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Evolving Neural Controllers for Collective Robotic Inspection

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Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

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Adversarial Training Should Be Cast As a Non-Zero-Sum Game

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One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are trained against adversarially-chosen perturbations of data. Despite the promi ...

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A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

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We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

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Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

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Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretiz ...

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From Kernel Methods to Neural Networks: A Unifying Variational Formulation

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Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

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Deep neural networks have completely revolutionized the field of machinelearning by achieving state-of-the-art results on various tasks ranging fromcomputer vision to protein folding. However, their application is hindered bytheir large computational and m ...

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Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

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Neural tangent kernel (NTK) is a powerful tool to analyze training dynamics of neural networks and their generalization bounds. The study on NTK has been devoted to typical neural network architectures, but it is incomplete for neural networks with Hadamar ...

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Understanding Deep Neural Networks using Adversarial Attacks

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Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as image recognition, object detection, and semantic segmentation. Even thoughthe discriminative power of DNNs is nowadays unquestionable, serious concerns have a ...

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Population pharmacokinetic model selection assisted by machine learning

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A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learni ...

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