no code implementations • 18 Aug 2023 • Mohammad Ahmadi Achachlouei, Omkar Patil, Tarun Joshi, Vijayan N. Nair
This paper surveys the current state of the art in document automation (DA).
no code implementations • 26 May 2022 • Omkar Patil, Rahul Singh, Tarun Joshi
This is not only because of the limitations in text generation capabilities but also due that to the lack of a proper definition of what qualifies as a paraphrase and corresponding metrics to measure how good it is.
no code implementations • 28 Oct 2021 • Archit Parnami, Rahul Singh, Tarun Joshi
Our results indicate that the method could eliminate as much as 40% of the attention heads in the BERT transformer model with no loss in accuracy.
no code implementations • 23 Sep 2021 • Mohammad Ahmadi Achachlouei, Omkar Patil, Tarun Joshi, Vijayan N. Nair
This paper surveys the current state of the art in document automation (DA).
no code implementations • 18 May 2021 • Wei Zhao, Rahul Singh, Tarun Joshi, Agus Sudjianto, Vijayan N. Nair
We also study the impact of the complexity of the convolutional layers and the classification layers on the model performance.
no code implementations • 20 Apr 2021 • Rahul Singh, Karan Jindal, Yufei Yu, Hanyu Yang, Tarun Joshi, Matthew A. Campbell, Wayne B. Shoumaker
This paper proposes a strategy to assess the robustness of different machine learning models that involve natural language processing (NLP).
no code implementations • 4 Sep 2020 • Mina Naghshnejad, Tarun Joshi, Vijayan N. Nair
Additionally, we discuss different techniques to improve the performance of these models at each stage of the pipeline.
no code implementations • 26 Aug 2020 • Wei Zhao, Tarun Joshi, Vijayan N. Nair, Agus Sudjianto
Deep neural networks are increasingly used in natural language processing (NLP) models.
no code implementations • 12 Aug 2020 • Rahul Singh, Tarun Joshi, Vijayan N. Nair, Agus Sudjianto
We propose algorithms to create adversarial attacks to assess model robustness in text classification problems.