This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting. To this extent, we present a novel approach reconciling classical state space models with deep learning methods.

This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting. To this extent, we present a novel approach reconciling classical state space models with deep learning methods.

We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection. By placing interpretability criteria directly into the model, we allow for the model to both optimize parameters related to interpretabil-ity and to directly report a global set of distinguishable dimensions to assist with further data exploration and hypothesis generation.

In this paper, we propose a novel adversarial attack for unlabeled data, which makes the model confuse the instance-level identities of the perturbed data samples. Further, we present a self-supervised contrastive learning framework to adversarially train a robust neural network without labeled data, which aims to maximize the similarity between a random augmentation of a data sample and its instance-wise adversarial perturbation.

We propose to use line-search techniques to automatically set the step-size when training models that can interpolate the data.

Existing approaches range from lightweight'diagonal preconditioning' approaches

Supplementary material: Hold me tight! A Theoretical margin distribution of a linear classifier 2 B Examples of frequency "flipped" images 4 C Invariance and elasticity on MNIST data 4 D Connections to catastrophic forgetting 5 E Examples of filtered images 6 F Subspace sampling of the DCT 6 G Training parameters 7 H Cross-dataset performance 8 I Margin distribution for standard networks 9 J Adversarial training parameters 13 K Description of L2-PGD attack on frequency "flipped" data 14 L Spectral decomposition on frequency "flipped" data 15 M Margin distribution for adversarially trained networks 16 N Margin distribution on random subspaces 19 We demonstrate this effect in practice by repeating the experiment of Sec. MLP we use a simple logistic regression (see Table S1).Clearly, although the values along Figure S1 shows a few example images of the frequency "flipped" versions of the standard computer We further validate our observation of Section 3.2.2 that small margin do indeed corresponds to After this, we continue training the network with a linearly decaying learning rate (max. Figure S4: Filtered image examples. Table S2 shows the performance and training parameters of the different networks used in the paper.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/