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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 6801–6825 of 9051 papers

TitleStatusHype
Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images—0
Microphone Array Signal Processing and Deep Learning for Speech Enhancement—0
Middle-Out Decoding—0
MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space—0
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication—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
Minimax Active Learning—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
Minor climatic fluctuations lead to species extinction in a conceptual ecosystem model—0
Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs—0
MIPE: A Metric Independent Pipeline for Effective Code-Mixed NLG Evaluation—0
MIST GAN: Modality Imputation Using Style Transfer for MRI—0
MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation—0
Mitigating Biases to Embrace Diversity: A Comprehensive Annotation Benchmark for Toxic Language—0
Mitigating Bias in Facial Analysis Systems by Incorporating Label Diversity—0
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data—0
Mitigating stereotypical biases in text to image generative systems—0
Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation—0
Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective—0
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