Publications

Toward Skills Dialog Orchestration with Online Learning
Djallel Bouneffouf
Raphael Feraud
Sohini Upadhyay
Mayank Agarwal
Yasaman Khazaeni
Irina Rish
Building multi-domain AI agents is a challenging task and an open problem in the area of AI. Within the domain of dialog, the ability to orc… (see more)hestrate multiple independently trained dialog agents, or skills, to create a unified system is of particular significance. In this work, we study the task of online posterior dialog orchestration, where we define posterior orchestration as the task of selecting a subset of skills which most appropriately answer a user input using features extracted from both the user input and the individual skills. To account for the various costs associated with extracting skill features, we consider online posterior orchestration under a skill execution budget. We formalize this setting as Context Attentive Bandit with Observations (CABO), a variant of context attentive bandits, and evaluate it on proprietary conversational datasets.
2021-06-05
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)
doi.org
Multimodal dynamics modeling for off-road autonomous vehicles
Jean-François Tremblay
Travis Manderson
Aurélio Noca
Gregory Dudek
David Meger
Dynamics modeling in outdoor and unstructured environments is difficult because different elements in the environment interact with the robo… (see more)t in ways that can be hard to predict. Leveraging multiple sensors to perceive maximal information about the robot's environment is thus crucial when building a model to perform predictions about the robot's dynamics with the goal of doing motion planning. We design a model capable of long-horizon motion predictions, leveraging vision, lidar and proprioception, which is robust to arbitrarily missing modalities at test time. We demonstrate in simulation that our model is able to leverage vision to predict traction changes. We then test our model using a real-world challenging dataset of a robot navigating through a forest, performing predictions in trajectories unseen during training. We try different modality combinations at test time and show that, while our model performs best when all modalities are present, it is still able to perform better than the baseline even when receiving only raw vision input and no proprioception, as well as when only receiving proprioception. Overall, our study demonstrates the importance of leveraging multiple sensors when doing dynamics modeling in outdoor conditions.
2021-06-04
2021 IEEE International Conference on Robotics and Automation (ICRA) (published)
doi.org
arxiv.org
Encoder-Decoder Neural Architecture Optimization for Keyword Spotting
Tong Mo
Bang Liu
2021-06-03
ArXiv (preprint)
arxiv.org
arxiv.org
Hierarchical Video Generation for Complex Data
Lluis Castrejon
Nicolas Ballas
Aaron Courville
2021-06-03
ArXiv (preprint)
arxiv.org
arxiv.org
SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization
Soroosh Shahtalebi
Jean-christophe Gagnon-audet
Touraj Laleh
Mojtaba Faramarzi
Kartik Ahuja
Irina Rish
A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data d… (see more)istribution is not i.i.d to the training domains. This failure often stems from learning non-generalizable features in the training domains that are spuriously correlated with the label of data. To address this shortcoming, there has been a growing surge of interest in learning good explanations that are hard to vary, which is studied under the notion of Out-of-Distribution (OOD) Generalization. The search for good explanations that are \textit{invariant} across different domains can be seen as finding local (global) minimas in the loss landscape that hold true across all of the training domains. In this paper, we propose a masking strategy, which determines a continuous weight based on the agreement of gradients that flow in each edge of network, in order to control the amount of update received by the edge in each step of optimization. Particularly, our proposed technique referred to as"Smoothed-AND (SAND)-masking", not only validates the agreement in the direction of gradients but also promotes the agreement among their magnitudes to further ensure the discovery of invariances across training domains. SAND-mask is validated over the Domainbed benchmark for domain generalization and significantly improves the state-of-the-art accuracy on the Colored MNIST dataset while providing competitive results on other domain generalization datasets.
