Publications

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
Sergey Bartunov
Adam Santoro
Blake Richards
Luke Marris
Geoffrey Hinton
Timothy P. Lillicrap
The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learn… (voir plus)ing and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks. Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance. We present results on the MNIST, CIFAR-10, and ImageNet datasets and explore variants of target-propagation (TP) and feedback alignment (FA) algorithms, and explore performance in both fully- and locally-connected architectures. We also introduce weight-transport-free variants of difference target propagation (DTP) modified to remove backpropagation from the penultimate layer. Many of these algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP and FA variants perform significantly worse than BP, especially for networks composed of locally connected units, opening questions about whether new architectures and algorithms are required to scale these approaches. Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.
openreview.net
RE-EVALUATE: Reproducibility in Evaluating Reinforcement Learning Algorithms
Khimya Khetarpal
Zafarali Ahmed
Andre Cianflone
Riashat Islam
Joelle Pineau
Reinforcement learning (RL) has recently achieved tremendous success in solving complex tasks. Careful considerations are made towards repro… (voir plus)ducible research in machine learning. Reproducibility in RL often becomes more difficult, due to the lack of standard evaluation method and detailed methodology for algorithms and comparisons with existing work. In this work, we highlight key differences in evaluation in RL compared to supervised learning, and discuss specific issues that are often non-intuitive for newcomers. We study the importance of reproducibility in evaluation in RL, and propose an evaluation pipeline that can be decoupled from the algorithm code. We hope such an evaluation pipeline can be standardized, as a step towards robust and reproducible research in RL.
2018-06-27
ICML.cc/2018/RML (poster)
openreview.net
openreview.net
Diffusion Models
Saeid Naderiparizi
painting of an artificial intelligent agent
2018-06-05
Financial Econometrics (publié)
doi.org
Coarse Lexical Frame Acquisition at the Syntax–Semantics Interface Using a Latent-Variable PCFG Model
Laura Kallmeyer
Behrang QasemiZadeh
Jackie Cheung
We present a method for unsupervised lexical frame acquisition at the syntax–semantics interface. Given a set of input strings derived fro… (voir plus)m dependency parses, our method generates a set of clusters that resemble lexical frame structures. Our work is motivated not only by its practical applications (e.g., to build, or expand the coverage of lexical frame databases), but also to gain linguistic insight into frame structures with respect to lexical distributions in relation to grammatical structures. We model our task using a hierarchical Bayesian network and employ tools and methods from latent variable probabilistic context free grammars (L-PCFGs) for statistical inference and parameter fitting, for which we propose a new split and merge procedure. We show that our model outperforms several baselines on a portion of the Wall Street Journal sentences that we have newly annotated for evaluation purposes.
2018-06-01
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics (publié)
doi.org
Commonsense mining as knowledge base completion? A study on the impact of novelty
Stanisław Jastrzębski
Dzmitry Bahdanau
Seyedarian Hosseini
Michael Noukhovitch
Yoshua Bengio
Jackie Cheung
Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by recent work by Li et al., … (voir plus)we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method that outperforms the previous state of the art on predicting more novel triples.
2018-06-01
Proceedings of the Workshop on Generalization in the Age of Deep Learning (publié)
doi.org
arxiv.org
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge
Ali Emami
Adam Trischler
Kaheer Suleman
Jackie Cheung
We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice … (voir plus)of Plausible Alternatives (COPA). Problem instances from these tasks require diverse, complex forms of inference and knowledge to solve. Our method uses a knowledge-hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine. It extracts and classifies knowledge from the returned results and weighs it to make a resolution. Our approach improves F1 performance on the WSC by 0.16 over the previous best and is competitive with the state-of-the-art on COPA, demonstrating its general applicability.
2018-06-01
North American Chapter of the Association for Computational Linguistics (publié)
doi.org
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization
Kian Kenyon-Dean
Jackie Cheung
Doina Precup
We present an approach to event coreference resolution by developing a general framework for clustering that uses supervised representation … (voir plus)learning. We propose a neural network architecture with novel Clustering-Oriented Regularization (CORE) terms in the objective function. These terms encourage the model to create embeddings of event mentions that are amenable to clustering. We then use agglomerative clustering on these embeddings to build event coreference chains. For both within- and cross-document coreference on the ECB+ corpus, our model obtains better results than models that require significantly more pre-annotated information. This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.
