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• Feature-robustness, flatness and generalization error for deep neural networks

  IntroductionIn this paper, the authors focus on the following open question: Why does minimizing the empirical error during deep neural network train...

• A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern

  HighlightsThey prove two theorems stating that, independently of the data sets: 1.- Every finite-dimensional pattern can be found as the loss landsca...

• Similarity of Neural Network Representations Revisited

  Highlights A similarity measure is introduced to measure the difference between representations created by trained neural networks. They compare to ...

• G-SGD: Optimizing ReLU neural networks in its positively scale-invariant space

  Highlights ReLU neural networks are optimized in a space that is invariant under positive rescaling. This space is also sufficient to represent ReLU ...

• An exponential learning rate schedule for deep learning

  IntroductionIn this paper, they show that when using batch normalization and weight decay one can use an exponentially increasing learning rate and st...

• Sharp Minima can Generalize for Deep Nets

  IntroductionGeneralization of neural networks has been linked to the flatness of the loss landscape around the found minimum. Therefore, it is reasona...

• Unadversarial examples: designing objects for robust vision

  HighlightsIn this paper, the authors assume a setting where the users have access to both the model and the objects they want to detect with the model...

• Deep Learning: A philosophical introduction

  IntroductionThis paper focuses on the, yet not answered satisfactorily, why question in deep learning: Why do CNNs work so good?Before going into the...

• Evasion attacks against machine learning at test time

  Highlights The authors propose an optimization method to generate adversarial examples for SVM and neural nets. The method has a regularization meth...

• Path-SGD: Path-Normalized Optimization in Deep Neural Networks

  Highlights Use of positive homogeneity (or positive scale invariance) of ReLU networks to suggest an optimization method that is invariant under resc...

• Intriguing properties of neural networks

  HighlightsThe authors find two properties of neural networks. There is no distinction between individual high-level units and random linear combinati...