Publications

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… (see more), 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 (published)

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… (see more) 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 (published)

Assessing the potential for virtualizable care in the pediatric emergency department

Esli Osmanlliu

Brett Burstein

Robyn Tamblyn

David L Buckeridge

There is a high potential for patient-to-provider telemedicine in the PED setting. Local patient and visit-level characteristics must be con… (see more)sidered in the design of safe and inclusive telemedicine models for pediatric acute care.

2022-11-19

Journal of Telemedicine and Telecare (published)

Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness

Abdelrahman Zayed

Prasanna Parthasarathi

Goncalo Mordido

Hamid Palangi

Samira Shabanian

Sarath Chandar

Data-driven predictive solutions predominant in commercial applications tend to suffer from biases and stereotypes, which raises equity conc… (see more)erns. Prediction models may discover, use, or amplify spurious correlations based on gender or other protected personal characteristics, thus discriminating against marginalized groups. Mitigating gender bias has become an important research focus in natural language processing (NLP) and is an area where annotated corpora are available. Data augmentation reduces gender bias by adding counterfactual examples to the training dataset. In this work, we show that some of the examples in the augmented dataset can be not important or even harmful for fairness. We hence propose a general method for pruning both the factual and counterfactual examples to maximize the model's fairness as measured by the demographic parity, equality of opportunity, and equality of odds. The fairness achieved by our method surpasses that of data augmentation on three text classification datasets, using no more than half of the examples in the augmented dataset. Our experiments are conducted using models of varying sizes and pre-training settings.

2022-11-19

ArXiv (preprint)

Does Entity Abstraction Help Generative Transformers Reason?

Nicolas Gontier

Siva Reddy

Christopher Pal

We study the utility of incorporating entity type abstractions into pre-trained Transformers and test these methods on four NLP tasks requir… (see more)ing different forms of logical reasoning: (1) compositional language understanding with text-based relational reasoning (CLUTRR), (2) abductive reasoning (ProofWriter), (3) multi-hop question answering (HotpotQA), and (4) conversational question answering (CoQA). We propose and empirically explore three ways to add such abstraction: (i) as additional input embeddings, (ii) as a separate sequence to encode, and (iii) as an auxiliary prediction task for the model. Overall, our analysis demonstrates that models with abstract entity knowledge performs better than without it. The best abstraction aware models achieved an overall accuracy of 88.8% and 91.8% compared to the baseline model achieving 62.9% and 89.8% on CLUTRR and ProofWriter respectively. However, for HotpotQA and CoQA, we find that F1 scores improve by only 0.5% on average. Our results suggest that the benefit of explicit abstraction is significant in formally defined logical reasoning settings requiring many reasoning hops, but point to the notion that it is less beneficial for NLP tasks having less formal logical structure.

2022-11-19

TMLR (accepted)

Learning from uncertain concepts via test time interventions

Ivaxi Sheth

Aamer Abdul Rahman

Laya Rafiee Sevyeri

Mohammad Havaei

S Ebrahimi Kahou

With neural networks applied to safety-critical applications, it has become increasingly important to understand the defining features of de… (see more)cision-making. Therefore, the need to uncover the black boxes to rational representational space of these neural networks is apparent. Concept bottleneck model (CBM) encourages interpretability by predicting human-understandable concepts. They predict concepts from input images and then labels from concepts. Test time intervention, a salient feature of CBM, allows for human-model interactions. However, these interactions are prone to information leakage and can often be ineffective inappropriate communication with humans. We propose a novel uncertainty based strategy, \emph{SIUL: Single Interventional Uncertainty Learning} to select the interventions. Additionally, we empirically test the robustness of CBM and the effect of SIUL interventions under adversarial attack and distributional shift. Using SIUL, we observe that the interventions suggested lead to meaningful corrections along with mitigation of concept leakage. Extensive experiments on three vision datasets along with a histopathology dataset validate the effectiveness of our interventional learning.

2022-11-19

NeurIPS.cc/2022/Workshop/TSRML (accepted)

Striving for data-model efficiency: Identifying data externalities on group performance

Esther Rolf

Ben Packer

Alex Beutel

Fernando Diaz

2022-11-19

NeurIPS.cc/2022/Workshop/TSRML (accepted)

GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction

Dominic Masters

Josef Dean

Kerstin Klaser

Zhiyi Li

Samuel Maddrell-Mander

Adam Sanders

Hatem Helal

Deniz Beker

Ladislav Rampasek

Dominique Beaini

2022-11-17

ArXiv (preprint)

SAMSON: Sharpness-Aware Minimization Scaled by Outlier Normalization for Improving DNN Generalization and Robustness

