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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 4776–4800 of 9051 papers

TitleStatusHype
Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference—0
Social and environmental impact of recent developments in machine learning on biology and chemistry researchCode0
School closures and educational path: how the Covid-19 pandemic affected transitions to college—0
Towards complete representation of bacterial contents in metagenomic samples—0
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm—0
Parea: multi-view ensemble clustering for cancer subtype discoveryCode1
Start Small: Training Controllable Game Level Generators without Training Data by Learning at Multiple SizesCode0
Federated Stain Normalization for Computational PathologyCode0
Domain-Unified Prompt Representations for Source-Free Domain GeneralizationCode1
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis—0
UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in RecommendationCode0
Revisiting Few-Shot Learning from a Causal PerspectiveCode0
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-LearningCode0
Mutation Effect Generalizability under Selection-Drift—0
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual ExplanationsCode0
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided DiffusionCode1
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models—0
Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation—0
Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation—0
Multiple-Choice Question Generation: Towards an Automated Assessment Framework—0
Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation—0
Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language ModelCode0
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation—0
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
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