Publications

Overcoming Challenges in Leveraging GANs for Few-Shot Data Augmentation
Christopher Beckham
Issam Laradji
Pau Rodríguez
David Vázquez
Derek Nowrouzezahrai
Christopher Pal
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. We per… (voir plus)form an exploration into how a GAN can be fine-tuned for such a task (one of which is in a class-incremental manner), as well as a rigorous empirical investigation into how well these models can perform to improve few-shot classification. We identify issues related to the difficulty of training such generative models under a purely supervised regime with very few examples, as well as issues regarding the evaluation protocols of existing works. We also find that in this regime, classification accuracy is highly sensitive to how the classes of the dataset are randomly split. Therefore, we propose a semi-supervised fine-tuning approach as a more pragmatic way forward to address these problems.
2022-11-27
Proceedings of The 1st Conference on Lifelong Learning Agents (publié)
doi.org
proceedings.mlr.press
Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning
Ivaxi Sheth
Aamer Abdul Rahman
Mohammad Havaei
S Ebrahimi Kahou
Humans have perfected the art of learning from multiple modalities through sensory organs. Despite their impressive predictive performance o… (voir plus)n a single modality, neural networks cannot reach human level accuracy with respect to multiple modalities. This is a particularly challenging task due to variations in the structure of respective modalities. Conditional Batch Normalization (CBN) is a popular method that was proposed to learn contextual features to aid deep learning tasks. This technique uses auxiliary data to improve representational power by learning affine transformations for convolutional neural networks. Despite the boost in performance observed by using CBN layers, our work reveals that the visual features learned by introducing auxiliary data via CBN deteriorates. We perform comprehensive experiments to evaluate the brittleness of CBN networks to various datasets, suggesting that learning from visual features alone could often be superior for generalization. We evaluate CBN models on natural images for bird classification and histology images for cancer type classification. We observe that the CBN network learns close to no visual features on the bird classification dataset and partial visual features on the histology dataset. Our extensive experiments reveal that CBN may encourage shortcut learning between the auxiliary data and labels.
2022-11-27
ArXiv (prépublication)
doi.org
arxiv.org
Shimming toolbox: An open‐source software toolbox for <scp>B0</scp> and <scp>B1</scp> shimming in MRI
Alexandre D'Astous
Gaspard Cereza
Daniel Papp
Kyle M. Gilbert
Jason P. Stockmann
Eva Alonso‐Ortiz
Julien Cohen‐Adad
Introduce Shimming Toolbox ( https://shimming‐toolbox.org), an open‐source software package for prototyping new methods and performing s… (voir plus)tatic, dynamic, and real‐time B0 shimming as well as B1 shimming experiments. Shimming Toolbox features various field mapping techniques, manual and automatic masking for the brain and spinal cord, B0 and B1 shimming capabilities accessible through a user‐friendly graphical user interface. Validation of Shimming Toolbox was demonstrated in three scenarios: (i) B0 dynamic shimming in the brain at 7T using custom AC/DC coils, (ii) B0 real‐time shimming in the spinal cord at 3T, and (iii) B1 static shimming in the spinal cord at 7T. Shimming Toolbox provides an open‐source platform where researchers can collaborate, prototype and conveniently test B0 and B1 shimming experiments. Future versions will include additional field map preprocessing techniques, optimization algorithms, and compatibility across multiple MRI manufacturers.
2022-11-27
Magnetic Resonance in Medicine (inconnu)
doi.org
Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance
Mats Leon Richter
Christopher Pal
Minimal changes to neural architectures (e.g. changing a single hyperparameter in a key layer), can lead to significant gains in predictive … (voir plus)performance in Convolutional Neural Networks (CNNs). In this work, we present a new approach to receptive field analysis that can yield these types of theoretical and empirical performance gains across twenty well-known CNN architectures examined in our experiments. By further developing and formalizing the analysis of receptive field expansion in convolutional neural networks, we can predict unproductive layers in an automated manner before ever training a model. This allows us to optimize the parameter-efficiency of a given architecture at low cost. Our method is computationally simple and can be done in an automated manner or even manually with minimal effort for most common architectures. We demonstrate the effectiveness of this approach by increasing parameter efficiency across past and current top-performing CNN-architectures. Specifically, our approach is able to improve ImageNet1K performance across a wide range of well-known, state-of-the-art (SOTA) model classes, including: VGG Nets, MobileNetV1, MobileNetV3, NASNet A (mobile), MnasNet, EfficientNet, and ConvNeXt - leading to a new SOTA result for each model class.
