Publications

Words Aren’t Enough, Their Order Matters: On the Robustness of Grounding Visual Referring Expressions
Arjun Reddy Akula
Spandana Gella
Yaser Al-Onaizan
Song-Chun Zhu
Siva Reddy
Visual referring expression recognition is a challenging task that requires natural language understanding in the context of an image. We cr… (voir plus)itically examine RefCOCOg, a standard benchmark for this task, using a human study and show that 83.7% of test instances do not require reasoning on linguistic structure, i.e., words are enough to identify the target object, the word order doesn’t matter. To measure the true progress of existing models, we split the test set into two sets, one which requires reasoning on linguistic structure and the other which doesn’t. Additionally, we create an out-of-distribution dataset Ref-Adv by asking crowdworkers to perturb in-domain examples such that the target object changes. Using these datasets, we empirically show that existing methods fail to exploit linguistic structure and are 12% to 23% lower in performance than the established progress for this task. We also propose two methods, one based on contrastive learning and the other based on multi-task learning, to increase the robustness of ViLBERT, the current state-of-the-art model for this task. Our datasets are publicly available at https://github.com/aws/aws-refcocog-adv.
2020-06-30
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences
Yi Tay
Donovan Ong
Jie Fu
Alvin Chan
Nancy Chen
Anh Tuan Luu
Christopher Pal
2020-06-30
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (publié)
doi.org
Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems
Anirudh Goyal
Alex Lamb
Phanideep Gampa
Philippe Beaudoin
Sergey Levine
Charles Blundell
Yoshua Bengio
Michael Curtis Mozer
2020-06-28
ArXiv (prépublication)
arxiv.org
arxiv.org
Inherent privacy limitations of decentralized contact tracing apps
Yoshua Bengio
Daphne Ippolito
Richard Janda
Max Jarvie
Benjamin Prud'homme
Jean-François Rousseau
Abhinav Sharma
Yun William Yu
2020-06-24
J. Am. Medical Informatics Assoc. (publié)
doi.org
Shared and unique brain network features predict cognition, personality and mental health in childhood
Jianzhong Chen
Angela Tam
Valeria Kebets
Csaba Orban
Leon Qi Rong Ooi
Leon Qi Rong Ooi
Scott Marek
Nico Dosenbach
Simon Eickhoff
Danilo Bzdok
Avram J Holmes
B.T. Thomas Yeo
The manner through which individual differences in brain network organization track population-level behavioral variability is a fundamental… (voir plus) question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, the focus of most studies on single behavioral traits has come at the expense of capturing broader relationships across behaviors. Here, we utilized a large-scale dataset of 1858 typically developing children to estimate whole-brain functional network organization that is predictive of individual differences in cognition, impulsivity-related personality, and mental health during rest and task states. Predictive network features were distinct across the broad behavioral domains: cognition, personality and mental health. On the other hand, traits within each behavioral domain were predicted by highly similar network features. This is surprising given decades of research emphasizing that distinct brain networks support different mental processes. Although tasks are known to modulate the functional connectome, we found that predictive network features were similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood, yet are unique to different behavioral domains.
2020-06-23
bioRxiv (prépublication)
doi.org
Image-to-image Mapping with Many Domains by Sparse Attribute Transfer
Matthew Amodio
Rim Assouel
Victor Schmidt
Tristan Sylvain
Smita Krishnaswamy
Yoshua Bengio
2020-06-22
ArXiv (prépublication)
arxiv.org
arxiv.org
Rethinking Distributional Matching Based Domain Adaptation
Bin Li
Yezhen Wang
Tong Che
Shanghang Zhang
Sicheng Zhao
Pengfei Xu
Wenzhen Zhou
Yoshua Bengio
Kurt W. Keutzer
Domain adaptation (DA) is a technique that transfers predictive models trained on a labeled source domain to an unlabeled target domain, wit… (voir plus)h the core difficulty of resolving distributional shift between domains. Currently, most popular DA algorithms are based on distributional matching (DM). However in practice, realistic domain shifts (RDS) may violate their basic assumptions and as a result these methods will fail. In this paper, in order to devise robust DA algorithms, we first systematically analyze the limitations of DM based methods, and then build new benchmarks with more realistic domain shifts to evaluate the well-accepted DM methods. We further propose InstaPBM, a novel Instance-based Predictive Behavior Matching method for robust DA. Extensive experiments on both conventional and RDS benchmarks demonstrate both the limitations of DM methods and the efficacy of InstaPBM: Compared with the best baselines, InstaPBM improves the classification accuracy respectively by
2020-06-22
ArXiv (prépublication)
arxiv.org
arxiv.org
HNHN: Hypergraph Networks with Hyperedge Neurons
Yihe Dong
W. Sawin
Yoshua Bengio
2020-06-21
ArXiv (prépublication)
arxiv.org
arxiv.org
Learning to Prove from Synthetic Theorems
Eser Aygün
Zafarali Ahmed
Ankit Anand
Vlad Firoiu
Xavier Glorot
Laurent Orseau
Doina Precup
Shibl Mourad
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in t… (voir plus)raining successful deep learning models. To tackle this problem, we propose an approach that relies on training with synthetic theorems, generated from a set of axioms. We show that such theorems can be used to train an automated prover and that the learned prover transfers successfully to human-generated theorems. We demonstrate that a prover trained exclusively on synthetic theorems can solve a substantial fraction of problems in TPTP, a benchmark dataset that is used to compare state-of-the-art heuristic provers. Our approach outperforms a model trained on human-generated problems in most axiom sets, thereby showing the promise of using synthetic data for this task.
2020-06-18
ArXiv (prépublication)
arxiv.org
arxiv.org
Individual differences in interpersonal coordination
Julia Ayache
Guillaume Dumas
A. Sumich
D. Kuss
Darren Rhodes
Nadja Heym
2020-06-17
(publié)
doi.org
Special Issue on Novel Informatics Approaches to COVID-19 Research
Huanan Xu
David L Buckeridge
Fei Wang Guest Editors
2020-06-16
Journal of Biomedical Informatics (publié)
doi.org
Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks
Ghouthi Boukli hacene
Vincent Gripon
Matthieu Arzel
Nicolas Farrugia
Yoshua Bengio
Deep Neural Networks (DNNs) in general and Convolutional Neural Networks (CNNs) in particular are state-of-the-art in numerous computer visi… (voir plus)on tasks such as object classification and detection. However, the large amount of parameters they contain leads to a high computational complexity and strongly limits their usability in budget-constrained devices such as embedded devices. In this paper, we propose a combination of a pruning technique and a quantization scheme that effectively reduce the complexity and memory usage of convolutional layers of CNNs, by replacing the complex convolutional operation by a low-cost multiplexer. We perform experiments on CIFAR10, CIFAR100 and SVHN datasets and show that the proposed method achieves almost state-of-the-art accuracy, while drastically reducing the computational and memory footprints compared to the baselines. We also propose an efficient hardware architecture, implemented on Field Programmable Gate Arrays (FPGAs), to accelerate inference, which works as a pipeline and accommodates multiple layers working at the same time to speed up the inference process. In contrast with most proposed approaches which have used external memory or software defined memory controllers, our work is based on algorithmic optimization and full-hardware design, enabling a direct, on-chip memory implementation of a DNN while keeping close to state of the art accuracy.
2020-06-15
2020 18th IEEE International New Circuits and Systems Conference (NEWCAS) (publié)
doi.org