Publications

Michael Mozer

2020-06-28

arXiv (preprint)

Shared and unique brain network features predict cognition, personality and mental health in childhood

Jianzhong Chen

Angela Tam

Valeria Kebets

Csaba Orbán

Leon Qi Rong Ooi

Scott Marek

Nico U.F. Dosenbach

Simon B. Eickhoff

Danilo Bzdok

Avram J. Holmes

B.T. Thomas Yeo

The manner through which individual differences in brain network organization track population-level behavioral variability is a fundamental… (see more) question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, the focus of most studies on single behavioral traits has come at the expense of capturing broader relationships across behaviors. Here, we utilized a large-scale dataset of 1858 typically developing children to estimate whole-brain functional network organization that is predictive of individual differences in cognition, impulsivity-related personality, and mental health during rest and task states. Predictive network features were distinct across the broad behavioral domains: cognition, personality and mental health. On the other hand, traits within each behavioral domain were predicted by highly similar network features. This is surprising given decades of research emphasizing that distinct brain networks support different mental processes. Although tasks are known to modulate the functional connectome, we found that predictive network features were similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood, yet are unique to different behavioral domains.

2020-06-23

bioRxiv (preprint)

Image-to-image Mapping with Many Domains by Sparse Attribute Transfer

Matthew Amodio

2020-06-22

arXiv (preprint)

Rethinking Distributional Matching Based Domain Adaptation

Bin Li

Yezhen Wang

Tong Che

Shanghang Zhang

Sicheng Zhao

Pengfei Xu

Wenzhen Zhou

Kurt W. Keutzer

Domain adaptation (DA) is a technique that transfers predictive models trained on a labeled source domain to an unlabeled target domain, wit… (see more)h the core difficulty of resolving distributional shift between domains. Currently, most popular DA algorithms are based on distributional matching (DM). However in practice, realistic domain shifts (RDS) may violate their basic assumptions and as a result these methods will fail. In this paper, in order to devise robust DA algorithms, we first systematically analyze the limitations of DM based methods, and then build new benchmarks with more realistic domain shifts to evaluate the well-accepted DM methods. We further propose InstaPBM, a novel Instance-based Predictive Behavior Matching method for robust DA. Extensive experiments on both conventional and RDS benchmarks demonstrate both the limitations of DM methods and the efficacy of InstaPBM: Compared with the best baselines, InstaPBM improves the classification accuracy respectively by

2020-06-22

ArXiv (preprint)

Advantages of biologically-inspired adaptive neural activation in RNNs during learning

Dynamic adaptation in single-neuron response plays a fundamental role in neural coding in biological neural networks. Yet, most neural activ… (see more)ation functions used in artificial networks are fixed and mostly considered as an inconsequential architecture choice. In this paper, we investigate nonlinear activation function adaptation over the large time scale of learning, and outline its impact on sequential processing in recurrent neural networks. We introduce a novel parametric family of nonlinear activation functions, inspired by input-frequency response curves of biological neurons, which allows interpolation between well-known activation functions such as ReLU and sigmoid. Using simple numerical experiments and tools from dynamical systems and information theory, we study the role of neural activation features in learning dynamics. We find that activation adaptation provides distinct task-specific solutions and in some cases, improves both learning speed and performance. Importantly, we find that optimal activation features emerging from our parametric family are considerably different from typical functions used in the literature, suggesting that exploiting the gap between these usual configurations can help learning. Finally, we outline situations where neural activation adaptation alone may help mitigate changes in input statistics in a given task, suggesting mechanisms for transfer learning optimization.

2020-06-21

ArXiv (preprint)

HNHN: Hypergraph Networks with Hyperedge Neurons

Yihe Dong

W. Sawin

2020-06-21

ArXiv (preprint)

Learning to Prove from Synthetic Theorems

Eser Aygün

Vlad Firoiu

Laurent Orseau

Shibl Mourad

A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in t… (see more)raining successful deep learning models. To tackle this problem, we propose an approach that relies on training with synthetic theorems, generated from a set of axioms. We show that such theorems can be used to train an automated prover and that the learned prover transfers successfully to human-generated theorems. We demonstrate that a prover trained exclusively on synthetic theorems can solve a substantial fraction of problems in TPTP, a benchmark dataset that is used to compare state-of-the-art heuristic provers. Our approach outperforms a model trained on human-generated problems in most axiom sets, thereby showing the promise of using synthetic data for this task.

2020-06-18

ArXiv (preprint)

Individual differences in interpersonal coordination

Julia Ayache

Guillaume Dumas

A. Sumich

D. Kuss

Darren Rhodes

Nadja Heym

2020-06-17

(published)

Special Issue on Novel Informatics Approaches to COVID-19 Research

Huanan Xu

David L Buckeridge

Fei Wang Guest Editors

2020-06-16

Journal of Biomedical Informatics (published)

Intelligent Tools for Precision Public Health.

Anya Okhmatovskaia

David L Buckeridge

2020-06-15

Medical Informatics Europe (published)

Investigating the Barriers to Physician Adoption of an Artificial Intelligence- Based Decision Support System in Emergency Care: An Interpretative Qualitative Study.

Cecile Petitgand

Aude Motulsky

Jean-Louis Denis

Catherine Régis

2020-06-15

Medical Informatics Europe (published)

Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

Ghouthi Boukli hacene

Vincent Gripon

Matthieu Arzel

Nicolas Farrugia

Deep Neural Networks (DNNs) in general and Convolutional Neural Networks (CNNs) in particular are state-of-the-art in numerous computer visi… (see more)on tasks such as object classification and detection. However, the large amount of parameters they contain leads to a high computational complexity and strongly limits their usability in budget-constrained devices such as embedded devices. In this paper, we propose a combination of a pruning technique and a quantization scheme that effectively reduce the complexity and memory usage of convolutional layers of CNNs, by replacing the complex convolutional operation by a low-cost multiplexer. We perform experiments on CIFAR10, CIFAR100 and SVHN datasets and show that the proposed method achieves almost state-of-the-art accuracy, while drastically reducing the computational and memory footprints compared to the baselines. We also propose an efficient hardware architecture, implemented on Field Programmable Gate Arrays (FPGAs), to accelerate inference, which works as a pipeline and accommodates multiple layers working at the same time to speed up the inference process. In contrast with most proposed approaches which have used external memory or software defined memory controllers, our work is based on algorithmic optimization and full-hardware design, enabling a direct, on-chip memory implementation of a DNN while keeping close to state of the art accuracy.

2020-06-15

IEEE New Circuits and Systems Conference (published)