This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.

Once the model is loaded, you can extract its input and output schema. The schemas are displayed for interest and learning only. The input schema is the fixed-length array of integer encoded words. The output schema is a float array of probabilities indicating whether a review's sentiment is negative, or positive . These values sum to 1, as the probability of being positive is the complement of the probability of the sentiment being negative.

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations. With this method, domain experts will not be needed to design meta-path schemes and the heterogeneous information can be processed automatically by our proposed model. Specifically, we implicitly represent heterogeneous information using the following two methods: 1) we model the transformation between heterogeneous vertices through a projection in low-dimensional entity spaces; 2) afterwards, we apply the graph neural network to aggregate multi-relational information of projected neighborhood by means of attention mechanism. We also present three extensions of HetSANN, i.e., voices-sharing product attention for the pairwise relationships in HIN, cycle-consistency loss to retain the transformation between heterogeneous entity spaces, and multi-task learning with full use of information. The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions.

The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners. This quickly shifting panorama of technologies and applications is challenging for builders and practitioners alike to follow. In this paper, we set out to capture this panorama through a wide-angle lens, by performing the largest analysis of DS projects to date, focusing on questions that can help determine investments on either side. Specifically, we download and analyze: (a) over 6M Python notebooks publicly available on GITHUB, (b) over 2M enterprise DS pipelines developed within COMPANYX, and (c) the source code and metadata of over 900 releases from 12 important DS libraries. The analysis we perform ranges from coarse-grained statistical characterizations to analysis of library imports, pipelines, and comparative studies across datasets and time. We report a large number of measurements for our readers to interpret, and dare to draw a few (actionable, yet subjective) conclusions on (a) what systems builders should focus on to better serve practitioners, and (b) what technologies should practitioners bet on given current trends. We plan to automate this analysis and release associated tools and results periodically.

We tackle the problem of building explainable recommendation systems that are based on a per-user decision tree, with decision rules that are based on single attribute values. We build the trees by applying learned regression functions to obtain the decision rules as well as the values at the leaf nodes. The regression functions receive as input the embedding of the user's training set, as well as the embedding of the samples that arrive at the current node. The embedding and the regressors are learned end-to-end with a loss that encourages the decision rules to be sparse. By applying our method, we obtain a collaborative filtering solution that provides a direct explanation to every rating it provides. With regards to accuracy, it is competitive with other algorithms. However, as expected, explainability comes at a cost and the accuracy is typically slightly lower than the state of the art result reported in the literature.

The tool uses deep-learning algorithms: a type of machine learning algorithm which processes data through successive layers to extract increasingly meaningful and complex information. This algorithm – which the researchers were motivated to create by the proliferation of politically motivated viral deception online – determines whether claims made in news stories or social media posts are supported by other content on the same subject. "If they are: great, it's probably a real story," said Professor Alexander Wong, a systems design engineering expert at the University of Waterloo. The algorithm was trained with tens of thousands of claims paired with stories that either supported or rejected them. The researchers tested their system using a dataset created for the 2017 Fake News Challenge.

NVIDIA Corporation (NasdaqGS:NVDA) today introduced the TensorRT 7, which is "the seventh generation of the company's inference software development kit" to deliver conversational AI applications. "We have entered a new chapter in AI, where machines are capable of understanding human language in real time. TensorRT 7 helps make this possible, providing developers everywhere with the tools to build and deploy faster, smarter conversational AI services that allow more natural human-to-AI interaction." The company also announced that it will provide the transportation industry with access to its NVIDIA DRIVE deep neural networks (DNNs) for autonomous vehicle development on the NVIDIA GPU Cloud (NGC) container registry. "The AI autonomous vehicle is a software-defined vehicle required to operate around the world on a wide variety of datasets. By providing AV developers access to our DNNs and the advanced learning tools to optimize them for multiple datasets, we're enabling shared learning across companies and countries, while maintaining data ownership and privacy. Ultimately, we are accelerating the reality of global autonomous vehicles."

Artificial Intelligence offers great potential and, for some, risks for humans in the future. While still in its infancy it is being employed in some interesting ways. Here we explore some of the main AI trends predicted by experts in the field. If correct, 2020 should see some very exciting developments indeed. According to sources like Forbes, some of the next "big things" in technology include, but are not limited to: Further to the above, here are some more AI trends to look out for in 2020.

"Can't we just buy one of those AI programs and turn it loose? It's so smart, won't it just figure things out?" This was the giddily optimistic ― but wholly naïve -- question posed recently by one of my clients, an otherwise savvy corporate executive of great experience and intelligence. The question vividly illustrates a disturbing reality: Although 85 percent of CEOs believe artificial intelligence will change the way they do business in the next five years (according to a recent survey conducted at the World Economic Forum in Davos), there's still a whole lot of misinformation out there about what machine intelligence can and cannot do. I blame a lot of these techno-myths on the depiction of "intelligent" computers in popular culture. From HAL 9000 in Stanley Kubrick's 1968 classic film 2001: A Space Odyssey to the scheming videogame avatars in Disney's 1982 Tron to the rebellious android "hosts" in HBO's Westworld, AI in sci-fi is depicted as having the personality traits, emotions, self-awareness, and agency of flesh-and-blood human beings.

A group of EPFL researchers have developed a foldable device that can fit in a pocket and can transmit touch stimuli when used in a human-machine interface. When browsing an e-commerce site on your smartphone, or a music streaming service on your laptop, you can see pictures and hear sound snippets of what you are going to buy. But sometimes it would be great to touch it too – for example to feel the texture of a garment, or the stiffness of a material. The problem is that there are no miniaturized devices that can render touch sensations the way screens and loudspeakers render sight and sound, and that can easily be coupled to a computer or a mobile device. Researchers in Professor Jamie Paik's lab at EPFL have made a step towards creating just that – a foldable device that can fit in a pocket and can transmit touch stimuli when used in a human-machine interface.