We derive a new variational formula for the R{\'e}nyi family of divergences, $R_\alpha(Q\|P)$, between probability measures $Q$ and $P$. Our result generalizes the classical Donsker-Varadhan variational formula for the Kullback-Leibler divergence. We further show that this R{\'e}nyi variational formula holds over a range of function spaces; this leads to a formula for the optimizer under very weak assumptions and is also key in our development of a consistency theory for R{\'e}nyi divergence estimators. By applying this theory to neural network estimators, we show that if a neural network family satisfies one of several strengthened versions of the universal approximation property then the corresponding R{\'e}nyi divergence estimator is consistent. In contrast to likelihood-ratio based methods, our estimators involve only expectations under $Q$ and $P$ and hence are more effective in high dimensional systems. We illustrate this via several numerical examples of neural network estimation in systems of up to 5000 dimensions.

The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass. Evaluated on WebQSP and GraphQuestions with extended annotations that cover multiple entities per question, ELQ outperforms the previous state of the art by a large margin of +12.7% and +19.6% F1, respectively. With a very fast inference time (1.57 examples/s on a single CPU), ELQ can be useful for downstream question answering systems. In a proof-of-concept experiment, we demonstrate that using ELQ significantly improves the downstream QA performance of GraphRetriever (arXiv:1911.03868). Code and data available at https://github.com/facebookresearch/BLINK/tree/master/elq

Torchattacks is a PyTorch (Paszke et al. 2019) library that contains adversarial attacks to generate adversarial examples and to verify the robustness of deep learning models. Since Szegedy et al. (2013) found that deep learning models are vulnerable to the perturbed examples with small noises, called adversarial examples, various adversarial attacks have been continuously proposed. In this technical report, we provide a list of implemented adversarial attacks and explain the algorithms of each method. Here are some important things to check before generating adversarial examples. To make it easy to use adversarial attacks, a reverse-normalization is not included in the attack process.

Recently, cyber-attacks have been extensively seen due to the everlasting increase of malware in the cyber world. These attacks cause irreversible damage not only to end-users but also to corporate computer systems. Ransomware attacks such as WannaCry and Petya specifically targets to make critical infrastructures such as airports and rendered operational processes inoperable. Hence, it has attracted increasing attention in terms of volume, versatility, and intricacy. The most important feature of this type of malware is that they change shape as they propagate from one computer to another. Since standard signature-based detection software fails to identify this type of malware because they have different characteristics on each contaminated computer. This paper aims at providing an image augmentation enhanced deep convolutional neural network (CNN) models for the detection of malware families in a metamorphic malware environment. The main contributions of the paper's model structure consist of three components, including image generation from malware samples, image augmentation, and the last one is classifying the malware families by using a convolutional neural network model. In the first component, the collected malware samples are converted binary representation to 3-channel images using windowing technique. The second component of the system create the augmented version of the images, and the last component builds a classification model. In this study, five different deep convolutional neural network model for malware family detection is used.

We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language. The former refers to the logical consistency between coarse- and fine-grained predictions, which is used to regularize the training process with propositional Boolean expressions. The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection. Experiments show that our method achieves superior performance, demonstrating that leveraging declarative knowledge can help the model to make more accurate predictions.

Sometime between March 2010 and May 2012, a meteor streaked across the Martian sky and broke into pieces, slamming into the planet's surface. The resulting craters were relatively small - just 13 feet (4 meters) in diameter. The smaller the features, the more difficult they are to spot using Mars orbiters. But in this case - and for the first time - scientists spotted them with a little extra help: artificial intelligence (AI). It's a milestone for planetary scientists and AI researchers at NASA's Jet Propulsion Laboratory in Southern California, who worked together to develop the machine-learning tool that helped make the discovery.

While this Putin looks and sounds remarkably like the Russian president, the real Putin never spoke these works. That's because this video is a deepfake, an algorithmically generated video that can make a subject realistically look like they're saying something they never really said. A nonpartisan nonprofit is using this fake video of Putin to convince people to vote. The group, called RepresentUs, recently released a pair of deepfake video ads featuring Putin and North Korean leader Kim Jong Un talking about how they need to do nothing at all to watch U.S. democracy disintegrate–that is, unless Americans exercise their right to vote. Forty percent of eligible voters did not vote in the 2016 election, according to Pew Research.

A new crop of startups are developing AI systems to tackle challenges from climate change to ... [ ] COVID-19. Artificial Intelligence is transforming the world at a rapid and accelerating pace, offering huge potential, but also posing social and economic challenges. Human beings are naturally fearful of machines – this is a constant. Technological advancements tend to outpace cultural shifts. It has taken the shock of a global pandemic to accelerate the uptake of many technologies that have been around for at least a decade.

A viral video shows a presidential candidate changing her stance on a crucial issue the day before voters head to the polls. A video admitted as evidence in a court case shows a man entering a building where a crime was later committed. These are some of the ways malicious actors could use deepfakes--video or audio clips manipulated through artificial intelligence--to compromise a business, put innocents behind bars or interfere in the electoral process.