Publications

Continually learning representations at scale
Alexandre Galashov
Jovana Mitrovic
Dhruva Tirumala
Yee Whye Teh
Timothy Nguyen
Arslan Chaudhry
Razvan Pascanu
2022-12-31
CoLLAs (published)
proceedings.mlr.press
proceedings.mlr.press
Contrast-agnostic deep learning-based registration pipeline: Validation in spinal cord multimodal MRI data
E. Béal
J. Cohen-Adad
Medical image registration can be challenging, in that optimal solutions depend on the application domain (unimodal, multimodal, intra-subje… (see more)ct and inter-subject), anatomical sites (e.g., brain, spinal cord (SC) and lungs), dimensionality of the data (2D, 3D and 4D), deformation constraints (rigid, affine and nonlinear) and computational time. Solutions that could accommodate a large variety of applications while producing satisfactory results are needed. SynthMorph was recently introduced as an unsupervised deep learning–based registration method. A particularly interesting feature is that training is performed on synthetic data so that registration becomes agnostic to image contrast and anatomy. However, SynthMorph is particularly sensitive to the initial closeness of the images. In this work, we extend the SynthMorph method by developing a cascaded pipeline of two models that can accommodate large and fine deformations, respectively. We also validate this pipeline for the registration of intra-subject multimodal and inter-subject uni/multimodal MRI data of the SC. This task is known to be particularly difficult due to the vicinity of multiple tissue types whose morphometrics can vary substantially across subjects and contrasts. Evaluation of the method was conducted on a publicly available dataset (spine-generic, 267 subjects) and was compared with a state-of-the-art benchmark: Spinal Cord Toolbox and Advanced Normalization Tools. Results demonstrate better registration accuracy compared with the benchmark and about 24–30 times faster on CPUs depending on the image size. This proposed pipeline provides an easy-to-use, accurate and fast solution for multimodal 3D registration. The code and trained models are freely available at https://github.com/ivadomed/multimodal-registration.
2022-12-31
Aperture Neuro (published)
doi.org
Contrasting intra-modal and ranking cross-modal hard negatives to enhance visio-linguistic compositional understanding
Le Zhang
Rabiul Awal
Aishwarya Agrawal
Vision-Language Models (VLMs), such as CLIP, exhibit strong image-text comprehension abilities, facilitating advances in several downstream … (see more)tasks such as zero-shot image classification, image-text retrieval, and text-to-image generation. However, the compositional reasoning abilities of existing VLMs remains subpar. The root of this limitation lies in the inadequate alignment between the images and captions in the pretraining datasets. Additionally, the current contrastive learning objective fails to focus on fine-grained grounding components like relations, actions, and attributes, resulting in "bag-of-words" representations. We introduce a simple and effective method to improve compositional reasoning in VLMs. Our method better leverages available datasets by refining and expanding the standard image-text contrastive learning framework. Our approach does not require specific annotations and does not incur extra parameters. When integrated with CLIP, our technique yields notable improvement over state-of-the-art baselines across five vision-language compositional benchmarks. We open-source our code at https://github.com/lezhang7/Enhance-FineGrained.
2022-12-31
arXiv.org (preprint)
doi.org
arxiv.org
Contrastive Positive Unlabeled Learning
Anish Acharya
Sujay Sanghavi
Li Jing
Bhargav Bhushanam
Michael G. 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… (see more)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.
2022-12-31
(published)
www.semanticscholar.org
Cross-lingual Open-Retrieval Question Answering for African Languages
Odunayo Ogundepo
Tajuddeen Gwadabe
Clara Rivera
Jonathan Clark
Sebastian Ruder
David Adelani
Bonaventure Dossou
Abdou Diop
Claytone Sikasote
Gilles Hacheme
Happy Buzaaba
Ignatius Ezeani
Rooweither Mabuya
Salomey Osei
Chris Emezue
Albert Kahira
Shamsuddeen Muhammad
Akintunde Oladipo
Abraham Owodunni
Atnafu Tonja … (see 24 more)
Iyanuoluwa Shode
Akari Asai
Anuoluwapo Aremu
Ayodele Awokoya
Bernard Opoku
Chiamaka Chukwuneke
Christine Mwase
Clemencia Siro
Stephen Arthur
Tunde Ajayi
Verrah Otiende
Andre Rubungo
Boyd Sinkala
Daniel Ajisafe
Emeka Onwuegbuzia
Falalu Lawan
Ibrahim Ahmad
Jesujoba Alabi
Chinedu Mbonu
Mofetoluwa Adeyemi
Mofya Phiri
Orevaoghene Ahia
Ruqayya Iro
Sonia Adhiambo
2022-12-31
Findings of the Association for Computational Linguistics: EMNLP 2023 (published)
doi.org
openreview.net
Cutting Planes from the Branch-and-Bound Tree: Challenges and Opportunities
Claudio Contardo
Andrea Lodi
Andrea Tramontani
2022-12-31
INFORMS J. Comput. (published)
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… (see more)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.
