Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
program_induction [2018/03/28 11:55]
admin
program_induction [2018/09/08 16:22]
admin
Line 247: Line 247:
 We demonstrate the effectiveness of our approach by using it to predict a method'​s name from the vector representation of its body. We evaluate our approach by training a model on a dataset of 14M methods. We show that code vectors trained on this dataset can predict method names from files that were completely unobserved during training. Furthermore,​ we show that our model learns useful method name vectors that capture semantic similarities,​ combinations,​ and analogies. ​ We demonstrate the effectiveness of our approach by using it to predict a method'​s name from the vector representation of its body. We evaluate our approach by training a model on a dataset of 14M methods. We show that code vectors trained on this dataset can predict method names from files that were completely unobserved during training. Furthermore,​ we show that our model learns useful method name vectors that capture semantic similarities,​ combinations,​ and analogies. ​
 Comparing previous techniques over the same data set, our approach obtains a relative improvement of over 75%, being the first to successfully predict method names based on a large, cross-project,​ corpus. Comparing previous techniques over the same data set, our approach obtains a relative improvement of over 75%, being the first to successfully predict method names based on a large, cross-project,​ corpus.
 +
 +https://​deepmind.com/​blog/​learning-to-generate-images/​ Learning to write programs that generate images
 +
 +
 +
 +https://​www.microsoft.com/​en-us/​research/​publication/​neural-guided-deductive-search-real-time-program-synthesis-examples/​ Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples
 +
 +. In this work, we propose Neural Guided Deductive Search (NGDS), a hybrid synthesis technique that combines the best of both symbolic logic techniques and statistical models. Thus, it produces programs that satisfy the provided specifications by construction and generalize well on unseen examples, similar to data-driven systems. Our technique effectively utilizes the deductive search framework to reduce the learning problem of the neural component to a simple supervised learning setup. Further, this allows us to both train on sparingly available real-world data and still leverage powerful recurrent neural network encoders. We demonstrate the effectiveness of our method by evaluating on real-world customer scenarios by synthesizing accurate programs with up to 12× speed-up compared to state-of-the-art systems.
 +
 +https://​arxiv.org/​abs/​1804.00218v1 Synthesis of Differentiable Functional Programs for Lifelong Learning
 +
 +Our learning algorithm consists of: (1) a program synthesizer that performs a type-directed search over programs in this language, and decides on the library functions that should be reused and the architectures that should be used to combine them; and (2) a neural module that trains synthesized programs using stochastic gradient descent.