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Tensorflex: Tensorflow bindings for the Elixir programming language
Anshuman Chhabra
Event URL: https://openreview.net/forum?id=rkxeCt6VhX »

Recently, with the advent of programmatic and practical machine learning tools, programmers have been able to successfully integrate applications for the web and the mobile with artificial intelligence capabilities. This trend has largely been possible because of major organizations and software companies releasing their machine learning frameworks to the public-- such as Tensorflow (Google), MXnet (Amazon) and PyTorch (Facebook). Python has been the de facto choice as the programming language for these frameworks because of it’s versatility and ease-of-use. In a similar vein, Elixir is the functional programming language equivalent of Python and Ruby, in that it combines the versatility and ease-of-use that Python and Ruby boast of, with functional programming paradigms and the Erlang VM’s fault tolerance and robustness. However, despite these obvious advantages, Elixir, similar to other functional programming languages, does not provide developers with a machine learning toolset which is essential for equipping applications with deep learning and statistical inference features. To bridge this gap, we present Tensorflex, an open source framework that allows users to leverage pre-trained Tensorflow models (written in Python, C or C++) for Inference (generating predictions) in Elixir. Moreover, Tensorflex was written as part of a Google Summer of Code (2018) project by Anshuman Chhabra, and José Valim was the mentor for the same.

Author Information

Anshuman Chhabra (University of California, Davis)
Anshuman Chhabra

Anshuman Chhabra is a Ph.D candidate at the University of California, Davis being advised by Prof. Prasant Mohapatra. Prior to that, he completed his B.Eng in Electronics and Communication Engineering from the University of Delhi, India. His research seeks to improve Machine Learning (ML) models and facilitate their adoption into society by analyzing model robustness along two dimensions: adversarial robustness (adversarial attacks/defenses against models) and social robustness (fair machine learning). His other research interests include designing Machine Learning and Reinforcement Learning based debiasing interventions for social media platforms such as YouTube and Twitter. He received the UC Davis Graduate Student Fellowship in 2018, and has held research positions at ESnet, Lawrence Berkeley National Laboratory, USA (2017), the Max Planck Institute for Software Systems, Germany (2020), and the University of Amsterdam, Netherlands (2022).

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