2021-06-03
ArXiv (preprint)
arxiv.org
arxiv.org
Continual Learning in Deep Networks: an Analysis of the Last Layer
Timothee LESORT
Thomas George
Irina Rish
We study how different output layers in a deep neural network learn and forget in continual learning settings. The following three factors… (see more) can affect catastrophic forgetting in the output layer: (1) weights modifications, (2) interference, and (3) projection drift. In this paper, our goal is to provide more insights into how changing the output layers may address (1) and (2). Some potential solutions to those issues are proposed and evaluated here in several continual learning scenarios. We show that the best-performing type of the output layer depends on the data distribution drifts and/or the amount of data available. In particular, in some cases where a standard linear layer would fail, it turns out that changing parameterization is sufficient in order to achieve a significantly better performance, whithout introducing a continual-learning algorithm and instead using the standard SGD to train a model. Our analysis and results shed light on the dynamics of the output layer in continual learning scenarios, and suggest a way of selecting the best type of output layer for a given scenario.
2021-06-02
ArXiv (preprint)
openreview.net
openreview.net
Enquire One’s Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion
Suyuchen Wang
Ruihui Zhao
X. T. Chen
Yefeng Zheng
Bang Liu
Taxonomy is a hierarchically structured knowledge graph that plays a crucial role in machine intelligence. The taxonomy expansion task aims … (see more)to find a position for a new term in an existing taxonomy to capture the emerging knowledge in the world and keep the taxonomy dynamically updated. Previous taxonomy expansion solutions neglect valuable information brought by the hierarchical structure and evaluate the correctness of merely an added edge, which downgrade the problem to node-pair scoring or mini-path classification. In this paper, we propose the Hierarchy Expansion Framework (HEF), which fully exploits the hierarchical structure’s properties to maximize the coherence of expanded taxonomy. HEF makes use of taxonomy’s hierarchical structure in multiple aspects: i) HEF utilizes subtrees containing most relevant nodes as self-supervision data for a complete comparison of parental and sibling relations; ii) HEF adopts a coherence modeling module to evaluate the coherence of a taxonomy’s subtree by integrating hypernymy relation detection and several tree-exclusive features; iii) HEF introduces the Fitting Score for position selection, which explicitly evaluates both path and level selections and takes full advantage of parental relations to interchange information for disambiguation and self-correction. Extensive experiments show that by better exploiting the hierarchical structure and optimizing taxonomy’s coherence, HEF vastly surpasses the prior state-of-the-art on three benchmark datasets by an average improvement of 46.7% in accuracy and 32.3% in mean reciprocal rank.
2021-06-02
Proceedings of the Web Conference 2021 (published)
doi.org
arxiv.org
Brainhack: Developing a culture of open, inclusive, community-driven neuroscience
Rémi Gau
Stephanie Noble
Katja Heuer
Katherine L. Bottenhorn
Isil Poyraz Bilgin
Yufang Yang
Julia M. Huntenburg
Johanna Bayer
Richard A. I. Bethlehem
Shawn A Rhoads
Christoph Vogelbacher
Valentina Borghesani
Elizabeth Levitis
Hao-Ting Wang
Sofie Van Den Bossche
Xenia Kobeleva
Jon Haitz Legarreta
Samuel Guay
Melvin Selim Atay
Gael P. Varoquaux … (see 80 more)
Dorien Huijser
Malin Sandström
Peer Herholz
Samuel A. Nastase
AmanPreet Badhwar
Guillaume Dumas
Simon Schwab
Stefano Moia
Michael Dayan
Yasmine Bassil
Paula P. Brooks
Matteo Mancini
James M. Shine
David O’Connor
Xihe Xie
Davide Poggiali
Patrick Friedrich
Anibal Sólon Heinsfeld
Lydia Riedl
Roberto Toro
César Caballero‐Gaudes
Anders Eklund
Kelly Garner
Christopher Nolan
Damion V. Demeter
Fernando A. Barrios
Junaid S. Merchant
Elizabeth A. McDevitt
Robert Oostenveld
R. Cameron Craddock
Ariel Rokem
Andrew Doyle
Satrajit Ghosh
Aki Nikolaidis
Olivia W. Stanley
Eneko Uruñuela
Nasim Anousheh
Aurina Arnatkevičiūtė
Guillaume Auzias
Dipankar Bachar
Élise Bannier
Ruggero Basanisi
Arshitha Basavaraj
Marco Bedini
Pierre Bellec
R. Austin Benn
Kathryn Berluti
Steffen Bollmann
Saskia Bollmann
Claire Bradley
Jesse A. Brown
Augusto Buchweitz
Patrick Callahan
Micaela Y. Chan