2022-11-25
ArXiv (prépublication)
doi.org
arxiv.org
Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world
Arash Shaban-Nejad
Martin Michalowski
Simone Bianco
John S. Brownstein
David L Buckeridge
Robert L Davis
2022-11-24
Experimental Biology and Medicine (publié)
doi.org
Taxonomy of Benchmarks in Graph Representation Learning
Renming Liu
Semih Cantürk
Frederik Wenkel
Sarah McGuire
Xinyi Wang
Anna Little
Leslie O’Bray
Michael Perlmutter
Bastian Rieck
Matthew Hirn
Guy Wolf
Ladislav Rampasek
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. Whil… (voir plus)e extensive research has been done on developing GNN models with superior performance according to a collection of graph representation learning benchmarks, it is currently not well understood what aspects of a given model are probed by them. For example, to what extent do they test the ability of a model to leverage graph structure vs. node features? Here, we develop a principled approach to taxonomize benchmarking datasets according to a
2022-11-23
Learning on Graphs Conference (présentation orale)
doi.org
proceedings.mlr.press
Towards Adaptive Cybersecurity for Green IoT
Talal Halabi
Martine Bellaiche
Benjamin C. M. Fung
The Internet of Things (IoT) paradigm has led to an explosion in the number of IoT devices and an exponential rise in carbon footprint incur… (voir plus)red by overburdened IoT networks and pervasive cloud/edge communications. Hence, there is a growing interest in industry and academia to enable the efficient use of computing infrastructures by optimizing the management of data center and IoT resources (hardware, software, network, and data) and reducing operational costs to slash greenhouse gas emissions and create healthy environments. Cybersecurity has also been considered in such efforts as a contributor to these environmental issues. Nonetheless, most green security approaches focus on designing low-overhead encryption schemes and do not emphasize energy-efficient security from architectural and deployment viewpoints. This paper sheds light on the emerging paradigm of adaptive cybersecurity as one of the research directions to support sustainable computing in green IoT. It presents three potential research directions and their associated methods for designing and deploying adaptive security in green computing and resource-constrained IoT environments to save on energy consumption. Such efforts will transform the development of data-driven IoT security solutions to be greener and more environment-friendly.
2022-11-23
2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) (publié)
doi.org
Age differences in functional brain networks associated with loneliness and empathy
Laetitia Mwilambwe-Tshilobo
Roni Setton
Danilo Bzdok
Gary R. Turner
R. Nathan Spreng
Network Neuroscience
Loneliness is associated with differences in resting-state functional connectivity (RSFC) within and between large-scale networks in early- … (voir plus)and middle-aged adult cohorts. However, age-related changes in associations between sociality and brain function into late adulthood are not well understood. Here, we examined age differences in the association between two dimensions of sociality—loneliness and empathic responding—and RSFC of the cerebral cortex. Self-report measures of loneliness and empathy were inversely related across the entire sample of younger (mean age = 22.6y, n = 128) and older (mean age = 69.0y, n = 92) adults. Using multivariate analyses of multi-echo fMRI RSFC, we identified distinct functional connectivity patterns for individual and age group differences associated with loneliness and empathic responding. Loneliness in young and empathy in both age groups was related to greater visual network integration with association networks (e.g., default, fronto-parietal control). In contrast, loneliness was positively related to within- and between-network integration of association networks for older adults. These results extend our previous findings in early- and middle-aged cohorts, demonstrating that brain systems associated with loneliness, as well as empathy, differ in older age. Further, the findings suggest that these two aspects of social experience engage different neurocognitive processes across human life-span development.
