Search Results for author: Marc Brockschmidt

Found 28 papers, 19 papers with code

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

1 code implementation NeurIPS 2023 Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka

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)

1 code implementation14 Jun 2022 Chencheng Liang, Philipp Rümmer, Marc Brockschmidt

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

no code implementations ICLR 2022 Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis

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.

Code Completion Code Generation +1

Self-Supervised Bug Detection and Repair

1 code implementation NeurIPS 2021 Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt

Machine learning-based program analyses have recently shown the promise of integrating formal and probabilistic reasoning towards aiding software development.

Self-Supervised Learning

Copy that! Editing Sequences by Copying Spans

1 code implementation8 Jun 2020 Sheena Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt

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

no code implementations17 Dec 2019 Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Rühle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt

To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models.

Language Modelling

Disentangling Interpretable Generative Parameters of Random and Real-World Graphs

1 code implementation12 Oct 2019 Niklas Stoehr, Emine Yilmaz, Marc Brockschmidt, Jan Stuehmer

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.

Disentanglement Graph Embedding +2

Analyzing Privacy Loss in Updates of Natural Language Models

no code implementations25 Sep 2019 Shruti Tople, Marc Brockschmidt, Boris Köpf, Olga Ohrimenko, Santiago Zanella-Béguelin

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

14 code implementations20 Sep 2019 Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, Marc Brockschmidt

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.

Code Search Information Retrieval +2

GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation

2 code implementations ICML 2020 Marc Brockschmidt

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

1 code implementation NeurIPS 2019 Richard Shin, Miltiadis Allamanis, Marc Brockschmidt, Oleksandr Polozov

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.

Code Generation Program Synthesis +1

Structured Neural Summarization

3 code implementations ICLR 2019 Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt

Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input.

Source Code Summarization

Robust Text-to-SQL Generation with Execution-Guided Decoding

1 code implementation9 Jul 2018 Chenglong Wang, Kedar Tatwawadi, Marc Brockschmidt, Po-Sen Huang, Yi Mao, Oleksandr Polozov, Rishabh Singh

We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries.

Semantic Parsing Text-To-SQL

Generative Code Modeling with Graphs

1 code implementation ICLR 2019 Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov

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.

Structured Prediction

Pointing Out SQL Queries From Text

no code implementations ICLR 2018 Chenglong Wang, Marc Brockschmidt, Rishabh Singh

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

2 code implementations ICLR 2018 Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi

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

no code implementations22 May 2017 Miltiadis Allamanis, Marc Brockschmidt

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.

Machine Translation Program Repair +2

Differentiable Functional Program Interpreters

1 code implementation7 Nov 2016 John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow

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.

Program Synthesis

Differentiable Programs with Neural Libraries

no code implementations ICML 2017 Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow

We develop a framework for combining differentiable programming languages with neural networks.

DeepCoder: Learning to Write Programs

3 code implementations7 Nov 2016 Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow

We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning.

Enumerative Search

TerpreT: A Probabilistic Programming Language for Program Induction

no code implementations15 Aug 2016 Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

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).

BIG-bench Machine Learning Probabilistic Programming +2

Gated Graph Sequence Neural Networks

13 code implementations17 Nov 2015 Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel

Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.

Drug Discovery Graph Classification +2

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