Goncalo Mordido

Sébastien Henwood

A. Chandar

Franccois Leduc-Primeau

Energy-efficient deep neural network (DNN) accelerators are prone to non-idealities that degrade DNN performance at inference time. To mitig… (see more)ate such degradation, existing methods typically add perturbations to the DNN weights during training to simulate inference on noisy hardware. However, this often requires knowledge about the target hardware and leads to a trade-off between DNN performance and robustness, decreasing the former to increase the latter. In this work, we show that applying sharpness-aware training, by optimizing for both the loss value and loss sharpness, significantly improves robustness to noisy hardware at inference time without relying on any assumptions about the target hardware. In particular, we propose a new adaptive sharpness-aware method that conditions the worst-case perturbation of a given weight not only on its magnitude but also on the range of the weight distribution. This is achieved by performing sharpness-aware minimization scaled by outlier minimization (SAMSON). Our approach outperforms existing sharpness-aware training methods both in terms of model generalization performance in noiseless regimes and robustness in noisy settings, as measured on several architectures and datasets.

2022-11-17

ArXiv (preprint)

APP: Anytime Progressive Pruning

Diganta Misra

Bharat Runwal

Tianlong Chen

Zhangyang Wang

Irina Rish

With the latest advances in deep learning, several methods have been investigated for optimal learning settings in scenarios where the data … (see more)stream is continuous over time. However, training sparse networks in such settings has often been overlooked. In this paper, we explore the problem of training a neural network with a target sparsity in a particular case of online learning: the anytime learning at macroscale paradigm (ALMA). We propose a novel way of progressive pruning, referred to as \textit{Anytime Progressive Pruning} (APP); the proposed approach significantly outperforms the baseline dense and Anytime OSP models across multiple architectures and datasets under short, moderate, and long-sequence training. Our method, for example, shows an improvement in accuracy of

2022-11-16

ACML.org/2022/Workshop/CLL (published)

The Liver Tumor Segmentation Benchmark (LiTS)

Patrick Bilic

Patrick Christ

Eugene Vorontsov

Hongwei Bran Li

Grzegorz Chlebus

Hao Chen

Qi Dou

Chi-Wing Fu

Xu Han

Gabriel Efrain Humpire Mamani

Pheng Ann Heng

Jürgen Hesser

Samuel Kadoury

Julian Walter Holch

Tomasz Konopczynski

Miao Yue

Chunming Li

X. Li

Jana Lipková

John Lowengrub … (see 99 more)

Michal Marianne Amitai

Hans Meine

J. Moltz

Christopher Pal

Marie Piraud

Ivan Ezhov

Xiaojuan Qi

Fernando Navarro

Jin Qi

Florian Kofler

Markus Rempfler

Johannes C. Paetzold

Karsten Roth

Suprosanna Shit

Andrea Schenk

Xiaobin Hu

Anjany Sekuboyina

Ping Zhou

Christian Hülsemeyer

Marcel Beetz

Jan Kirschke

Florian Ettlinger

Felix Gruen

Benedikt Wiestler

Zhiheng Zhang

Georgios Kaissis

Fabian Lohöfer

Rickmer Braren

J. Holch

Michela Antonelli

Felix Hofmann

Woong Bae

Wieland Sommer

Míriam Bellver

Volker Heinemann

Lei Bi

Colin Jacobs

G. Mamani

Bram van Ginneken

Erik B. Dam

Gabriel Chartrand

An Tang

Michal Drozdzal

Bogdan Georgescu

Avi Ben-Cohen

Xavier Giró-i-Nieto

Eyal Klang

M. Amitai

E. Konen

Hayit Greenspan

Johan Moreau

Jan Hendrik Moltz

Alexandre Hostettler

Christian Igel

Luc Soler

Fabian Isensee

Refael Vivanti

Paul Jäger

Adi Szeskin

Fucang Jia

Naama Lev-Cohain

Krishna Chaitanya Kaluva

Jacob Sosna

Mahendra Khened

Leo Joskowicz

Ildoo Kim

Bjoern Menze

Jae-Hun Kim

Zengming Shen

Sungwoong Kim

Simon Kohl

Avinash Kori

Ganapathy Krishnamurthi

Fan Li

Hongchao Li

Junbo Li

Xiaomeng Li

Jun Ma

Klaus Maier-Hein

Kevis-Kokitsi Maninis

Dorit Merhof

Akshay Pai

Mathias Perslev

Jens Petersen

Jordi Pont-Tuset

Oliver Rippel

Ignacio Sarasua

Jordi Torres

Christian Wachinger

Chunliang Wang

Leon Weninger

Jianrong Wu

Daguang Xu

Xiaoping Yang

Simon Chun-Ho Yu

Yading Yuan

Liping Zhang

Jorge Cardoso

Spyridon Bakas

2022-11-16

Medical image analysis (published)