Pascal Lamblin
Pascal Lamblin is a software developer working with research teams in Google Brain. After completing an engineering degree at École Centrale Paris, he has done research under the supervision of Yoshua Bengio at Université de Montréal, where he also worked on the development of Theano.
He is working mainly on few-shot learning, and is also interested in automatic differentiation and program transformations.
Research Areas
Authored Publications
Google Publications
Other Publications
Sort By
Resolving Code Review Comments with Machine Learning
Alexander Frömmgen
Peter Choy
Elena Khrapko
Marcus Revaj
2024 IEEE/ACM 46th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (to appear)
Preview abstract
Code reviews are a critical part of the software development process, taking a significant amount of the code authors’ and the code reviewers’ time. As part of this process, the reviewer inspects the proposed code and asks the author for code changes through comments written in natural language. At Google, we see millions of reviewer comments per year, and authors require an average of ∼60 minutes active shepherding time between sending changes for review and finally submitting the change. In our measurements, the required active work time that the code author must devote to address reviewer comments grows almost linearly with the number of comments. However, with machine learning (ML), we have an opportunity to automate and streamline the code-review process, e.g., by proposing code changes based on a comment’s text.
We describe our application of recent advances in large sequence models in a real-world setting to automatically resolve code-review comments in the day-to-day development workflow at Google. We present the evolution of this feature from an asynchronous generation of suggested edits after the reviewer sends feedback, to an interactive experience that suggests code edits to the reviewer at review time. In deployment, code-change authors at Google address 7.5% of all reviewer comments by applying an ML-suggested edit. The impact of this will be to reduce the time spent on code reviews by hundreds of thousands of engineer hours annually at Google scale. Unsolicited, very positive feedback highlights that the impact of ML-suggested code edits increases Googlers’ productivity and allows them to focus on more creative and complex tasks.
View details
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair
Zimin Chen
Vincent J Hellendoorn
Subhodeep Moitra
Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) (2021)
Preview abstract
Machine learning for understanding and editing source code has recently attracted significant interest, with many developments in new models,
new code representations, and new tasks.
This proliferation can appear disparate and disconnected, making each approach seemingly unique and incompatible, thus obscuring the core
machine learning challenges and contributions.
In this work, we demonstrate that the landscape can be significantly simplified by taking a general approach of mapping a graph to a sequence of tokens and pointers.
Our main result is to show that 16 recently published tasks of different shapes can be cast in this form, based on which a single model architecture achieves near or above state-of-the-art results on nearly all tasks, outperforming custom models like code2seq and alternative generic models like Transformers.
This unification further enables multi-task learning and a series of cross-cutting experiments about the importance of different modeling choices for code understanding and repair tasks.
The full framework, called PLUR, is easily extensible to more tasks, and will be open-sourced (link).
View details
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Eleni Triantafillou
Kelvin Xu
Carles Gelada
International Conference on Learning Representations (submission) (2020)
Preview abstract
Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle this recently, we find the current procedure and datasets that are used to systematically assess progress in this setting lacking. To address this, we propose META-DATASET: a new benchmark for training and evaluating few-shot classifiers that is large-scale, consists of multiple datasets, and presents more natural and realistic tasks. The aim is to measure the ability of state-of the-art models to leverage diverse sources of data to achieve higher generalization, and to evaluate that generalization ability in a more challenging setting. We additionally measure robustness of current methods to variations in the number of available examples and the number of classes. Finally our extensive empirical evaluation leads us to identify weaknesses in Prototypical Networks and MAML, two popular few-shot classification methods, and to propose a new method, ProtoMAML, which achieves improved performance on our benchmark.
View details
Preview abstract
We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approaches such as operator overloading (OO) and source transformation (ST) used for AD, graph-based intermediate representations for programs, and source languages. Based on these insights, we introduce a new graph-based intermediate representation (IR) which specifically aims to efficiently support fully-general AD for array programming. Unlike existing dataflow programming representations in ML frameworks, our IR naturally supports function calls, higher-order functions and recursion, making ML models easier to implement. The ability to represent closures allows us to perform AD using ST without a tape, making the resulting derivative (adjoint) program amenable to ahead-of-time optimization using tools from functional language compilers, and enabling higher-order derivatives. Lastly, we introduce a proof of concept compiler toolchain called Myia which uses a subset of Python as a front end.
View details
EmoNets: Multimodal deep learning approaches for emotion recognition in video
Samira Ebrahimi Kahou
Xavier Bouthillier
Caglar Gulcehre
Vincent Michalski
Kishore Konda
Sébastien Jean
Pierre Froumenty
Yann Dauphin
Nicolas Boulanger-Lewandowski
Raul Chandias Ferrari
Mehdi Mirza
David Warde-Farley
Aaron Courville
Pascal Vincent
Roland Memisevic
Christopher Pal
Yoshua Bengio
Journal on Multimodal User Interfaces, vol. 10 (2016), 99–111
Exploring strategies for training deep neural networks
Yoshua Bengio
Jérôme Louradour
Journal of Machine Learning Research, vol. 1 (2009)
Greedy layer-wise training of deep networks
Yoshua Bengio
Dan Popovici
Advances in neural information processing systems 19 (2007)
