In this paper, we prove that some Gaussian structural equation models with dependent errors having equal variances are identifiable from their corresponding Gaussian distributions. Specifically, we prove identifiability for the Gaussian structural equation models that can be represented as Andersson-Madigan-Perlman chain graphs (Andersson et al., 2001). These chain graphs were originally developed to represent independence models. However, they are also suitable for representing causal models with additive noise (Pe\~{n}a, 2016. Our result implies then that these causal models can be identified from observational data alone. Our result generalizes the result by Peters and B\"{u}hlmann (2014), who considered independent errors having equal variances. The suitability of the equal error variances assumption should be assessed on a per domain basis.

Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of "neighboring" nodes that a node's representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture -- jumping knowledge (JK) networks -- that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.

In this paper, we present a database of emotional speech intended to be open-sourced and used for synthesis and generation purpose. It contains data for male and female actors in English and a male actor in French. The database covers 5 emotion classes so it could be suitable to build synthesis and voice transformation systems with the potential to control the emotional dimension in a continuous way. We show the data's efficiency by building a simple MLP system converting neutral to angry speech style and evaluate it via a CMOS perception test. Even though the system is a very simple one, the test show the efficiency of the data which is promising for future work.

This work presents a hierarchical deep learning natural language parser for fashion. Our proposal intends not only to recognize fashion-domain entities but also to expose syntactic and morphologic insights. We leverage the usage of an architecture of specialist models, each one for a different task (from parsing to entity recognition). Such architecture renders a hierarchical model able to capture the nuances of the fashion language. The natural language parser is able to deal with textual ambiguities which are left unresolved by our currently existing solution. Our empirical results establish a robust baseline, which justifies the use of hierarchical architectures of deep learning models while opening new research avenues to explore.

CASP is an extension of ASP that allows for numerical constraints to be added in the rules. PDDL+ is an extension of the PDDL standard language of automated planning for modeling mixed discrete-continuous dynamics. In this paper, we present CASP solutions for dealing with PDDL+ problems, i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP CASP solver in order to solve CASP programs arising from PDDL+ domains. An experimental analysis, performed on well-known linear and non-linear variants of PDDL+ domains, involving various configurations of the EZCSP solver, other CASP solvers, and PDDL+ planners, shows the viability of our solution.

Many neural networks use the tanh activation function, however when given a probability distribution as input, the problem of computing the output distribution in neural networks with tanh activation has not yet been addressed. One important example is the initialization of the echo state network in reservoir computing, where random initialization of the reservoir requires time to wash out the initial conditions, thereby wasting precious data and computational resources. Motivated by this problem, we propose a novel solution utilizing a moment based approach to propagate uncertainty through an Echo State Network to reduce the washout time. In this work, we contribute two new methods to propagate uncertainty through the tanh activation function and propose the Probabilistic Echo State Network (PESN), a method that is shown to have better average performance than deterministic Echo State Networks given the random initialization of reservoir states. Additionally we test single and multi-step uncertainty propagation of our method on two regression tasks and show that we are able to recover similar means and variances as computed by Monte-Carlo simulations.

The essential graph is a distinguished member of a Markov equivalence class of AMP chain graphs. However, the directed edges in the essential graph are not necessarily strong or invariant, i.e. they may not be shared by every member of the equivalence class. Likewise for the undirected edges. In this paper, we develop a procedure for identifying which edges in an essential graph are strong. We also show how this makes it possible to bound some causal effects when the true chain graph is unknown.

A little over a week after the fervor surrounding Google's involvement in the Department of Defense's Project Maven, an autonomous drone program, showed signs of abating, another machine learning controversy returned to the headlines: local law enforcement deploying Amazon's Rekognition, a computer vision service with facial recognition capabilities. In a letter addressed to Amazon CEO Jeff Bezos, 19 groups of shareholders expressed concerns that Rekognition's facial recognition capabilities will be misused in ways that "violate [the] civil and human rights" of "people of color, immigrants, and civil society organizations." And they said that it set the stage for sales of the software to foreign governments and authoritarian regimes. Amazon, for its part, said in a statement that it will "suspend … customer's right to use … services [like Rekognition]" if it determines those services are being "abused." It has so far declined, however, to define the bright-line rules that would trigger a suspension.

Members of the scientific and military communities from approximately 20 NATO countries came together to discuss leading developments in big data and artificial intelligence (AI) at the NATO Science and Technology Organization's specialists meeting, Big Data and Artificial Intelligence for Military Decision Making, from May 30 – June 01, 2018 in Bordeaux, France. Over the three day seminar, approximately 300 experts and researchers in data, artificial intelligence, modeling and simulation, and military operations discussed how technology, data and machine learning are increasingly influencing the trajectory of modern warfare – and the inherent risks and challenges involved. The meeting's purpose was to better inform technology acquisition, funding, and operational decisions within the NATO community. Specific topics focused on systems and operations, human-machine interface, security, and enabling technologies. Secretary of the Air Force for Operational Energy, Roberto Guerrero, led the senior leader panel on June 01.

With the advent of new technologies, experts now say the definition of intelligence is changing. Smart people are not just individuals capable of solving complicated problems on their own, but also those who understand the way artificial intelligence, or AI, can best serve them. Simply put, understanding technology is essential. Yet technology and artificial intelligence often scare people who get tangled in complicated explanations of what AI is and how it works. Two professors, Nick Polson from the University of Chicago Booth School of Business and James Scott from the University of Texas at Austin, tried to put a face on the technology by writing a book that illustrates the beginning of AI through several examples of historical figures and other individuals who developed algorithms for humanity's different problems.