ServiceNow Research

Data Augmentation

Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Data augmentation is a widely employed technique to alleviate the problem of data scarcity. In this work, we propose a prompting-based …
Montreal AI Symposium (MAIS),  2022.
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Overcoming challenges in leveraging GANs for few-shot data augmentation
Overcoming challenges in leveraging GANs for few-shot data augmentation
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. …
Workshop at the Conference on Lifelong Learning Agents (CoLLAs),  2022.
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Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Data augmentation alleviates the problem of data scarcity when training language models (LMs) by generating new examples based on the …
Workshop at the Annual Meetings of the Association for Computational Linguistics (ACL),  2022.
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Learning Data Augmentation with Online Bilevel Optimization for Image Classification
Learning Data Augmentation with Online Bilevel Optimization for Image Classification
Data augmentation is a key practice in machine learning for improving generalization performance. However, finding the best data …
Winter Conference on Applications of Computer Vision (WACV),  2021.
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Phylogenetic Manifold Regularization: a semi-supervised approach to predict transcription factor binding sites
Phylogenetic Manifold Regularization: a semi-supervised approach to predict transcription factor binding sites
The computational prediction of transcription factor binding sites remains a challenging problems in bioinformatics, despite …
International Conference on Bioinformatics and Biomedicine (BIBM),  2020.
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Mass spectra alignment using virtual lock-masses
Mass spectra alignment using virtual lock-masses
Mass spectrometry is a valued method to evaluate the metabolomics content of a biological sample. The recent advent of rapid ionization …
Nature Scientific Reports,  2019.
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Adversarial Learning of General Transformations for Data Augmentation
Adversarial Learning of General Transformations for Data Augmentation
Data augmentation (DA) is fundamental against overfitting in large convolutional neural networks, especially with a limited training …
Workshop at the International Conference on Learning Representations (ICLR),  2019.
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