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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 3301–3325 of 9051 papers

TitleStatusHype
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving—0
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning—0
BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations—0
Evaluating and reducing the distance between synthetic and real speech distributions—0
Evaluating automatic cross-domain Dutch semantic role annotation—0
Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education—0
Diversifying Reply Suggestions using a Matching-Conditional Variational Autoencoder—0
Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization—0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao—0
Evaluating Diversity of Multiword Expressions in Annotated Text—0
Evaluating Evasion Strategies in Zebrafish Larvae—0
Comeback kids: an evolutionary approach of the long-run innovation process—0
Evaluating for Diversity in Question Generation over Text—0
Brain-Computer Interfaces for Emotional Regulation in Patients with Various Disorders—0
Evaluating Image Caption via Cycle-consistent Text-to-Image Generation—0
Diversifying Question Generation over Knowledge Base via External Natural Questions—0
Evaluating Inclusivity, Equity, and Accessibility of NLP Technology: A Case Study for Indian Languages—0
Evaluating the Diversity, Equity and Inclusion of NLP Technology: A Case Study for Indian Languages—0
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling—0
Comment Section Personalization: Algorithmic, Interface, and Interaction Design—0
Evaluating Post-Training Compression in GANs using Locality-Sensitive Hashing—0
Evaluating race and sex diversity in the world's largest companies using deep neural networks—0
Evaluating Stochastic Rankings with Expected Exposure—0
Shadow Transfer: Single Image Relighting For Urban Road Scenes—0
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