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Naveen Panwar

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3 papers

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AAAI Conference 2021 Conference Paper

Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text

Nishtha Madaan
Inkit Padhi
Naveen Panwar
Diptikalyan Saha

Machine Learning has seen tremendous growth recently, which has led to a larger adoption of ML systems for educational assessments, credit risk, healthcare, employment, criminal justice, to name a few. Trustworthiness of ML and NLP systems is a crucial aspect and requires guarantee that the decisions they make are fair and robust. Aligned with this, we propose a framework GYC, to generate a set of counterfactual text samples, which are crucial for testing these ML systems. Our main contributions include a) We introduce GYC, a framework to generate counterfactual samples such that the generation is plausible, diverse, goal-oriented and effective, b) We generate counterfactual samples, that can direct the generation towards a corresponding condition such as named-entity tag, semantic role label, or sentiment. Our experimental results on various domains, show that GYC generates counterfactual text samples exhibiting the above four properties. GYC generates counterfactuals that can act as test cases to evaluate a model and any text debiasing algorithm.

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AAAI Conference 2018 System Paper

Democratization of Deep Learning Using DARVIZ

Anush Sankaran
Naveen Panwar
Shreya Khare
Senthil Mani
Akshay Sethi
Rahul Aralikatte
Neelamadhav Gantayat

With an abundance of research papers in deep learning, adoption and reproducibility of existing works becomes a challenge. To make a DL developer life easy, we propose a novel system, DARVIZ, to visually design a DL model using a drag-and-drop framework in an platform agnostic manner. The code could be automatically generated in both Caffe and Keras. DARVIZ could import (i) any existing Caffe code, or (ii) a research paper containing a DL design; extract the design, and present it in visual editor.

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AAAI Conference 2018 Conference Paper

DLPaper2Code: Auto-Generation of Code From Deep Learning Research Papers

Akshay Sethi
Anush Sankaran
Naveen Panwar
Shreya Khare
Senthil Mani

With an abundance of research papers in deep learning, reproducibility or adoption of the existing works becomes a challenge. This is due to the lack of open source implementations provided by the authors. Even if the source code is available, then re-implementing research papers in a different library is a daunting task. To address these challenges, we propose a novel extensible approach, DLPaper2Code, to extract and understand deep learning design ﬂow diagrams and tables available in a research paper and convert them to an abstract computational graph. The extracted computational graph is then converted into execution ready source code in both Keras and Caffe, in real-time. An arXiv-like website is created where the automatically generated designs is made publicly available for 5, 000 research papers. The generated designs could be rated and edited using an intuitive drag-and-drop UI framework in a crowd sourced manner. To evaluate our approach, we create a simulated dataset with over 216, 000 valid deep learning design ﬂow diagrams using a manually deﬁned grammar. Experiments on the simulated dataset show that the proposed framework provide more than 93% accuracy in ﬂow diagram content extraction.

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