2018-06-01
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics (publié)
doi.org
arxiv.org
Traceability in the Wild: Automatically Augmenting Incomplete Trace Links
Michael Rath
Jacob Rendall
Jin Guo
Jane Cleland-Huang
Patrick Mäder
Software and systems traceability is widely accepted as an essential element for supporting many software development tasks. Today's version… (voir plus) control systems provide inbuilt features that allow developers to tag each commit with one or more issue ID, thereby providing the building blocks from which project-wide traceability can be established between feature requests, bug fixes, commits, source code, and specific developers. However, our analysis of six open source projects showed that on average only 60% of the commits were linked to specific issues. Without these fundamental links the entire set of project-wide links will be incomplete, and therefore not trustworthy. In this paper we address the fundamental problem of missing links between commits and issues. Our approach leverages a combination of process and text-related features characterizing issues and code changes to train a classifier to identify missing issue tags in commit messages, thereby generating the missing links. We conducted a series of experiments to evaluate our approach against six open source projects and showed that it was able to effectively recommend links for tagging issues at an average of 96% recall and 33% precision. In a related task for augmenting a set of existing trace links, the classifier returned precision at levels greater than 89% in all projects and recall of 50%.
2018-05-27
Proceedings of the 40th International Conference on Software Engineering (publié)
doi.org
arxiv.org
How Do the Open Source Communities Address Usability and UX Issues?: An Exploratory Study
Jinghui Cheng
Jin Guo
Usability and user experience (UX) issues are often not well emphasized and addressed in open source software (OSS) development. There is an… (voir plus) imperative need for supporting OSS communities to collaboratively identify, understand, and fix UX design issues in a distributed environment. In this paper, we provide an initial step towards this effort and report on an exploratory study that investigated how the OSS communities currently reported, discussed, negotiated, and eventually addressed usability and UX issues. We conducted in-depth qualitative analysis of selected issue tracking threads from three OSS projects hosted on GitHub. Our findings indicated that discussions about usability and UX issues in OSS communities were largely influenced by the personal opinions and experiences of the participants. Moreover, the characteristics of the community may have greatly affected the focus of such discussion.
2018-04-20
Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (publié)
doi.org
arxiv.org
Minimization of Graph Weighted Models over Circular Strings
Guillaume Rabusseau
2018-04-16
Foundations of Software Science and Computation Structure (publié)
doi.org
Frank-Wolfe Splitting via Augmented Lagrangian Method
Gauthier Gidel
Fabian Pedregosa
Simon Lacoste-Julien
Minimizing a function over an intersection of convex sets is an important task in optimization that is often much more challenging than mini… (voir plus)mizing it over each individual constraint set. While traditional methods such as Frank-Wolfe (FW) or proximal gradient descent assume access to a linear or quadratic oracle on the intersection, splitting techniques take advantage of the structure of each sets, and only require access to the oracle on the individual constraints. In this work, we develop and analyze the Frank-Wolfe Augmented Lagrangian (FW-AL) algorithm, a method for minimizing a smooth function over convex compact sets related by a "linear consistency" constraint that only requires access to a linear minimization oracle over the individual constraints. It is based on the Augmented Lagrangian Method (ALM), also known as Method of Multipliers, but unlike most existing splitting methods, it only requires access to linear (instead of quadratic) minimization oracles. We use recent advances in the analysis of Frank-Wolfe and the alternating direction method of multipliers algorithms to prove a sublinear convergence rate for FW-AL over general convex compact sets and a linear convergence rate for polytopes.
2018-03-31
ArXiv (preprint)
arxiv.org
arxiv.org
Nonlinear Weighted Finite Automata
Tianyu Li
Guillaume Rabusseau
Doina Precup
Weighted finite automata (WFA) can expressively model functions defined over strings but are inherently linear models. Given the recent succ… (voir plus)esses of nonlinear models in machine learning, it is natural to wonder whether extending WFA to the nonlinear setting would be beneficial. In this paper, we propose a novel model of neural network based nonlinear WFA model (NL-WFA) along with a learning algorithm. Our learning algorithm is inspired by the spectral learning algorithm for WFA and relies on a nonlinear decomposition of the so-called Hankel matrix, by means of an auto-encoder network. The expressive power of NL-WFA and the proposed learning algorithm are assessed on both synthetic and real world data, showing that NL-WFA can lead to smaller model sizes and infer complex grammatical structures from data.
2018-03-31
International Conference on Artificial Intelligence and Statistics (published)
dblp.uni-trier.de