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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 4176–4200 of 9051 papers

TitleStatusHype
Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery—0
Argument Quality Assessment in the Age of Instruction-Following Large Language Models—0
Evaluating for Diversity in Question Generation over Text—0
Comeback kids: an evolutionary approach of the long-run innovation process—0
Evaluating Evasion Strategies in Zebrafish Larvae—0
Evaluating Diversity of Multiword Expressions in Annotated Text—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
Action Unit Memory Network for Weakly Supervised Temporal Action Localization—0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images—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
Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education—0
Evaluating automatic cross-domain Dutch semantic role annotation—0
Combining RGB and Points to Predict Grasping Region for Robotic Bin-Picking—0
Evaluating and reducing the distance between synthetic and real speech distributions—0
Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference—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
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning—0
Evaluating AI for Law: Bridging the Gap with Open-Source Solutions—0
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving—0
Combining multi-spectral data with statistical and deep-learning models for improved exoplanet detection in direct imaging at high contrast—0
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