2022-11-22
Network Neuroscience (publié)
doi.org
Momentum Extragradient is Optimal for Games with Cross-Shaped Spectrum
Junhyung Lyle Kim
Gauthier Gidel
Anastasios Kyrillidis
Fabian Pedregosa
Google Research
© J.l. Kim
The extragradient method has recently gained a lot of attention, due to its convergence behavior on smooth games. In games, the eigenvalues … (voir plus)of the Jacobian of the vector field are distributed on the complex plane, exhibiting more convoluted dynamics compared to minimization. In this work, we take a polynomial-based analysis of the extragradient with momentum for optimizing games with \emph{cross-shaped} spectrum on the complex plane. We show two results: first, the extragradient with momentum exhibits three different modes of convergence based on the hyperparameter setup: when the eigenvalues are distributed
2022-11-22
NeurIPS.cc/2022/Workshop/OPT (poster)
openreview.net
openreview.net
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
Sitao Luan
Harry Zhao
Mingde Zhao
Chenqing Hua
Xiao-Wen Chang
Doina Precup
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filte… (voir plus)rs the neighborhood node information. Though effective for various tasks, in this paper, we show that they are potentially a problematic factor underlying all GNN methods for learning on certain datasets, as they force the node representations similar, making the nodes gradually lose their identity and become indistinguishable. Hence, we augment the aggregation operations with their dual, i.e. diversification operators that make the node more distinct and preserve the identity. Such augmentation replaces the aggregation with a two-channel filtering process that, in theory, is beneficial for enriching the node representations. In practice, the proposed two-channel filters can be easily patched on existing GNN methods with diverse training strategies, including spectral and spatial (message passing) methods. In the experiments, we observe desired characteristics of the models and significant performance boost upon the baselines on 9 node classification tasks.
2022-11-21
NeurIPS.cc/2022/Workshop/GLFrontiers (accepté)
doi.org
openreview.net
GraphCG: Unsupervised Discovery of Steerable Factors in Graphs
Shengchao Liu
Chengpeng Wang
Weili Nie
Hanchen Wang
Jiarui Lu
Bolei Zhou
Jian Tang
Deep generative models have been extensively explored recently, especially for the graph data such as molecular graphs and point clouds. Yet… (voir plus), much less investigation has been carried out on understanding the learned latent space of deep graph generative models. Such understandings can open up a unified perspective and provide guidelines for essential tasks like controllable generation. In this paper, we first examine the representation space of the recent deep generative model trained for graph data, observing that the learned representation space is not perfectly disentangled. Based on this observation, we then propose an unsupervised method called GraphCG, which is model-agnostic and task-agnostic for discovering steerable factors in graph data. Specifically, GraphCG learns the semantic-rich directions via maximizing the corresponding mutual information, where the edited graph along the same direction will possess certain steerable factors. We conduct experiments on two types of graph data, molecular graphs and point clouds. Both the quantitative and qualitative results show the effectiveness of GraphCG for discovering steerable factors. The code will be public in the near future.
2022-11-21
NeurIPS.cc/2022/Workshop/GLFrontiers (accepté)
openreview.net
openreview.net
SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows
Vikram Voleti
Boris N. Oreshkin
Florent Bocquelet
Félix G. Harvey
Louis-Simon Ménard
Christopher Pal
Inverse Kinematics (IK) systems are often rigid with respect to their input character, thus requiring user intervention to be adapted to new… (voir plus) skeletons. In this paper we aim at creating a flexible, learned IK solver applicable to a wide variety of human morphologies. We extend a state-of-the-art machine learning IK solver to operate on the well known Skinned Multi-Person Linear model (SMPL). We call our model SMPL-IK, and show that when integrated into real-time 3D software, this extended system opens up opportunities for defining novel AI-assisted animation workflows. For example, pose authoring can be made more flexible with SMPL-IK by allowing users to modify gender and body shape while posing a character. Additionally, when chained with existing pose estimation algorithms, SMPL-IK accelerates posing by allowing users to bootstrap 3D scenes from 2D images while allowing for further editing. Finally, we propose a novel SMPL Shape Inversion mechanism (SMPL-SI) to map arbitrary humanoid characters to the SMPL space, allowing artists to leverage SMPL-IK on custom characters. In addition to qualitative demos showing proposed tools, we present quantitative SMPL-IK baselines on the H36M and AMASS datasets.
2022-11-21
SIGGRAPH Asia 2022 Technical Communications (publié)
doi.org
arxiv.org