Open AccessProceedings Article
Learning Program Embeddings to Propagate Feedback on Student Code
Chris Piech,Jonathan Huang,Andy Nguyen,Mike Phulsuksombati,Mehran Sahami,Leonidas J. Guibas +5 more
- pp 1093-1102
Reads0
Chats0
TLDR
A neural network method is introduced to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and an algorithm for feedback at scale is proposed using these linear maps as features.Abstract:
Providing feedback, both assessing final work and giving hints to stuck students, is difficult for open-ended assignments in massive online classes which can range from thousands to millions of students. We introduce a neural network method to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and propose an algorithm for feedback at scale using these linear maps as features. We apply our algorithm to assessments from the Code.org Hour of Code and Stanford University's CS1 course, where we propagate human comments on student assignments to orders of magnitude more submissions.read more
Citations
More filters
Posted Content
Neural Program Synthesis with a Differentiable Fixer.
TL;DR: This work presents a new program synthesis approach that combines an encoder-decoder based synthesis architecture with a differentiable program fixer, and shows that the addition of the fixer module leads to a significant improvement on synthesis accuracy compared to using beam search.
Proceedings ArticleDOI
Source Code Summarization Using Attention-Based Keyword Memory Networks
TL;DR: This work proposes a two-phase model that consists of a keyword predictor and a description generator that can effectively reduce the semantic gap and generate more accurate descriptions of source codes.
Proceedings Article
Synthesizing Tasks for Block-based Programming
Umair Z. Ahmed,Maria Christakis,Aleksandr Efremov,Nigel Fernandez,Ahana Ghosh,Abhik Roychoudhury,Adish Singla +6 more
TL;DR: This paper formalizes the problem of synthesizing visual programming tasks and proposes a novel methodology to automatically generate a set of new tasks along with solution codes such that tasks T^{in} and T^{out} are conceptually similar but visually dissimilar.
Journal ArticleDOI
Hyperbolic Function Embedding: Learning Hierarchical Representation for Functions of Source Code in Hyperbolic Space
TL;DR: A novel hyperbolic function embedding (HFE) method is proposed, which can learn a distributed and hierarchical representation for each function via the Poincaré ball model, which is more compact in terms of lower dimensionality than the existing graph embedding methods.
Proceedings ArticleDOI
Unleashing the Power of Compiler Intermediate Representation to Enhance Neural Program Embeddings
TL;DR: This paper introduces IRGEN, a framework based on genetic algorithms (GA), to identify (near-)optimal sequences of optimization flags that can significantly improve embedding quality, and uses IRGEN to find optimal sequences of LLVM optimization flags by performing GA on source code datasets.
References
More filters
Proceedings Article
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
TL;DR: Adaptive subgradient methods as discussed by the authors dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning, which allows us to find needles in haystacks in the form of very predictive but rarely seen features.
Journal Article
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
TL;DR: This work describes and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal functions that can be chosen in hindsight.
Journal Article
Random search for hyper-parameter optimization
James Bergstra,Yoshua Bengio +1 more
TL;DR: This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid, and shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper- parameter optimization algorithms.
Proceedings Article
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Richard Socher,Alex Perelygin,Jean Y. Wu,Jason Chuang,Christopher D. Manning,Andrew Y. Ng,Christopher Potts +6 more
TL;DR: A Sentiment Treebank that includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality, and introduces the Recursive Neural Tensor Network.
Book
A complexity measure
TL;DR: In this paper, a graph-theoretic complexity measure for managing and controlling program complexity is presented. But the complexity is independent of physical size, and complexity depends only on the decision structure of a program.