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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 4426–4450 of 9051 papers

TitleStatusHype
Diversity from Human Feedback—0
Hexa: Self-Improving for Knowledge-Grounded Dialogue System—0
Growing ecosystem of deep learning methods for modeling proteinx2013protein interactions—0
Understanding Transfer Learning and Gradient-Based Meta-Learning TechniquesCode0
Affine Frequency Division Multiplexing With Index Modulation—0
Increasing Entropy to Boost Policy Gradient Performance on Personalization TasksCode0
Enhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions—0
Benchmarking Large Language Models with Augmented Instructions for Fine-grained Information Extraction—0
FM Tone Transfer with Envelope Learning—0
A Holistic Evaluation of Piano Sound Quality—0
QE-BEV: Query Evolution for Bird's Eye View Object Detection in Varied ContextsCode0
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIsCode0
Knolling Bot: Learning Robotic Object Arrangement from Tidy Demonstrations—0
A Process for Topic Modelling Via Word Embeddings—0
Learning Personalized Alignment for Evaluating Open-ended Text Generation—0
Robustness-Guided Image Synthesis for Data-Free Quantization—0
CLEVRER-Humans: Describing Physical and Causal Events the Human Way—0
Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries—0
Unbiased estimation of sampling variance for Simpson's diversity indexCode0
MUNCH: Modelling Unique 'N Controllable Heads—0
A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization—0
Something for (almost) nothing: Improving deep ensemble calibration using unlabeled data—0
scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain—0
Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning—0
Exploring Model Learning Heterogeneity for Boosting Ensemble RobustnessCode0
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