Recent developments in unsupervised representation learning have successfully established the concept of transfer learning in NLP. Mainly three forces are driving the improvements in this area of research: More elaborated architectures are making better use of contextual information. Instead of simply plugging in static pre-trained representations, these are learned based on surrounding context in end-to-end trainable models with more intelligently designed language modelling objectives. Along with this, larger corpora are used as resources for pre-training large language models in a self-supervised fashion which are afterwards fine-tuned on supervised tasks. Advances in parallel computing as well as in cloud computing, made it possible to train these models with growing capacities in the same or even in shorter time than previously established models. These three developments agglomerate in new state-of-the-art (SOTA) results being revealed in a higher and higher frequency. It is not always obvious where these improvements originate from, as it is not possible to completely disentangle the contributions of the three driving forces. We set ourselves to providing a clear and concise overview on several large pre-trained language models, which achieved SOTA results in the last two years, with respect to their use of new architectures and resources. We want to clarify for the reader where the differences between the models are and we furthermore attempt to gain some insight into the single contributions of lexical/computational improvements as well as of architectural changes. We explicitly do not intend to quantify these contributions, but rather see our work as an overview in order to identify potential starting points for benchmark comparisons. Furthermore, we tentatively want to point at potential possibilities for improvement in the field of open-sourcing and reproducible research.

Artificial neural networks (ANNs) have proven to be extremely useful for solving problems such as classification, regression, function estimation and dimensionality reduction. However, it turns out that different neural network architectures are able to achieve higher performances for certain problems. This article will provide an overview of the most common neural network architectures -- including recurrent neural networks and convolutional neural -- and how they can be implemented to aid blockchain technology. Convolutional neural networks (CNNs) are a type of neural network that is designed to capture increasingly more complex features within its input data. To do this, CNNs are constructed from a sequence of layers, each of which consists of a series of cube-shaped filters.

Serial cybersecurity entrepreneur Shlomo Kramer said in a 2005 interview that cybersecurity is "a bit like Alice in Wonderland" where you run as fast as you can only to stay in place. In 2020, to paraphrase the second part of the Red Queen's observation (actually from Through the Looking Glass), if you wish to stay ahead of cyber criminals, you must run twice--or ten times--as fast as that. The 141 predictions listed here reveal the state-of-mind of key participants in the cybersecurity defense industry and highlight all that's hot today. The future is murky, but we know for sure that on January 1, 2020, the California Consumer Privacy Act (CCPA) will go into effect; that the U.S. presidential election will take place on November 3, 2020; and that on October 1, 2020, if you "wish to fly on commercial aircrafts or access federal facilities" in the U.S., you must have a REAL ID compliant card. Other than these known events, the crystal balls of the participants in this survey warn us ...

We consider the problem of structure learning for Gaifman models and learn relational features that can be used to derive feature representations from a knowledge base. These relational features are first-order rules that are then partially grounded and counted over local neighborhoods of a Gaifman model to obtain the feature representations. We propose a method for learning these relational features for a Gaifman model by using relational tree distances. Our empirical evaluation on real data sets demonstrates the superiority of our approach over classical rule-learning.

Supervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However, the impact of label noise has not received sufficient attention. Recent studies have shown that label noise can significantly impact the performance of deep learning models in many machine learning and computer vision applications. This is especially concerning for medical applications, where datasets are typically small, labeling requires domain expertise and suffers from high inter- and intra-observer variability, and erroneous predictions may influence decisions that directly impact human health. In this paper, we first review the state-of-the-art in handling label noise in deep learning. Then, we review studies that have dealt with label noise in deep learning for medical image analysis. Our review shows that recent progress on handling label noise in deep learning has gone largely unnoticed by the medical image analysis community. To help achieve a better understanding of the extent of the problem and its potential remedies, we conducted experiments with three medical imaging datasets with different types of label noise. Based on the results of these experiments and our review of the literature, we make recommendations on methods that can be used to alleviate the effects of different types of label noise on deep models trained for medical image analysis. We hope that this article helps the medical image analysis researchers and developers in choosing and devising new techniques that effectively handle label noise in deep learning.

