Search Results for author: Paul Michel

Found 22 papers, 14 papers with code

Distributionally Robust Models with Parametric Likelihood Ratios

1 code implementation ICLR 2022 Paul Michel, Tatsunori Hashimoto, Graham Neubig

As machine learning models are deployed ever more broadly, it becomes increasingly important that they are not only able to perform well on their training distribution, but also yield accurate predictions when confronted with distribution shift.

Text Classification

Balancing Average and Worst-case Accuracy in Multitask Learning

no code implementations12 Oct 2021 Paul Michel, Sebastian Ruder, Dani Yogatama

When training and evaluating machine learning models on a large number of tasks, it is important to not only look at average task accuracy -- which may be biased by easy or redundant tasks -- but also worst-case accuracy (i. e. the performance on the task with the lowest accuracy).

Image Classification Language Modelling

Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative

1 code implementation ICLR 2022 Lucio M. Dery, Paul Michel, Ameet Talwalkar, Graham Neubig

In most settings of practical concern, machine learning practitioners know in advance what end-task they wish to boost with auxiliary tasks.

Meta-Learning

Examining and Combating Spurious Features under Distribution Shift

1 code implementation14 Jun 2021 Chunting Zhou, Xuezhe Ma, Paul Michel, Graham Neubig

Group distributionally robust optimization (DRO) provides an effective tool to alleviate covariate shift by minimizing the worst-case training loss over a set of pre-defined groups.

Modeling the Second Player in Distributionally Robust Optimization

1 code implementation ICLR 2021 Paul Michel, Tatsunori Hashimoto, Graham Neubig

Distributionally robust optimization (DRO) provides a framework for training machine learning models that are able to perform well on a collection of related data distributions (the "uncertainty set").

Model Selection

Weight Poisoning Attacks on Pre-trained Models

1 code implementation14 Apr 2020 Keita Kurita, Paul Michel, Graham Neubig

We show that by applying a regularization method, which we call RIPPLe, and an initialization procedure, which we call Embedding Surgery, such attacks are possible even with limited knowledge of the dataset and fine-tuning procedure.

Sentiment Analysis Spam detection

Optimizing Data Usage via Differentiable Rewards

1 code implementation ICML 2020 Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime Carbonell, Graham Neubig

To acquire a new skill, humans learn better and faster if a tutor, based on their current knowledge level, informs them of how much attention they should pay to particular content or practice problems.

Image Classification Machine Translation

Regularizing Trajectories to Mitigate Catastrophic Forgetting

no code implementations25 Sep 2019 Paul Michel, Elisabeth Salesky, Graham Neubig

Regularization-based continual learning approaches generally prevent catastrophic forgetting by augmenting the training loss with an auxiliary objective.

Continual Learning

Are Sixteen Heads Really Better than One?

3 code implementations NeurIPS 2019 Paul Michel, Omer Levy, Graham Neubig

Attention is a powerful and ubiquitous mechanism for allowing neural models to focus on particular salient pieces of information by taking their weighted average when making predictions.

On Meaning-Preserving Adversarial Perturbations for Sequence-to-Sequence Models

no code implementations ICLR 2019 Paul Michel, Graham Neubig, Xi-An Li, Juan Miguel Pino

Adversarial examples have been shown to be an effective way of assessing the robustness of neural sequence-to-sequence (seq2seq) models, by applying perturbations to the input of a model leading to large degradation in performance.

Adversarial Robustness Machine Translation +1

compare-mt: A Tool for Holistic Comparison of Language Generation Systems

2 code implementations NAACL 2019 Graham Neubig, Zi-Yi Dou, Junjie Hu, Paul Michel, Danish Pruthi, Xinyi Wang, John Wieting

In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation.

Machine Translation Text Generation +1

On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models

1 code implementation NAACL 2019 Paul Michel, Xi-An Li, Graham Neubig, Juan Miguel Pino

Adversarial examples --- perturbations to the input of a model that elicit large changes in the output --- have been shown to be an effective way of assessing the robustness of sequence-to-sequence (seq2seq) models.

Adversarial Robustness Machine Translation

MTNT: A Testbed for Machine Translation of Noisy Text

2 code implementations EMNLP 2018 Paul Michel, Graham Neubig

In this paper, we propose a benchmark dataset for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on Reddit (www. reddit. com) and professionally sourced translations.

Machine Translation Translation

Extreme Adaptation for Personalized Neural Machine Translation

1 code implementation ACL 2018 Paul Michel, Graham Neubig

Every person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin.

Machine Translation Translation

DyNet: The Dynamic Neural Network Toolkit

4 code implementations15 Jan 2017 Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, Pengcheng Yin

In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.

graph construction

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