Publications

A circulating proteome-informed prognostic model of COVID-19 disease activity that relies on 1 routinely available clinical laboratories 2
William Ma
Antoine Soulé
Karine Tremblay
Simon Rousseau
Amin Emad
Abstract
2023-01-01
(publié)
www.semanticscholar.org
Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport
Alexander Tong
Nikolay Malkin
Guillaume Huguet
Yanlei Zhang
Jarrid Rector-Brooks
Kilian FATRAS
Guy Wolf
Yoshua Bengio
2023-01-01
arXiv.org (prépublication)
doi.org
Constant Memory Attentive Neural Processes
Leo Feng
Frederick Tung
Hossein Hajimirsadeghi
Yoshua Bengio
Mohamed Osama Ahmed
2023-01-01
arXiv.org (prépublication)
doi.org
Contrast-agnostic deep learning–based registration pipeline: Validation in spinal cord multimodal MRI data
E. Beal
Julien Cohen-Adad
2023-01-01
Aperture Neuro (publié)
doi.org
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Fine-grained Understanding
Le Zhang
Md. Rabiul Awal
Aishwarya Agrawal
2023-01-01
arXiv.org (prépublication)
doi.org
Contrastive Positive Unlabeled Learning
Anish Acharya
Sujay Sanghavi
Li Jing
Bhargav Bhushanam
Michael Rabbat
I. Dhillon
Self-supervised pretraining on unlabeled data followed by supervised fine-tuning on labeled data is a popular paradigm for learning from lim… (voir plus)ited labeled examples. We extend this paradigm to the classical positive unlabeled (PU) setting, where the task is to learn a binary classifier given only a few labeled positive samples, and (often) a large amount of unlabeled samples (which could be positive or negative). We first propose a simple extension of standard infoNCE family of contrastive losses, to the PU setting; and show that this learns superior representations, as compared to existing unsupervised and supervised approaches. We then develop a simple methodology to pseudo-label the unlabeled samples using a new PU-specific clustering scheme; these pseudo-labels can then be used to train the final (positive vs. negative) classifier. Our method handily outperforms state-of-the-art PU methods over several standard PU benchmark datasets, while not requiring a-priori knowledge of any class prior (which is a common assumption in other PU methods). We also provide a simple theoretical analysis that motivates our methods.
2023-01-01
(publié)
www.semanticscholar.org
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity
Eduard Gorbunov
Adrien Taylor
Samuel Horváth
Gauthier Gidel
Algorithms for min-max optimization and variational inequalities are often studied under monotonicity assumptions. Motivated by non-monotone… (voir plus) machine learning applications, we follow the line of works (Diakonikolas et al., 2021; Lee & Kim, 2021; Pethick et al., 2022; Bohm,2022) aiming at going beyond monotonicity by considering the weaker *negative comonotonicity* assumption. In this work, we provide tight complexity analyses for the Proximal Point (PP), Extragradient (EG), and Optimistic Gradient (OG) methods in this setup, closing several questions on their working guarantees beyond monotonicity. In particular, we derive the first non-asymptotic convergence rates for PP under negative comonotonicity and star-negative comonotonicity and show their tightness via constructing worst-case examples; we also relax the assumptions for the last-iterate convergence guarantees for EG and OG and prove the tightness of the existing best-iterate guarantees for EG and OG via constructing counter-examples.
2023-01-01
ICML (publié)
openreview.net
openreview.net
Cutting Planes from the Branch-and-Bound Tree: Challenges and Opportunities
Claudio Contardo
Andrea Lodi
Andrea Tramontani
2023-01-01
INFORMS J. Comput. (publié)
doi.org
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection
Hadi Hojjati
Narges Armanfard
Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. With recent adv… (voir plus)ancements in deep learning, researchers have designed efficient deep anomaly detection methods. Existing works commonly use neural networks to map the data into a more informative representation and then apply an anomaly detection algorithm. In this paper, we propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hyper-sphere on its latent representation. We propose an anomaly score which is a combination of autoencoder's reconstruction error and the distance from the center of the enclosing hypersphere in the latent representation. Minimizing this anomaly score aids us in learning the underlying distribution of the normal class during training. Including the reconstruction error in the anomaly score ensures that DASVDD does not suffer from the common hypersphere collapse issue since the DASVDD model does not converge to the trivial solution of mapping all inputs to a constant point in the latent representation. Experimental evaluations on several benchmark datasets show that the proposed method outperforms the commonly used state-of-the-art anomaly detection algorithms while maintaining robust performance across different anomaly classes.
2023-01-01
IEEE Transactions on Knowledge and Data Engineering (publié)
doi.org
arxiv.org
Deep Multirepresentation Learning for Data Clustering.
Mohammadreza Sadeghi
Narges Armanfard
Deep clustering incorporates embedding into clustering in order to find a lower-dimensional space suitable for clustering tasks. Conventiona… (voir plus)l deep clustering methods aim to obtain a single global embedding subspace (aka latent space) for all the data clusters. In contrast, in this article, we propose a deep multirepresentation learning (DML) framework for data clustering whereby each difficult-to-cluster data group is associated with its own distinct optimized latent space and all the easy-to-cluster data groups are associated with a general common latent space. Autoencoders (AEs) are employed for generating cluster-specific and general latent spaces. To specialize each AE in its associated data cluster(s), we propose a novel and effective loss function which consists of weighted reconstruction and clustering losses of the data points, where higher weights are assigned to the samples more probable to belong to the corresponding cluster(s). Experimental results on benchmark datasets demonstrate that the proposed DML framework and loss function outperform state-of-the-art clustering approaches. In addition, the results show that the DML method significantly outperforms the SOTA on imbalanced datasets as a result of assigning an individual latent space to the difficult clusters.
2023-01-01
IEEE Transactions on Neural Networks and Learning Systems (publié)
doi.org
Deep Networks as Paths on the Manifold of Neural Representations
Richard D Lange
Devin Kwok
Jordan Kyle Matelsky
Xinyue Wang
David Rolnick
Konrad Paul Kording
2023-01-01
TAG-ML (publié)
proceedings.mlr.press
openreview.net
Definitive Care for Severely Injured Children in Quebec
Mélyssa Fortin
Zoe Atsaidis
Brent Hopkins
Etienne St-Louis
Elena Guadagno
Debbie Friedman
Dan Poenaru
2023-01-01
Injury (publié)
doi.org