2022-12-31
IEEE Transactions on Knowledge and Data Engineering (published)
doi.org
arxiv.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
2022-12-31
TAG-ML (published)
proceedings.mlr.press
proceedings.mlr.press
Definitive Care for Severely Injured Children in Quebec
Mélyssa Fortin
Zoe Atsaidis
Brent Hopkins
Etienne St-Louis
Elena Guadagno
Debbie Friedman
Dan Poenaru
2022-12-31
Injury (published)
doi.org
Design and Application of Adaptive Sparse Deep Echo State Network
Cuili Yang
Sheng Yang
Jian Tang
Bing Li
The prediction of appliances energy consumption in building belongs to time series forecasting problem, which can be solved by echo state ne… (see more)twork (ESN). However, due to the randomly initialized inputs and reservoir, some redundant or irrelevant components are inevitably generated in original ESN. To solve this problem, the adaptive sparse deep echo state network (ASDESN) is proposed, in which the information is processed layer by layer. Firstly, the principal component analysis (PCA) layer is inserted to penalize the redundant projection transmitted between sub-reservoirs. Secondly, the coordinate descent based adaptive sparse learning method is proposed to generate the sparse output weights. Particularly, the designed adaptive threshold strategy is able to enlarge the sparsity of output weights as network depth increases. Moreover, the echo state property (ESP) of ASDESN is given to ensure its applications. The experiment results in both simulated benchmark and real appliances energy datasets illustrate that the proposed ASDESN outperforms other ESNs with higher prediction accuracy and stability.
2022-12-31
IEEE transactions on consumer electronics (published)
doi.org
Differential and Overlapping Effects between Exogenous and Endogenous Attention Shape Perceptual Facilitation during Visual Processing.
Mathieu Landry
Jason da Silva Castanheira
Karim Jerbi
Visuospatial attention is not a monolithic process and can be divided into different functional systems. In this framework, exogenous attent… (see more)ion reflects the involuntary orienting of attention resources following a salient event, whereas endogenous attention corresponds to voluntary orienting based on the goals and intentions of individuals. Previous work shows that these attention processes map onto distinct functional systems, yet evidence suggests that they are not fully independent. In the current work, we investigated the differential and overlapping effects of exogenous and endogenous attention on visual processing. We combined spatial cueing of visuospatial attention, EEG, and multivariate pattern analysis to examine where and when the effects of exogenous and endogenous attention were maximally different and maximally similar. Critically, multivariate pattern analysis provided new insights by examining whether classifiers trained to decode the cueing effect for one attention process (e.g., exogenous attention) can successfully decode the cueing effect for the other attention process (e.g., endogenous attention). These analyses uncovered differential and overlapping effects between exogenous and endogenous attention. Next, we combined principal component analyses, single-trial ERPs, and mediation analysis to determine whether these effects facilitate perception, as indexed by the behavioral spatial cueing effects of exogenous and endogenous attention. This approach revealed that three EEG components shape the cueing effects of exogenous and endogenous attention at various times after target onset. Altogether, our study provides a comprehensive account about how overlapping and differential processes of endogenous and exogenous relate to perceptual facilitation in the context of visuospatial attention.
2022-12-31
Journal of Cognitive Neuroscience (unknown)
doi.org
A Distributed Pricing Strategy for Edge Computation Offloading Optimization in Autonomous Driving
Jie Tang
Weilin Zhu
Xiaoming Li
Shaoshan Liu
Xue Liu
The increase of on-vehicle applications has brought explosive computation demands to autonomous vehicles and overwhelmed their limited onboa… (see more)rd resources. Edge computing can offload application load and effectively alleviate this problem. However, the introduction of edge computing faces significant challenges, including the considerable amount of resource contention due to the scarcity of edge resources and the competition among edge computing resource providers to earn users’ services requests. We notice that the problem is not purely technical as solutions for these two problems can become conflicting to each other. In this paper, we propose a distributed pricing strategy to achieve full use of computing resources at the edge and maximize the revenue of service operators, both with guaranteed quality-of-service of on-vehicle applications. More specifically, we first use the multi-leader multi-follower Stackelberg game theory to model the pricing of on-vehicle task offloading under edge computing. Next, we propose a distributed pricing strategy to enable edge servers to adjust their local price distributions so that edge servers can bargain with offloading requesters independently. Experimental results confirm that the proposed distributed pricing strategy can provide more optimized server computing resource utilization while guaranteeing the performance of in-vehicle applications.
2022-12-31
IEEE Network (published)
doi.org