Over the past few years, the rise of AI fakes has got a lot of people very worried, with experts warning that this technology could be used to spread lies and misinformation online. But actual evidence of this happening has so far been thin on the ground, which is why a new report from the Associated Press makes for such interesting reading. The AP says it found evidence of a what seems to be a would-be spy using an AI-generated profile picture to fool contacts on LinkedIn. The publication says that the fake profile, given the name Katie Jones, connected with a number of policy experts in Washington. These included a scattering of government figures such as a senator's aide, a deputy assistant secretary of state, and Paul Winfree, an economist currently being considered for a seat on the Federal Reserve.

Sustainable Supply Chain (SSC) management aims at integrating economic, environmental and social goals to assist in the long-term planning of a company and its supply chains. There is no consensus in the literature as to whether social and environmental responsibilities are profit-compatible. However, the conflicting nature of these goals is explicit when considering specific assessment measures and, in this scenario, multi-objective optimization is a way to represent problems that simultaneously optimize the goals. This paper proposes a Lagrangian matheuristic method, called $AugMathLagr$, to solve a hard and relevant multi-objective problem found in the literature. $AugMathLagr$ was extensively tested using artificial instances defined by a generator presented in this paper. The results show a competitive performance of $AugMathLagr$ when compared with an exact multi-objective method limited by time and a matheuristic recently proposed in the literature and adapted here to address the studied problem. In addition, computational results on a case study are presented and analyzed, and demonstrate the outstanding performance of $AugMathLagr$.

Simulating dynamic rupture propagation is challenging due to the uncertainties involved in the underlying physics of fault slip, stress conditions, and frictional properties of the fault. A trial and error approach is often used to determine the unknown parameters describing rupture, but running many simulations usually requires human review to determine how to adjust parameter values and is thus not very efficient. To reduce the computational cost and improve our ability to determine reasonable stress and friction parameters, we take advantage of the machine learning approach. We develop two models for earthquake rupture propagation using the artificial neural network (ANN) and the random forest (RF) algorithms to predict if a rupture can break a geometric heterogeneity on a fault. We train the models using a database of 1600 dynamic rupture simulations computed numerically. Fault geometry, stress conditions, and friction parameters vary in each simulation. We cross-validate and test the predictive power of the models using an additional 400 simulated ruptures, respectively. Both RF and ANN models predict rupture propagation with more than 81% accuracy, and model parameters can be used to infer the underlying factors most important for rupture propagation. Both of the models are computationally efficient such that the 400 testings require a fraction of a second, leading to potential applications of dynamic rupture that have previously not been possible due to the computational demands of physics-based rupture simulations.

In this paper, we evaluate the PredNet \cite{lotter16} on the Something-something action data set \cite{farzaneh18} and implement the PredNet+, which we train in a multi-task fashion to output both classification labels and predictions. Our idea is to condition video prediction and action classification on each other. We discuss a series of observations about the PredNet and conclude that it does not completely follow the principles of the predictive coding framework.

With the unprecedented success of deep convolutional neural networks came the quest for training always deeper networks. However, while deeper neural networks give better performance when trained appropriately, that depth also translates in memory and computation heavy models, typically with tens of millions of parameters. Several methods have been proposed to leverage redundancies in the network to alleviate this complexity. Either a pretrained network is compressed, e.g. using a low-rank tensor decomposition, or the architecture of the network is directly modified to be more effective. In this paper, we study both approaches in a unified framework, under the lens of tensor decompositions. We show how tensor decomposition applied to the convolutional kernel relates to efficient architectures such as MobileNet. Moreover, we propose a tensor-based method for efficient higher order convolutions, which can be used as a plugin replacement for N-dimensional convolutions. We demonstrate their advantageous properties both theoretically and empirically for image classification, for both 2D and 3D convolutional networks.

While adversarial training can improve robust accuracy (against an adversary), it sometimes hurts standard accuracy (when there is no adversary). Previous work has studied this tradeoff between standard and robust accuracy, but only in the setting where no predictor performs well on both objectives in the infinite data limit. In this paper, we show that even when the optimal predictor with infinite data performs well on both objectives, a tradeoff can still manifest itself with finite data. Furthermore, since our construction is based on a convex learning problem, we rule out optimization concerns, thus laying bare a fundamental tension between robustness and generalization. Finally, we show that robust self-training mostly eliminates this tradeoff by leveraging unlabeled data.

In this paper we propose a method of obtaining points of extreme overfitting - parameters of modern neural networks, at which they demonstrate close to 100 % training accuracy, simultaneously with almost zero accuracy on the test sample. Despite the widespread opinion that the overwhelming majority of critical points of the loss function of a neural network have equally good generalizing ability, such points have a huge generalization error. The paper studies the properties of such points and their location on the surface of the loss function of modern neural networks.

Human domain experts solve difficult planning problems by drawing on years of experience. In many cases, computing a solution to such problems is computationally intractable or requires encoding heuristics from human domain experts. As codifying this knowledge leaves much to be desired, we aim to infer their strategies through observation. The challenge lies in that humans exhibit heterogeneity in their latent decision-making criteria. To overcome this, we propose a personalized apprenticeship learning framework that automatically infers a representation of all human task demonstrators by extracting a human-specific embedding. Our framework is built on a propositional architecture that allows for distilling an interpretable representation of each human demonstrator's decision-making.

Training neural networks with verifiable robustness guarantees is challenging. Several existing successful approaches utilize relatively tight linear relaxation based bounds of neural network outputs, but they can slow down training by a factor of hundreds and over-regularize the network. Meanwhile, interval bound propagation (IBP) based training is efficient and significantly outperform linear relaxation based methods on some tasks, yet it suffers from stability issues since the bounds are much looser. In this paper, we first interpret IBP training as training an augmented network which computes non-linear bounds, thus explaining its good performance. We then propose a new certified adversarial training method, CROWN-IBP, by combining the fast IBP bounds in the forward pass and a tight linear relaxation based bound, CROWN, in the backward pass. The proposed method is computationally efficient and consistently outperforms IBP baselines on training verifiably robust neural networks. We conduct large scale experiments using 53 models on MNIST, Fashion-MNIST and CIFAR datasets. On MNIST with $\epsilon=0.3$ and $\epsilon=0.4$ ($\ell_\infty$ norm distortion) we achieve 7.46\% and 12.96\% verified error on test set, respectively, outperforming previous certified defense methods.

The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform image-based unsupervised clustering or semi-supervised classification. Combining the power of these two generative models, we introduce Multi-Adversarial Variational autoEncoder Networks (MAVENs), a novel network architecture that incorporates an ensemble of discriminators in a VAE-GAN network, with simultaneous adversarial learning and variational inference. We apply MAVENs to the generation of synthetic images and propose a new distribution measure to quantify the quality of the generated images. Our experimental results using datasets from the computer vision and medical imaging domains---Street View House Numbers, CIFAR-10, and Chest X-Ray datasets---demonstrate competitive performance against state-of-the-art semi-supervised models both in image generation and classification tasks.