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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 5551–5575 of 9051 papers

TitleStatusHype
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks—0
Middle-Out Decoding—0
MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space—0
Training Mixed-Objective Pointing Decoders for Block-Level Optimization in Search Recommendation—0
Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction—0
Training on test data: Removing near duplicates in Fashion-MNIST—0
Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data—0
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication—0
Training Task Experts through Retrieval Based Distillation—0
Mind the gap: how multiracial individuals get left behind when we talk about race, ethnicity, and ancestry in genomic research—0
Distribution-Aware Compensation Design for Sustainable Data Rights in Machine Learning—0
Augmentations vs Algorithms: What Works in Self-Supervised Learning—0
Estimation of functional diversity and species traits from ecological monitoring data—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation—0
Auditing Source Diversity Bias in Video Search Results Using Virtual Agents—0
Auditing Google's Search Algorithm: Measuring News Diversity Across Brazil, the UK, and the US—0
Minimax Active Learning—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity—0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
Minimizing Annotation Effort via Max-Volume Spectral Sampling—0
Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision—0
Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents—0
Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization—0
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