Publications

On the Expressivity of Markov Reward (Extended Abstract)
David Abel
Will Dabney
Anna Harutyunyan
Mark K. Ho
Michael L. Littman
Doina Precup
Satinder Singh
2022-07-23
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (publié)
doi.org
Flaky Performances when Pre-Training on Relational Databases with a Plan for Future Characterization Efforts
Shengchao Liu
David Vazquez
Jian Tang
Pierre-Andre Noel
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extra… (voir plus)cted from relational databases (RDBs). Intu-itively, this joint use of SSL and GNNs allows us to leverage more of the available data, which could translate to better results. However, while we observe positive transfer in some cases, others showed systematic performance degradation, including some spectacular ones. We hypothesize a mechanism that could explain this behaviour and draft the plan for future work testing it by characterizing how much relevant information different strategies can (theoretically and/or empirically) extract from (synthetic and/or real) RDBs.
2022-07-22
ICML.cc/2022/Workshop/Pre-Training (accepté)
openreview.net
openreview.net
Revisiting Hotels-50K and Hotel-ID
Aarash Feizi
Arantxa Casanova
Adriana Romero Soriano
Reihaneh Rabbany
In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels-50K and Hotel-ID. The revisited versions prov… (voir plus)ide evaluation setups with different levels of difficulty to better align with the intended real-world application, i.e. countering human trafficking. Real-world scenarios involve hotels and locations that are not captured in the current data sets, therefore it is important to consider evaluation settings where classes are truly unseen. We test this setup using multiple state-of-the-art image retrieval models and show that as expected, the models’ performances decrease as the evaluation gets closer to the real-world unseen settings. The rankings of the best performing models also change across the different evaluation settings, which further motivates using the proposed revisited datasets.
2022-07-20
ArXiv (prépublication)
doi.org
arxiv.org
On the Generalization and Adaption Performance of Causal Models
Nino Scherrer
Anirudh Goyal
Stefan Bauer
Yoshua Bengio
Nan Rosemary Ke
2022-07-20
ICML.cc/2022/Workshop/SCIS (poster)
doi.org
openreview.net
Characterizing User Behaviors in Open-Source Software User Forums: An Empirical Study
Jazlyn Hellman
Jiahao Chen
Md. Sami Uddin
Jinghui Cheng
Jin Guo
User forums of Open Source Software (OSS) enable end-users to collaboratively discuss problems concerning the OSS applications. Despite deca… (voir plus)des of research on OSS, we know very little about how end-users engage with OSS communities on these forums, in particular, the challenges that hinder their continuous and meaningful participation in the OSS community. Many previous works are developer-centric and overlook the importance of end-user forums. As a result, end-users' expectations are seldom reflected in OSS development. To better understand user behaviors in OSS user forums, we carried out an empirical study analyzing about 1.3 million posts from user forums of four popular OSS applications: Zotero, Audacity, VLC, and RStudio. Through analyzing the contribution patterns of three common user types (end-users, developers, and organizers), we observed that end-users not only initiated most of the threads (above 96% of threads in three projects, 86% in the other), but also acted as the significant contributors for responding to other users' posts, even though they tended to lack confidence in their activities as indicated by psycho-linguistic analyses. Moreover, we found end-users more open, reflecting a more positive emotion in communication than organizers and developers in the forums. Our work contributes new knowledge about end-users' activities and behaviors in OSS user forums that the vital OSS stakeholders can leverage to improve end-user engagement in the OSS development process.
2022-07-19
Proceedings of the 15th International Conference on Cooperative and Human Aspects of Software Engineering (publié)
doi.org
arxiv.org
Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training
William Harvey
Michael Teng
Frank N. Wood
Hard visual attention is a promising approach to reduce the computational burden of modern computer vision methodologies. However, hard atte… (voir plus)ntion mechanisms can be difficult and slow to train, which is especially costly for applications like neural architecture search where multiple networks must be trained. We introduce a method to amortise the cost of training by generating an extra supervision signal for a subset of the training data. This supervision is in the form of sequences of ‘good’ locations to attend to for each image. We find that the best method to generate supervision sequences comes from framing hard attention for image classification as a Bayesian optimal experimental design (BOED) problem. From this perspective, the optimal locations to attend to are those which provide the greatest expected reduction in the entropy of the classification distribution. We introduce methodology from the BOED literature to approximate this optimal behaviour and generate ‘near-optimal’ supervision sequences. We then present a hard attention network training objective that makes use of these sequences and show that it allows faster training than prior work. We finally demonstrate the utility of faster hard attention training by incorporating supervision sequences in a neural architecture search, resulting in hard attention architectures which can outperform networks with access to the entire image.
