ICLR 2022
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Deep Learning for Code

Torsten Scholak · Gabriel Orlanski · Disha Shrivastava · Arun Raja · Dzmitry Bahdanau · Jonathan Herzig

An exciting application area of machine learning and deep learning methods is completion, repair, synthesis, and automatic explanation of program code. This field has received a fair amount of attention in the last decade, yet arguably the recent application of large scale language modelling techniques to the domain of code holds a tremendous promise to completely revolutionize this area. The new large pretrained models excel at completing code and synthesizing code from natural language descriptions; they work across a wide range of domains, tasks, and programming languages. The excitement about new possibilities is spurring tremendous interest in both industry and academia. Yet, we are just beginning to explore the potential of large-scale deep learning for code, and state-of-the-art models still struggle with correctness and generalization. This calls for platforms to exchange ideas and discuss the challenges in this line of work. Deep Learning for Code (DL4C) is a workshop that will provide a platform for researchers to share their work on deep learning for code.DL4C welcomes researchers interested in a number of topics, including but not limited to: AI code assistants, representations and model architectures for code, pretraining methods, methods for producing code from natural language, static code analysis and evaluation of deep learning for code techniques.

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Timezone: America/Los_Angeles