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Found 28 papers, 18 papers with code
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$).
Exploring Representation of Horn Clauses using GNNs (Extended Technical Report)
For the second challenge, we explore graph representations of CHCs, and propose a new Relational Hypergraph Neural Network (R-HyGNN) architecture to learn program features.
Learning to Complete Code with Sketches
To evaluate models, we consider both ROUGE as well as a new metric RegexAcc that measures success of generating completions matching long outputs with as few holes as possible.
Self-Supervised Bug Detection and Repair
Machine learning-based program analyses have recently shown the promise of integrating formal and probabilistic reasoning towards aiding software development.
Learning to Extend Molecular Scaffolds with Structural Motifs
Recent advancements in deep learning-based modeling of molecules promise to accelerate in silico drug discovery.
Copy that! Editing Sequences by Copying Spans
Neural sequence-to-sequence models are finding increasing use in editing of documents, for example in correcting a text document or repairing source code.
Analyzing Information Leakage of Updates to Natural Language Models
To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models.
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs
While a wide range of interpretable generative procedures for graphs exist, matching observed graph topologies with such procedures and choices for its parameters remains an open problem.
Analyzing Privacy Loss in Updates of Natural Language Models
To continuously improve quality and reflect changes in data, machine learning-based services have to regularly re-train and update their core models.
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
To enable evaluation of progress on code search, we are releasing the CodeSearchNet Corpus and are presenting the CodeSearchNet Challenge, which consists of 99 natural language queries with about 4k expert relevance annotations of likely results from CodeSearchNet Corpus.
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
Results of experiments comparing different GNN architectures on three tasks from the literature are presented, based on re-implementations of baseline methods.
Program Synthesis and Semantic Parsing with Learned Code Idioms
Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time.
Structured Neural Summarization
Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input.
Learning to Represent Edits
We introduce the problem of learning distributed representations of edits.
Robust Text-to-SQL Generation with Execution-Guided Decoding
We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries.
Constrained Graph Variational Autoencoders for Molecule Design
Graphs are ubiquitous data structures for representing interactions between entities.
Generative Code Modeling with Graphs
Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs.
Graph Partition Neural Networks for Semi-Supervised Classification
We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs.
Pointing Out SQL Queries From Text
We present a system that allows for querying data tables using natural language questions, where the system translates the question into an executable SQL query.
Learning to Represent Programs with Graphs
Learning tasks on source code (i. e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax.
SmartPaste: Learning to Adapt Source Code
As first solutions, we design a set of deep neural models that learn to represent the context of each variable location and variable usage in a data flow-sensitive way.
Summary - TerpreT: A Probabilistic Programming Language for Program Induction
A TerpreT model is composed of a specification of a program representation and an interpreter that describes how programs map inputs to outputs.
Differentiable Functional Program Interpreters
Recent work on differentiable interpreters relaxes the discrete space of programs into a continuous space so that search over programs can be performed using gradient-based optimization.
Differentiable Programs with Neural Libraries
We develop a framework for combining differentiable programming languages with neural networks.
DeepCoder: Learning to Write Programs
We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning.
TerpreT: A Probabilistic Programming Language for Program Induction
TerpreT is similar to a probabilistic programming language: a model is composed of a specification of a program representation (declarations of random variables) and an interpreter describing how programs map inputs to outputs (a model connecting unknowns to observations).
Gated Graph Sequence Neural Networks
Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.
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