2022-07-18
2022 International Joint Conference on Neural Networks (IJCNN) (publié)
doi.org
arxiv.org
On the Effectiveness of Interpretable Feedforward Neural Network
Miles Q. Li
Benjamin Fung
Adel Abusitta
Deep learning models have achieved state-of-the-art performance in many classification tasks. However, most of them cannot provide an explan… (voir plus)ation for their classification results. Machine learning models that are interpretable are usually linear or piecewise linear and yield inferior performance. Non-linear models achieve much better classification performance, but it is usually hard to explain their classification results. As a counter-example, an interpretable feedforward neural network (IFFNN) is proposed to achieve both high classification performance and interpretability for malware detection. If the IFFNN can perform well in a more flexible and general form for other classification tasks while providing meaningful explanations, it may be of great interest to the applied machine learning community. In this paper, we propose a way to generalize the interpretable feedforward neural network to multi-class classification scenarios and any type of feedforward neural networks, and evaluate its classification performance and interpretability on interpretable datasets. We conclude by finding that the generalized IFFNNs achieve comparable classification performance to their normal feedforward neural network counterparts and provide meaningful explanations. Thus, this kind of neural network architecture has great practical use.
2022-07-18
2022 International Joint Conference on Neural Networks (IJCNN) (publié)
doi.org
arxiv.org
Generative Models of Brain Dynamics
Mahta Ramezanian-Panahi
Germán Abrevaya
Jean-Christophe Gagnon-Audet
Vikram Voleti
Irina Rish
Guillaume Dumas
2022-07-15
Frontiers in Artificial Intelligence (publié)
doi.org
Attention for Inference Compilation
William Harvey
Andreas Munk
Atilim Güneş Baydin
Alexander Bergholm
Frank N. Wood
We present a new approach to automatic amortized inference in universal probabilistic programs which improves performance compared to curren… (voir plus)t methods. Our approach is a variation of inference compilation (IC) which leverages deep neural networks to approximate a posterior distribution over latent variables in a probabilistic program. A challenge with existing IC network architectures is that they can fail to model long-range dependencies between latent variables. To address this, we introduce an attention mechanism that attends to the most salient variables previously sampled in the execution of a probabilistic program. We demonstrate that the addition of attention allows the proposal distributions to better match the true posterior, enhancing inference about latent variables in simulators.
2022-07-14
Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (publié)
doi.org
arxiv.org
Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics
Simon A. Keek
Manon Beuque
Sergey Primakov
Henry C. Woodruff
Avishek Chatterjee
Janita E. van Timmeren
Martin Vallières
Lizza E. L. Hendriks
Johannes Kraft
Nicolaus Andratschke
Steve E. Braunstein
Olivier Morin
Philippe Lambin
Introduction There is a cumulative risk of 20–40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) en… (voir plus)ables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain. It is therefore of clinical interest to identify patients at a high risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, and patient characteristics or their combination can predict ARE risk in patients with BM before SRT. Methods Gadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (N = 1,404) and a validation cohort (N = 237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features, and patient characteristics or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per patient developing ARE were investigated. Results The best-performing XGBoost model on a lesion level was trained on a combination of radiomics features and DL features (AUC of 0.71 and recall of 0.80). On a patient level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72 and recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per lesion and an AUC of 0.70 and recall of 0.60 per patient. Conclusion Machine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion levels. These models could be used in clinical decision making, informing patients on their risk of ARE and allowing physicians to opt for different therapies.
2022-07-13
Frontiers in Oncology (publié)
doi.org
Interpretable Malware Classification based on Functional Analysis
Miles Q. Li
Benjamin Fung
2022-07-11
Proceedings of the 17th International Conference on Software Technologies (publié)
doi.org
Revisiting Transfer Functions: Learning About a Lagged Exposure-Outcome Association in Time-Series Data
Hiroshi Mamiya
Alexandra M. Schmidt
Erica E. M. Moodie
David Buckeridge
2022-07-11
International Journal of Public Health (publié)
doi.org