We thank all reviewers for their helpful comments and suggestions! We will address all minor issues. We will add references in the revision. UMG ( π) has cycles. We havn't thought about checking it from data, which is an interesting statistical Theorem 1 is effectively known.

They lower bound the minimal number of interventions required to orient any graph in terms of the size of the largest cliques in the essential graph of the causal model.

Well-established cognitive models coming from anthropology have shown that, due to the cognitive constraints that limit our "bandwidth" for social interactions, humans organize their social relations according to a regular structure. In this work, we postulate that similar regularities can be found in other cognitive processes, such as those involving language production. In order to investigate this claim, we analyse a dataset containing tweets of a heterogeneous group of Twitter users (regular users and professional writers). Leveraging a methodology similar to the one used to uncover the well-established social cognitive constraints, we find regularities at both the structural and semantic level. At the former, we find that a concentric layered structure (which we call ego network of words, in analogy to the ego network of social relationships) very well captures how individuals organise the words they use. The size of the layers in this structure regularly grows (approximately 2-3 times with respect to the previous one) when moving outwards, and the two penultimate external layers consistently account for approximately 60% and 30% of the used words, irrespective of the number of the total number of layers of the user. For the semantic analysis, each ring of each ego network is described by a semantic profile, which captures the topics associated with the words in the ring. We find that ring #1 has a special role in the model. It is semantically the most dissimilar and the most diverse among the rings. We also show that the topics that are important in the innermost ring also have the characteristic of being predominant in each of the other rings, as well as in the entire ego network. In this respect, ring #1 can be seen as the semantic fingerprint of the ego network of words.

Despite the improvement of translation quality, neural machine translation (NMT) often suffers from the lack of diversity in its generation. In this paper, we propose to generate diverse translations by deriving a large number of possible models with Bayesian modelling and sampling models from them for inference. The possible models are obtained by applying concrete dropout to the NMT model and each of them has specific confidence for its prediction, which corresponds to a posterior model distribution under specific training data in the principle of Bayesian modeling. With variational inference, the posterior model distribution can be approximated with a variational distribution, from which the final models for inference are sampled. We conducted experiments on Chinese-English and English-German translation tasks and the results shows that our method makes a better trade-off between diversity and accuracy.

Every year, ODSC West gets hundreds of submissions from an array of incredibly talented data science practitioners. Those submissions offer a unique insight into what are some of the most important topics in data science right now. In this article, we'll outline just a few of these topics that our speakers will be presenting on at ODSC West October 29th - November 1st. Deepfakes: Identified as one of the major threats to governments, politicians, businesses, and private individuals alike, deepfakes–both innocent and malevolent–have been dominating the news for several months. Originally only known as a side effect of GANs, deepfakes can create images, videos, and voice files capable of deceiving, at least initially, the general public.

While the hype of artificial intelligence (AI) and its potential role as a driver of transformational change to businesses and industries is pervasive, there are limited insights into what companies are actually doing to reap its benefits. The Artificial Intelligence Report, conducted by Ernst & Young, aims at getting a deeper understanding of how companies currently manage their AI activities, and how they address the current challenges and opportunities ahead. To get to the heart of the AI agenda, we received input from AI leaders in 277 companies, across 7 sectors and 15 countries in Europe, via surveys and/or interviews. Below is a brief summary of what they had to say. This is significantly higher than on the non-managerial / employee level where AI is only considered an important topic in 28% of the companies.

Ewan will be talking about the ethical considerations we take when implementing customer facing ML systems, or systems that interact with customer data. This is an increasingly important topic for our industry. This will be a talk from the practitioner's point of view, covering topics including: • Why ethics is an important topic for data scientists working in industry, and an essential problem for our companies. From the way that we log data, through to optimising towards the correct metrics. About the speaker Ewan has been working professionally with numbers and computers for the past 12 years.

In this exploration notebook, we shall try to uncover the basic information about the dataset which will help us build our models / features. The Dataset of this competition is an anonymized sample of over 3,000,000 grocery orders from more than 200,000 Instacart users. Now we have to predict which previously purchased products will be in a user's next order.

For a data scientist is essential to be familiar with the most important and current fields of research in machine learning and data mining. The algorithms in machine learning and data mining advance to a higher level of accuracy and flexibility and a data scientist should be prepared to implement the best algorithms and methods. The investigation of most common topics in machine learning and data mining provides an insight about the most relevant areas of research. To achieve this goal, I used the database of ScienceDirect.com. ScienceDirect has access to about 2,500 academic journals, more than 26,000 e-books and more than 13 million articles.

For a data scientist is essential to be familiar with the most important and current fields of research in machine learning and data mining. The algorithms in machine learning and data mining advance to a higher level of accuracy and flexibility and a data scientist should be prepared to implement the best algorithms and methods. The investigation of most common topics in machine learning and data mining provides an insight about the most relevant areas of research. To achieve this goal, I used the database of ScienceDirect.com. ScienceDirect has access to about 2,500 academic journals, more than 26,000 e-books and more than 13 million articles.