research
∙
07/26/2023
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis
When writing programs, people have the ability to tackle a new complex t...
share
research
∙
06/11/2023
A Probabilistic Framework for Modular Continual Learning
Modular approaches, which use a different composition of modules for eac...
research
∙
06/03/2023
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas
Search is an important technique in program synthesis that allows for ad...
research
∙
12/19/2022
Natural Language to Code Generation in Interactive Data Science Notebooks
Computational notebooks, such as Jupyter notebooks, are interactive comp...
research
∙
08/15/2022
A Library for Representing Python Programs as Graphs for Machine Learning
Graph representations of programs are commonly a central element of mach...
research
∙
07/21/2022
Language Model Cascades
Prompted models have demonstrated impressive few-shot learning abilities...
research
∙
07/17/2022
Repairing Systematic Outliers by Learning Clean Subspaces in VAEs
Data cleaning often comprises outlier detection and data repair. Systema...
research
∙
04/07/2022
Compositional Generalization and Decomposition in Neural Program Synthesis
When writing programs, people have the ability to tackle a new complex t...
research
∙
04/05/2022
PaLM: Scaling Language Modeling with Pathways
Large language models have been shown to achieve remarkable performance ...
research
∙
03/20/2022
CrossBeam: Learning to Search in Bottom-Up Program Synthesis
Many approaches to program synthesis perform a search within an enormous...
research
∙
11/30/2021
Show Your Work: Scratchpads for Intermediate Computation with Language Models
Large pre-trained language models perform remarkably well on tasks that ...
research
∙
08/16/2021
Program Synthesis with Large Language Models
This paper explores the limits of the current generation of large langua...
research
∙
06/26/2021
SpreadsheetCoder: Formula Prediction from Semi-structured Context
Spreadsheet formula prediction has been an important program synthesis p...
research
∙
04/11/2021
Couplings for Multinomial Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is a popular sampling method in Bayesian i...
research
∙
12/01/2020
Latent Programmer: Discrete Latent Codes for Program Synthesis
In many sequence learning tasks, such as program synthesis and document ...
research
∙
11/10/2020
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Discrete structures play an important role in applications like program ...
research
∙
10/23/2020
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
Graph neural networks (GNNs) have emerged as a powerful tool for learnin...
research
∙
10/22/2020
Conditional independence by typing
A central goal of probabilistic programming languages (PPLs) is to separ...
research
∙
07/28/2020
BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration
Program synthesis is challenging largely because of the difficulty of se...
research
∙
06/19/2020
Neural Program Synthesis with a Differentiable Fixer
We present a new program synthesis approach that combines an encoder-dec...
research
∙
04/28/2020
SCELMo: Source Code Embeddings from Language Models
Continuous embeddings of tokens in computer programs have been used to s...
research
∙
04/01/2020
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
We present a new approach to the type inference problem for dynamic lang...
research
∙
03/17/2020
Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code
Statistical language modeling techniques have successfully been applied ...
research
∙
02/27/2020
Towards Modular Algorithm Induction
We present a modular neural network architecture Main that learns algori...
research
∙
02/21/2020
Incremental Sampling Without Replacement for Sequence Models
Sampling is a fundamental technique, and sampling without replacement is...
research
∙
02/13/2020
Learning to Represent Programs with Property Signatures
We introduce the notion of property signatures, a representation for pro...
research
∙
11/04/2019
Learning to Fix Build Errors with Graph2Diff Neural Networks
Professional software developers spend a significant amount of time fixi...
research
∙
07/15/2019
Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data
We focus on the problem of unsupervised cell outlier detection in mixed ...
research
∙
05/30/2019
How Often Do Single-Statement Bugs Occur? The ManySStuBs4J Dataset
Program repair is an important but difficult software engineering proble...
research
∙
05/30/2019
Learning Semantic Annotations for Tabular Data
The usefulness of tabular data such as web tables critically depends on ...
research
∙
03/13/2019
Maybe Deep Neural Networks are the Best Choice for Modeling Source Code
Statistical language modeling techniques have successfully been applied ...
research
∙
11/27/2018
Wrangling Messy CSV Files by Detecting Row and Type Patterns
It is well known that data scientists spend the majority of their time o...
research
∙
11/04/2018
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction
Automatically annotating column types with knowledge base (KB) concepts ...
research
∙
11/02/2018
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
Stan is a probabilistic programming language that has been increasingly ...
research
∙
06/12/2018
Deep Learning to Detect Redundant Method Comments
Comments in software are critical for maintenance and reuse. But apart f...
research
∙
05/31/2018
Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models
Deep generative models can learn to generate realistic-looking images on...
research
∙
04/21/2018
Variational Inference In Pachinko Allocation Machines
The Pachinko Allocation Machine (PAM) is a deep topic model that allows ...
research
∙
03/31/2018
Synthesis of Differentiable Functional Programs for Lifelong Learning
We present a neurosymbolic approach to the lifelong learning of algorith...
research
∙
03/11/2018
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge
Interpreting black box classifiers, such as deep networks, allows an ana...
research
∙
10/14/2017
Popularity of arXiv.org within Computer Science
It may seem surprising that, out of all areas of science, computer scien...
research
∙
09/18/2017
A Survey of Machine Learning for Big Code and Naturalness
Research at the intersection of machine learning, programming languages,...
research
∙
05/22/2017
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Deep generative models provide powerful tools for distributions over com...
research
∙
03/04/2017
Autoencoding Variational Inference For Topic Models
Topic models are one of the most popular methods for learning representa...
research
∙
11/04/2016
Learning Continuous Semantic Representations of Symbolic Expressions
Combining abstract, symbolic reasoning with continuous neural reasoning ...
research
∙
06/19/2016
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation
A good clustering can help a data analyst to explore and understand a da...
research
∙
02/16/2016
A Subsequence Interleaving Model for Sequential Pattern Mining
Recent sequential pattern mining methods have used the minimum descripti...
research
∙
02/09/2016
A Convolutional Attention Network for Extreme Summarization of Source Code
Attention mechanisms in neural networks have proved useful for problems ...
research
∙
10/30/2015
Latent Bayesian melding for integrating individual and population models
In many statistical problems, a more coarse-grained model may be suitabl...
research
∙
10/14/2015
A Bayesian Network Model for Interesting Itemsets
Mining itemsets that are the most interesting under a statistical model ...
research
∙
06/15/2014