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Found 11 papers, 2 papers with code
Theoretical research on generative diffusion models: an overview
Generative diffusion models showed high success in many fields with a powerful theoretical background.
Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving
Math Word Problem (MWP) solving presents a challenging task in Natural Language Processing (NLP).
Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies
The study concludes that the use of augmentation methods, especially in conjunction with MCCL, leads to improved results in various classification tasks, providing a foundation for future advances in text augmentation strategies in NLP.
Iterative Mask Filling: An Effective Text Augmentation Method Using Masked Language Modeling
Data augmentation is an effective technique for improving the performance of machine learning models.
Investigating Semi-Supervised Learning Algorithms in Text Datasets
SSL methods that use data augmentation are most successful for image datasets.
From Text to Multimodal: A Comprehensive Survey of Adversarial Example Generation in Question Answering Systems
Integrating adversarial machine learning with Question Answering (QA) systems has emerged as a critical area for understanding the vulnerabilities and robustness of these systems.
Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models
This study introduces and evaluates tiny, mini, small, and medium-sized uncased Turkish BERT models, aiming to bridge the research gap in less-resourced languages.
Reviewer Assignment Problem: A Systematic Review of the Literature
Appropriate reviewer assignment significantly impacts the quality of proposal evaluation, as accurate and fair reviews are contingent on their assignment to relevant reviewers.
Transformers as Neural Augmentors: Class Conditional Sentence Generation via Variational Bayes
The presented results show that, our model increases the performance of current models compared to other data augmentation techniques with a small amount of computation power.
A Robust Optimization Method for Label Noisy Datasets Based on Adaptive Threshold: Adaptive-k
SGD does not produce robust results on datasets with label noise.
Training with Growing Sets: A Simple Alternative to Curriculum Learning and Self Paced Learning
Studies in these topics show that starting with a small training set and adding new samples according to difficulty levels improves the learning performance.
