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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 8801–8825 of 9051 papers

TitleStatusHype
DI2: prior-free and multi-item discretization ofbiomedical data and its applicationsCode0
dhSegment: A generic deep-learning approach for document segmentationCode0
Automatic Identification of Traditional Colombian Music Genres based on Audio Content Analysis and Machine Learning TechniqueCode0
DGSAN: Discrete Generative Self-Adversarial NetworkCode0
Rethinking Mitosis Detection: Towards Diverse Data and Feature RepresentationCode0
A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANsCode0
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM PerformanceCode0
Unveiling the Mystery of Visual Attributes of Concrete and Abstract Concepts: Variability, Nearest Neighbors, and Challenging CategoriesCode0
Rethinking Robustness of Model AttributionsCode0
Vision-and-Language PretrainingCode0
Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation AbilityCode0
No Offense Taken: Eliciting Offensiveness from Language ModelsCode0
Exploring Format Consistency for Instruction TuningCode0
Automatic Generation of Word Problems for Academic Education via Natural Language Processing (NLP)Code0
Rethinking the transfer learning for FCN based polyp segmentation in colonoscopyCode0
Normalized DiversificationCode0
Normalizing Flow based Hidden Markov Models for Classification of Speech Phones with ExplainabilityCode0
Rethinking Time Encoding via Learnable Transformation FunctionsCode0
Rethinking Token Reduction with Parameter-Efficient Fine-Tuning in ViT for Pixel-Level TasksCode0
Exploring Flat Minima for Domain Generalization with Large Learning RatesCode0
A Guide for Practical Use of ADMG Causal Data AugmentationCode0
TVM: An Automated End-to-End Optimizing Compiler for Deep LearningCode0
Novel Policy Seeking with Constrained OptimizationCode0
Cascading CMA-ES Instances for Generating Input-diverse Solution BatchesCode0
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement LearningCode0
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