In this paper we develop a data-driven smoothing technique for high-dimensional and non-linear panel data models. We allow for individual specific (non-linear) functions and estimation with econometric or machine learning methods by using weighted observations from other individuals. The weights are determined by a data-driven way and depend on the similarity between the corresponding functions and are measured based on initial estimates. The key feature of such a procedure is that it clusters individuals based on the distance / similarity between them, estimated in a first stage. Our estimation method can be combined with various statistical estimation procedures, in particular modern machine learning methods which are in particular fruitful in the high-dimensional case and with complex, heterogeneous data. The approach can be interpreted as a \textquotedblleft soft-clustering\textquotedblright\ in comparison to traditional\textquotedblleft\ hard clustering\textquotedblright that assigns each individual to exactly one group. We conduct a simulation study which shows that the prediction can be greatly improved by using our estimator. Finally, we analyze a big data set from didichuxing.com, a leading company in transportation industry, to analyze and predict the gap between supply and demand based on a large set of covariates. Our estimator clearly performs much better in out-of-sample prediction compared to existing linear panel data estimators.

Consider a reference Markov process with initial distribution $\pi_{0}$ and transition kernels $\{M_{t}\}_{t\in[1:T]}$, for some $T\in\mathbb{N}$. Assume that you are given distribution $\pi_{T}$, which is not equal to the marginal distribution of the reference process at time $T$. In this scenario, Schr\"odinger addressed the problem of identifying the Markov process with initial distribution $\pi_{0}$ and terminal distribution equal to $\pi_{T}$ which is the closest to the reference process in terms of Kullback--Leibler divergence. This special case of the so-called Schr\"odinger bridge problem can be solved using iterative proportional fitting, also known as the Sinkhorn algorithm. We leverage these ideas to develop novel Monte Carlo schemes, termed Schr\"odinger bridge samplers, to approximate a target distribution $\pi$ on $\mathbb{R}^{d}$ and to estimate its normalizing constant. This is achieved by iteratively modifying the transition kernels of the reference Markov chain to obtain a process whose marginal distribution at time $T$ becomes closer to $\pi_T = \pi$, via regression-based approximations of the corresponding iterative proportional fitting recursion. We report preliminary experiments and make connections with other problems arising in the optimal transport, optimal control and physics literatures.

A new market assessment report on the Cognitive Computing market provides a comprehensive overview of the Cognitive Computing industry for the forecast period 2019 – 2026. The analytical study is proposed to provide immense clarity on the market size, share and growth rate across different regions. The study will also feature the key companies operating in the industry, their product/business portfolio, market share, financial status, regional share, segment revenue, SWOT analysis, key strategies including mergers & acquisitions, product developments, joint ventures & partnerships an expansions among others, and their latest news as well. The study will also provide a list of emerging players in the Cognitive Computing market. In this report, the global Cognitive Computing market is valued at USD xx million in 2019 and is expected to reach USD xx million by the end of 2026, growing at a CAGR of xx.x% between 2019 and 2026.

With increasing competition and pace in the financial markets, robust forecasting methods are becoming more and more valuable to investors. While machine learning algorithms offer a proven way of modeling non-linearities in time series, their advantages against common stochastic models in the domain of financial market prediction are largely based on limited empirical results. The same holds true for determining advantages of certain machine learning architectures against others. This study surveys more than 150 related articles on applying machine learning to financial market forecasting. Based on a comprehensive literature review, we build a table across seven main parameters describing the experiments conducted in these studies.

Recommender Systems (RS) have became a popular research topic and, since 2016, Deep Learning methods and techniques have been increasingly explored in this area. News RS are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. The main contribution of this research was named CHAMELEON, a Deep Learning meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks. As information about users' past interactions is scarce in the news domain, the user context can be leveraged to deal with the user cold-start problem. Articles' content is also important to tackle the item cold-start problem. Additionally, the temporal decay of items (articles) relevance is very accelerated in the news domain. Furthermore, external breaking events may temporally attract global readership attention, a phenomenon generally known as concept drift in machine learning. All those characteristics are explicitly modeled on this research by a contextual hybrid session-based recommendation approach using Recurrent Neural Networks. The task addressed by this research is session-based news recommendation, i.e., next-click prediction using only information available in the current user session. A method is proposed for a realistic temporal offline evaluation of such task, replaying the stream of user clicks and fresh articles being continuously published in a news portal. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based recommendation algorithms.