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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 1976–2000 of 9051 papers

TitleStatusHype
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain GeneralizationCode1
DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts—0
A Survey of Data Synthesis ApproachesCode0
On the Effectiveness of Acoustic BPE in Decoder-Only TTS—0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
Diverse and Fine-Grained Instruction-Following Ability Exploration with Synthetic Data—0
RobocupGym: A challenging continuous control benchmark in RobocupCode1
Regurgitative Training: The Value of Real Data in Training Large Language Models—0
Advanced Framework for Animal Sound Classification With Features Optimization—0
Semantically Rich Local Dataset Generation for Explainable AI in GenomicsCode0
Towards a Scalable Reference-Free Evaluation of Generative ModelsCode0
Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate MetricCode0
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning—0
Reinforcement Learning for Sequence Design Leveraging Protein Language Models—0
Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking DatasetCode1
Fake News Detection: It's All in the Data!Code5
Investigating the Effects of Large-Scale Pseudo-Stereo Data and Different Speech Foundation Model on Dialogue Generative Spoken Language Model—0
Generative Monoculture in Large Language ModelsCode0
ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social InteractionsCode0
The #Somos600M Project: Generating NLP resources that represent the diversity of the languages from LATAM, the Caribbean, and Spain—0
Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs—0
Modified CMA-ES Algorithm for Multi-Modal Optimization: Incorporating Niching Strategies and Dynamic Adaptation Mechanism—0
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets—0
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
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