Code Generation is an important field to predict explicit code or program structure from multimodal data sources such as incomplete code, programs in another programming language, natural language descriptions or execution examples. Code Generation tools can assist the development of automatic programming tools to improve programming productivity.
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Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications.
Ranked #1 on Code Generation on 100 sleep nights of 8 caregivers (using extra training data)
Hardware architectures and machine learning (ML) libraries evolve rapidly.
Deep learning models typically use single-precision (FP32) floating point data types for representing activations and weights, but a slew of recent research work has shown that computations with reduced-precision data types (FP16, 16-bit integers, 8-bit integers or even 4- or 2-bit integers) are enough to achieve same accuracy as FP32 and are much more efficient.
This paper proposes an end-to-end deep hashing framework with category mask for fast video retrieval.
We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs).
Ranked #1 on Code Generation on Django
Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures.
Automated documentation of programming source code and automated code generation from natural language are challenging tasks of both practical and scientific interest.