large Language Models—0
Comeback kids: an evolutionary approach of the long-run innovation process—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Argument Identification in Public Comments from eRulemaking—0
A Generic Coordinate Descent Framework for Learning from Implicit Feedback—0
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems—0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization—0
Argument Generation with Retrieval, Planning, and Realization—0
Combining RGB and Points to Predict Grasping Region for Robotic Bin-Picking—0
Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?—0
A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao—0
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving—0
A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks—0
Combining multi-spectral data with statistical and deep-learning models for improved exoplanet detection in direct imaging at high contrast—0
Combining Learned Lyrical Structures and Vocabulary for Improved Lyric Generation—0
ARFlow: Human Action-Reaction Flow Matching with Physical Guidance—0
Action Understanding with Multiple Classes of Actors—0
A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior—0
Combining keyphrase extraction and lexical diversity to characterize ideas in publication titles—0
Combining Kernelized Autoencoding and Centroid Prediction for Dynamic Multi-objective Optimization—0
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?—0
Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization—0
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