Publications

The generalizability of pre-processing techniques on the accuracy and fairness of data-driven building models: a case study
Ying Sun
Benjamin C. M. Fung
Fariborz Haghighat
2022-04-30
Energy and Buildings (published)
doi.org
The Power of Prompt Tuning for Low-Resource Semantic Parsing
Nathan Schucher
Siva Reddy
Harm de Vries
Prompt tuning has recently emerged as an effective method for adapting pre-trained language models to a number of language understanding and… (see more) generation tasks. In this paper, we investigate prompt tuning for semantic parsing—the task of mapping natural language utterances onto formal meaning representations. On the low-resource splits of Overnight and TOPv2, we find that a prompt tuned T5-xl significantly outperforms its fine-tuned counterpart, as well as strong GPT-3 and BART baselines. We also conduct ablation studies across different model scales and target representations, finding that, with increasing model scale, prompt tuned T5 models improve at generating target representations that are far from the pre-training distribution.
2022-04-30
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (published)
doi.org
arxiv.org
Unsupervised Dependency Graph Network
Yikang Shen
Shawn Tan
Alessandro Sordoni
Peng Li
Jie Zhou
Aaron Courville
2022-04-30
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)
doi.org
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
Zichao Li
Prakhar Sharma
Xing Han Lu
Jackie CK Cheung
Siva Reddy
2022-04-30
Findings of the Association for Computational Linguistics: ACL 2022 (published)
doi.org
arxiv.org
Using Population Datasets to Identify the Brain Basis of Social Isolation
Danilo Bzdok
2022-04-30
Biological Psychiatry (published)
doi.org
Vision-Language Pretraining: Current Trends and the Future
Aishwarya Agrawal
Damien Teney
Aida Nematzadeh
In the last few years, there has been an increased interest in building multimodal (vision-language) models that are pretrained on larger bu… (see more)t noisier datasets where the two modalities (e.g., image and text) loosely correspond to each other (e.g., Lu et al., 2019; Radford et al., 2021). Given a task (such as visual question answering), these models are then often fine-tuned on task-specific supervised datasets. (e.g., Lu et al., 2019; Chen et al.,2020; Tan and Bansal, 2019; Li et al., 2020a,b). In addition to the larger pretraining datasets, the transformer architecture (Vaswani et al., 2017) and in particular self-attention applied to two modalities are responsible for the impressive performance of the recent pretrained models on downstream tasks (Hendricks et al., 2021). In this tutorial, we focus on recent vision-language pretraining paradigms. Our goal is to first provide the background on image–language datasets, benchmarks, and modeling innovations before the multimodal pretraining area. Next we discuss the different family of models used for vision-language pretraining, highlighting their strengths and shortcomings. Finally, we discuss the limits of vision-language pretraining through statistical learning, and the need for alternative approaches such as causal representation learning.
2022-04-30
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts (published)
doi.org
Public Perspectives on Exposure Notification Apps: A Patient and Citizen Co-Designed Study
Esli Osmanlliu
Jesseca Paquette
Maria Alejandra Rodriguez Duarte
Sylvain Bédard
Nathalie de Marcellis-Warin
Majlinda Zhegu
Catherine Régis
Marie-Eve Bouthillier
Annie-Danielle Grenier
Paul Lewis
Marie-Pascale Pomey
Canada deployed a digital exposure notification app (COVID Alert) as a strategy to support manual contact tracing. Our aims are to (1) asses… (see more)s the use, knowledge, and concerns of the COVID Alert app, (2) identify predictors of app downloads, and (3) develop strategies to promote social acceptability. A 36-item questionnaire was co-designed by 12 citizens and patients partnered with 16 academic researchers and was distributed in the province of Québec, Canada, from May 27 to 28 June 2021. Of 959 respondents, 43% had downloaded the app. Messaging from government sources constituted the largest influence on app download. Infrequent social contacts and perceived app inefficacy were the main reasons not to download the app. Cybersecurity, data confidentiality, loss of privacy, and geolocation were the most frequent concerns. Nearly half of the respondents inaccurately believed that the app used geolocation. Most respondents supported citizen involvement in app development. The identified predictors for app uptake included nine characteristics. In conclusion, this project highlights four key themes on how to promote the social acceptability of such tools: (1) improved communication and explanation of key app characteristics, (2) design features that incentivize adoption, (3) inclusive socio-technical features, and (4) upstream public partnership in development and deployment.
2022-04-29
Journal of Personalized Medicine (published)
doi.org
GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network
Mohammad Amin Mozaffari
Xinyuan Zhang
Jinghui Cheng
Jin L.C. Guo
Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and technique… (see more)s that support inspiration usually only focus on example browsing with limited user control or similarity-based example retrieval, leading to undesirable design outcomes such as focus drift and design fixation. To address these issues, we propose the GANSpiration approach that suggests design examples for both targeted and serendipitous inspiration, leveraging a style-based Generative Adversarial Network. A quantitative evaluation revealed that the outputs of GANSpiration-based example suggestion approaches are relevant to the input design, and at the same time include diverse instances. A user study with professional UI/UX practitioners showed that the examples suggested by our approach serve as viable sources of inspiration for overall design concepts and specific design elements. Overall, our work paves the road of using advanced generative machine learning techniques in supporting the creative design practice.
2022-04-28
CHI Conference on Human Factors in Computing Systems (published)
doi.org
arxiv.org
Determinants of technology adoption and continued use among cognitively impaired older adults: a qualitative study
Samantha Dequanter
Maaike Fobelets
Iris Steenhout
Marie-Pierre Gagnon
Anne Bourbonnais
S. A. Rahimi
Ronald Buyl
Ellen Gorus
2022-04-27
BMC Geriatrics (published)
doi.org
Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma
Elizabeth George
Elizabeth Flagg
Kuan-chun Chang
Hai-Yang Bai
H. Aerts
Martin Vallières
David A. Reardon
R.Y. Huang
BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of t… (see more)reatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.
2022-04-27
American Journal of Neuroradiology (published)
doi.org
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
Luke Y. Prince
Roy Henha Eyono
Ellen Boven
Arna Ghosh
Joe Pemberton
Franz Scherr
Claudia Clopath
Rui Ponte Costa
Wolfgang Maass
Blake A. Richards
Cristina Savin
Katharina Anna Wilmes
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them… (see more) with the efficiency of gradient-based learning in recurrent neural networks. The key issues discussed in this review include: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. We conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help bring clarity to these issues.
2022-04-26
Neurons, Behavior, Data Analysis, and Theory (published)
doi.org
arxiv.org
Don't Freeze Your Embedding: Lessons from Policy Finetuning in Environment Transfer
Victoria Dean
Daniel Toyama
Doina Precup
A common occurrence in reinforcement learning (RL) research is making use of a pretrained vision stack that converts image observations to l… (see more)atent vectors. Using a visual embedding in this way leaves open questions, though: should the vision stack be updated with the policy? In this work, we evaluate the effectiveness of such decisions in RL transfer settings. We introduce policy update formulations for use after pretraining in a different environment and analyze the performance of such formulations. Through this evaluation, we also detail emergent metrics of benchmark suites and present results on Atari and AndroidEnv.
2022-04-26
ICLR.cc/2022/Workshop/GPL (poster)
openreview.net
openreview.net