Bramsh Q. Chandio
Theresa W Cheng
Sidhant Chopra
Ai Wern Chung
Thomas Close
Etienne Combrisson
Giorgia Cona
R. Todd Constable
Claire Cury
Kamalaker Dadi
Pablo F. Damasceno
Samir Das
Fabrizio De Vico Fallani
Krista DeStasio
Erin W. Dickie
Lena Dorfschmidt
Eugene Duff
Elizabeth DuPré
Sarah L. Dziura
Nathália Bianchini Esper
Oscar Estéban
Shreyas Fadnavis
Guillaume Flandin
Jessica Flannery
John C. Flournoy
Stephanie J. Forkel

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open and inclusive environment. Depa… (see more)rting from the formats of typical scientific workshops, these events are based on grassroots projects and training, and foster open and reproducible scientific practices. We describe here the multifaceted, lasting benefits of Brainhacks for individual participants, particularly early career researchers. We further highlight the unique contributions that Brainhacks can make to the research community, contributing to scientific progress by complementing opportunities available in conventional formats.

2021-05-31
Neuron (published)
doi.org
Enabling Technologies for Energy Cloud
Thar Intisar Baker
Zehua Guo
Ali Ismail Ali Awad
Shangguang Wang
Benjamin C. M. Fung
2021-05-31
Journal of Parallel and Distributed Computing (published)
doi.org
ER-AE: Differentially Private Text Generation for Authorship Anonymization
Haohan Bo
Steven H. H. Ding
Benjamin C. M. Fung
Farkhund Iqbal
Most of privacy protection studies for textual data focus on removing explicit sensitive identifiers. However, personal writing style, as a … (see more)strong indicator of the authorship, is often neglected. Recent studies, such as SynTF, have shown promising results on privacy-preserving text mining. However, their anonymization algorithm can only output numeric term vectors which are difficult for the recipients to interpret. We propose a novel text generation model with a two-set exponential mechanism for authorship anonymization. By augmenting the semantic information through a REINFORCE training reward function, the model can generate differentially private text that has a close semantic and similar grammatical structure to the original text while removing personal traits of the writing style. It does not assume any conditioned labels or paralleled text data for training. We evaluate the performance of the proposed model on the real-life peer reviews dataset and the Yelp review dataset. The result suggests that our model outperforms the state-of-the-art on semantic preservation, authorship obfuscation, and stylometric transformation.
2021-05-31
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (published)
doi.org
arxiv.org
Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle
Yikang Shen
Shawn Tan
Alessandro Sordoni
Siva Reddy
Aaron Courville
Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization proble… (see more)ms and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with an incremental parser and maintains the conditional probability setting of a standard language model (left-to-right). To train the incremental parser and avoid exposure bias, we also propose a novel dynamic oracle, so that SOM is more robust to wrong parsing decisions. Experiments show that SOM can achieve strong results in language modeling, incremental parsing and syntactic generalization tests, while using fewer parameters than other models.
2021-05-31
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (published)
doi.org
arxiv.org
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management
Zhengxu Hou
Bang Liu
Ruihui Zhao
Zijing Ou
Yafei Liu
Xi Chen 0003
X. T. Chen
Yefeng Zheng
For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency a… (see more)nd slow convergence speed due to the sparse rewards in RL. To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of state-action pairs in real dialogs. In this paper, we propose a multi-level reward modeling approach that factorizes a reward into a three-level hierarchy: domain, act, and slot. Based on inverse adversarial reinforcement learning, our designed reward model can provide more accurate and explainable reward signals for state-action pairs. Extensive evaluations show that our approach can be applied to a wide range of reinforcement learning-based dialog systems and significantly improves both the performance and the speed of convergence.
2021-05-31
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (published)
doi.org
arxiv.org