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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
Robustness for Free: Quality-Diversity Driven Discovery of Agile Soft Robotic Gaits—0
NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors—0
Artificial Intelligence Ethics Education in Cybersecurity: Challenges and Opportunities: a focus group report—0
Multi-dimensional data refining strategy for effective fine-tuning LLMs—0
Long Story Short: a Summarize-then-Search Method for Long Video Question AnsweringCode0
Tailoring Mixup to Data for CalibrationCode0
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language ModelsCode1
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards—0
StableFDG: Style and Attention Based Learning for Federated Domain Generalization—0
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object SegmentationCode1
Optimal Budgeted Rejection Sampling for Generative Models—0
Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion—0
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval—0
Evaluating Neural Language Models as Cognitive Models of Language AcquisitionCode0
Optimizing accuracy and diversity: a multi-task approach to forecast combinations—0
Farthest Greedy Path Sampling for Two-shot Recommender Search—0
SimMMDG: A Simple and Effective Framework for Multi-modal Domain GeneralizationCode1
Role of Structural and Conformational Diversity for Machine Learning Potentials—0
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient SelectionCode1
A Path to Simpler Models Starts With Noise—0
AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series ForecastingCode0
LLMs and Finetuning: Benchmarking cross-domain performance for hate speech detection—0
Controllable Group Choreography using Contrastive DiffusionCode1
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation—0
End-to-End Autoregressive Retrieval via Bootstrapping for Smart Reply Systems—0
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