Publications

Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection
Bonaventure F. P. Dossou
Dianbo Liu
Xiaolu Ji
Moksh J. Jain
Almer M. van der Sloot
Roger Palou
Mike Tyers
Yoshua Bengio
2022-09-18
ArXiv (prépublication)
doi.org
arxiv.org
OSSEM: one-shot speaker adaptive speech enhancement using meta learning
Cheng Yu
Szu-Wei Fu
Tsun-An Hsieh
Yu Tsao
Mirco Ravanelli
2022-09-18
Interspeech 2022 (publié)
doi.org
arxiv.org
SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation
Artem Ploujnikov
Mirco Ravanelli
2022-09-18
Interspeech 2022 (publié)
doi.org
arxiv.org
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Reza Azad
Moein Heidari
Julien Cohen-Adad
Ehsan Adeli
Dorit Merhof
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related disea… (voir plus)ses such as osteoporosis, vertebral fractures, and intervertebral disc herniation. To date, various approaches have been developed in the literature which routinely relies on detecting the discs as the primary step. A disadvantage of many cohort studies is that the localization algorithm also yields false-positive detections. In this study, we aim to alleviate this problem by proposing a novel U-Net-based structure to predict a set of candidates for intervertebral disc locations. In our design, we integrate the image shape information (image gradients) to encourage the model to learn rich and generic geometrical information. This additional signal guides the model to selectively emphasize the contextual representation and suppress the less discriminative features. On the post-processing side, to further decrease the false positive rate, we propose a permutation invariant 'look once' model, which accelerates the candidate recovery procedure. In comparison with previous studies, our proposed approach does not need to perform the selection in an iterative fashion. The proposed method was evaluated on the spine generic public multi-center dataset and demonstrated superior performance compared to previous work. We have provided the implementation code in https://github.com/rezazad68/intervertebral-lookonce
2022-09-16
Predictive Intelligence in Medicine (publié)
doi.org
arxiv.org
Video Game Bad Smells: What They Are and How Developers Perceive Them
Vittoria Nardone
Biruk Asmare Muse
Mouna Abidi
Foutse Khomh
Massimiliano Di Penta
Video games represent a substantial and increasing share of the software market. However, their development is particularly challenging as i… (voir plus)t requires multi-faceted knowledge, which is not consolidated in computer science education yet. This article aims at defining a catalog of bad smells related to video game development. To achieve this goal, we mined discussions on general-purpose and video game-specific forums. After querying such a forum, we adopted an open coding strategy on a statistically significant sample of 572 discussions, stratified over different forums. As a result, we obtained a catalog of 28 bad smells, organized into five categories, covering problems related to game design and logic, physics, animation, rendering, or multiplayer. Then, we assessed the perceived relevance of such bad smells by surveying 76 game development professionals. The survey respondents agreed with the identified bad smells but also provided us with further insights about the discussed smells. Upon reporting results, we discuss bad smell examples, their consequences, as well as possible mitigation/fixing strategies and trade-offs to be pursued by developers. The catalog can be used not only as a guideline for developers and educators but also can pave the way toward better automated tool support for video game developers.
2022-09-15
ACM Transactions on Software Engineering and Methodology (publié)
doi.org
Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity.
Benjamin Clemens
Jeremy Lefort-Besnard
Christoph Ritter
Elke Smith
Mikhail Votinov
Birgit Derntl
Ute Habel
Danilo Bzdok
BACKGROUND Sexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors… (voir plus). OBJECTIVE Here, we bring to bear a machine learning approach to predict sexual orientation from gray matter volumes (GMVs) or resting-state functional connectivity (RSFC) in a cohort of 45 heterosexual and 41 homosexual participants. METHODS  In both brain assessments, we used penalized logistic regression models and nonparametric permutation. RESULTS  We found an average accuracy of 62% (±6.72) for predicting sexual orientation based on GMV and an average predictive accuracy of 92% (±9.89) using RSFC. Regions in the precentral gyrus, precuneus and the prefrontal cortex were significantly informative for distinguishing heterosexual from homosexual participants in both the GMV and RSFC settings. CONCLUSIONS  These results indicate that, aside from self-reports, RSFC offers neurobiological information valuable for highly accurate prediction of sexual orientation. We demonstrate for the first time that sexual orientation is reflected in specific patterns of RSFC, which enable personalized, brain-based predictions of this highly complex human trait. While these results are preliminary, our neurobiologically based prediction framework illustrates the great value and potential of RSFC for revealing biologically meaningful and generalizable predictive patterns in the human brain.
2022-09-14
Cerebral Cortex (publié)
doi.org
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
Leo Feng
Padideh Nouri
Aneri Muni
Yoshua Bengio
Pierre-Luc Bacon
The ability to accelerate the design of biological sequences can have a substantial impact on the progress of the medical field. The problem… (voir plus) can be framed as a global optimization problem where the objective is an expensive black-box function such that we can query large batches restricted with a limitation of a low number of rounds. Bayesian Optimization is a principled method for tackling this problem. However, the astronomically large state space of biological sequences renders brute-force iterating over all possible sequences infeasible. In this paper, we propose MetaRLBO where we train an autoregressive generative model via Meta-Reinforcement Learning to propose promising sequences for selection via Bayesian Optimization. We pose this problem as that of finding an optimal policy over a distribution of MDPs induced by sampling subsets of the data acquired in the previous rounds. Our in-silico experiments show that meta-learning over such ensembles provides robustness against reward misspecification and achieves competitive results compared to existing strong baselines.
2022-09-13
ArXiv (prépublication)
doi.org
arxiv.org
Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship
Andres Ferraro
Gustavo Ferreira
Fernando Diaz
Georgina Born
2022-09-13
Proceedings of the 16th ACM Conference on Recommender Systems (publié)
doi.org
arxiv.org
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
Kiwan Maeng
Haiyu Lu
Luca Melis
John Nguyen
Michael Rabbat
Carole-Jean Wu
2022-09-13
Proceedings of the 16th ACM Conference on Recommender Systems (publié)
doi.org
arxiv.org
Processing visual ambiguity in fractal patterns: Pareidolia as a sign of creativity
Antoine Bellemare Pepin
Yann Harel
Jordan O’Byrne
Geneviève Mageau
Arne Dietrich
Karim Jerbi
2022-09-08
iScience (publié)
doi.org
Rapidly Inferring Personalized Neurostimulation Parameters with Meta-Learning: A Case Study of Individualized Fiber Recruitment in Vagus Nerve Stimulation
Ximeng Mao
Yao-Chuan Chang
Stavros Zanos
Guillaume Lajoie
2022-09-08
bioRxiv (prépublication)
doi.org
Unifying Generative Models with GFlowNets
Dinghuai Zhang
Ricky T. Q. Chen
Nikolay Malkin
Yoshua Bengio
There are many frameworks for deep generative modeling, each often presented with their own specific training algorithms and inference metho… (voir plus)ds. Here, we demonstrate the connections between existing deep generative models and the recently introduced GFlowNet framework, a probabilistic inference machine which treats sampling as a decision-making process. This analysis sheds light on their overlapping traits and provides a unifying viewpoint through the lens of learning with Markovian trajectories. Our framework provides a means for unifying training and inference algorithms, and provides a route to shine a unifying light over many generative models. Beyond this, we provide a practical and experimentally verified recipe for improving generative modeling with insights from the GFlowNet perspective.
2022-09-06
ArXiv (prépublication)
doi.org
arxiv.org