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Andre Cianflone

cianfloa@mila.quebec
Doctorat - McGill University
Superviseur⋅e principal⋅e
Jackie Cheung
Github

Publications

Clustering-Oriented Representation Learning with Attractive-Repulsive Loss
Kian Kenyon-Dean
Andre Cianflone
Lucas Caccia
Guillaume Rabusseau
Jackie Cheung
Doina Precup
The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the trai… (voir plus)ning data; building useful representations is not a necessary byproduct of this objective. In this work, we propose clustering-oriented representation learning (COREL) as an alternative to CCE in the context of a generalized attractive-repulsive loss framework. COREL has the consequence of building latent representations that collectively exhibit the quality of natural clustering within the latent space of the final hidden layer, according to a predefined similarity function. Despite being simple to implement, COREL variants outperform or perform equivalently to CCE in a variety of scenarios, including image and news article classification using both feed-forward and convolutional neural networks. Analysis of the latent spaces created with different similarity functions facilitates insights on the different use cases COREL variants can satisfy, where the Cosine-COREL variant makes a consistently clusterable latent space, while Gaussian-COREL consistently obtains better classification accuracy than CCE.
2018-12-18
ArXiv (prépublication)
arxiv.org
arxiv.org
Let’s do it “again”: A First Computational Approach to Detecting Adverbial Presupposition Triggers
Andre Cianflone
Yulan Feng
Jad Kabbara
Jackie Cheung
We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as s… (voir plus)ummarization and dialogue systems. We introduce two new corpora, derived from the Penn Treebank and the Annotated English Gigaword dataset and investigate the use of a novel attention mechanism tailored to this task. Our attention mechanism augments a baseline recurrent neural network without the need for additional trainable parameters, minimizing the added computational cost of our mechanism. We demonstrate that this model statistically outperforms our baselines.
2018-07-01
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
doi.org
arxiv.org
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