Graph-level representation learning is the pivotal step for downstream tasks that operate on the whole graph. The most common approach to this problem heretofore is graph pooling, where node features are typically averaged or summed to obtain the graph representations. However, pooling operations like averaging or summing inevitably cause massive information missing, which may severely downgrade the final performance. In this paper, we argue what is crucial to graph-level downstream tasks includes not only the topological structure but also the distribution from which nodes are sampled. Therefore, powered by existing Graph Neural Networks (GNN), we propose a new plug-and-play pooling module, termed as Distribution Knowledge Embedding (DKEPool), where graphs are rephrased as distributions on top of GNNs and the pooling goal is to summarize the entire distribution information instead of retaining a certain feature vector by simple predefined pooling operations. A DKEPool network de facto disassembles representation learning into two stages, structure learning and distribution learning. Structure learning follows a recursive neighborhood aggregation scheme to update node features where structure information is obtained. Distribution learning, on the other hand, omits node interconnections and focuses more on the distribution depicted by all the nodes. Extensive experiments demonstrate that the proposed DKEPool significantly and consistently outperforms the state-of-the-art methods.

Neurons in the brain are complex machines with distinct functional compartments that interact nonlinearly. In contrast, neurons in artificial neural networks abstract away this complexity, typically down to a scalar activation function of a weighted sum of inputs. Here we emulate more biologically realistic neurons by learning canonical activation functions with two input arguments, analogous to basal and apical dendrites. We use a network-in-network architecture where each neuron is modeled as a multilayer perceptron with two inputs and a single output. This inner perceptron is shared by all units in the outer network. Remarkably, the resultant nonlinearities often produce soft XOR functions, consistent with recent experimental observations about interactions between inputs in human cortical neurons. When hyperparameters are optimized, networks with these nonlinearities learn faster and perform better than conventional ReLU nonlinearities with matched parameter counts, and they are more robust to natural and adversarial perturbations.

In recent years, the transformer has established itself as a workhorse in many applications ranging from natural language processing to reinforcement learning. Similarly, Bayesian deep learning has become the gold-standard for uncertainty estimation in safety-critical applications, where robustness and calibration are crucial. Surprisingly, no successful attempts to improve transformer models in terms of predictive uncertainty using Bayesian inference exist. In this work, we study this curiously underpopulated area of Bayesian transformers. We find that weight-space inference in transformers does not work well, regardless of the approximate posterior. We also find that the prior is at least partially at fault, but that it is very hard to find well-specified weight priors for these models. We hypothesize that these problems stem from the complexity of obtaining a meaningful mapping from weight-space to function-space distributions in the transformer. Therefore, moving closer to function-space, we propose a novel method based on the implicit reparameterization of the Dirichlet distribution to apply variational inference directly to the attention weights. We find that this proposed method performs competitively with our baselines.

Although the potential of such technology to reduce the global disease burden of breast cancer is significant, there are a number of pitfalls in DeepMind's research that will hinder it from being universally adopted as a true replacement for a radiologist in the developing world. First is that the DeepMind team failed to disclose the technical architecture of their AI system. The second criticism is that DeepMind chose to exclusively use datasets from predominantly Caucasian patients. Although DeepMind made a strong case for the use of supplementary local data, clearly any attempt to create models that generalize globally require supplementary data from a number of diverse ethnic regions. Moreover, any models that are designed to generalize globally and be used in the developing world would require testing and validation in a variety of ethnogeographic regions.

Understanding customer sentiments is of paramount importance in marketing strategies today. Not only will it give companies an insight as to how customers perceive their products and/or services, but it will also give them an idea on how to improve their offers. This paper attempts to understand the correlation of different variables in customer reviews on a women clothing e-commerce, and to classify each review whether it recommends the reviewed product or not and whether it consists of positive, negative, or neutral sentiment. To achieve these goals, we employed univariate and multivariate analyses on dataset features except for review titles and review texts, and we implemented a bidirectional recurrent neural network (RNN) with long-short term memory unit (LSTM) for recommendation and sentiment classification. Results have shown that a recommendation is a strong indicator of a positive sentiment score, and vice-versa.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Graphs are everywhere around us. Your social network is a graph of people and relations. The roads you take to go from point A to point B constitute a graph. The links that connect this webpage to others form a graph.

A class-action lawsuit has been launched against DeepMind, the Google-owned AI research company, over its use of the personal records of 1.6 million patients from the UK's National Health Service (thanks, AI News). The health data was provided by the Royal Free London NHS Foundation Trust in 2015.DeepMind is known for several achievements, not least kicking everyone's ass at Starcraft 2, but it was given the records in order to create a health app called Streams. This was supposed to be an AI-powered assistant to healthcare professionals and has been used by the UK NHS—but no more. This August it was announced that Streams is being decommissioned, and DeepMind's own 'health' section now returns a server error.The handing-over of patient records to one of the world's biggest technology companies was exposed by New Scientist in 2017, in a report showing that DeepMind had access to far more data than had been publicly announced. The UK Information Commission launched an investigation that ruled the Royal Free hospital hadn't done enough to protect patients' privacy: following which, DeepMind apologised. "Our investigation found a number of shortcomings in the way patient records were shared for this trial," Information Commissioner Elizabeth Denham said at the time. "Patients would not have reasonably expected their information to have been used in this way."The new suit has been launched by lead plaintiff Andrew Prismall, who was a patient at the Royal Free hospital, and includes approximately 1.6 million other affected patients on an 'opt-out' basis—that is, all parties will be included in the action unless they request otherwise."Given the very positive experience of the NHS that I have always had during my various treatments, I was greatly concerned to find that a tech giant had ended up with my confidential medical records," said Prismall in a statement."As a patient having any sort of medical treatment, the last thing you would expect is your private medical records to be in the hands of one of the world’s biggest technology companies. I hope that this case will help achieve a fair outcome and closure for all of the patients whose confidential records were obtained in this instance without their knowledge or consent."

Distinguishing between artificial intelligence, machine learning, and deep learning has always been a demanding task especially for newbies in this field. It is therefore necessary to explain briefly, ways to help tell them apart. Artificial Intelligence(AI): In simple terms, AI is training computers to understand data as humans do. A machine is said to exhibit intelligent behavior whenever it solves a problem based on a defined set of rules called an algorithm. The algorithm could be a deep learning algorithm or a machine learning algorithm.

Hello guys, if you want to learn Natural Langauge Processing (NLP) and looking for the best online training courses then you have come to the right place. Earlier, I have shared the best courses to learn Data Science, Machine Learning, Tableau, and Power BI for Data visualization and In this article, I'll share the best online courses you can take online to learn Natural Langauge Processing or NLP. These are the best online courses from Udemy, Coursera, and Pluralsight, three of the most popular online learning platforms. They are created by experts and trusted by thousands of developers around the world and you can join them online to learn this in-demand skill from your home. Natural language processing is a science related to Artificial Intelligence and Computer Science that uses data to learn how to communicate like a human being and answer questions, translate texts, spell check, spam filtering, autocomplete, chatbots that you can interact with such as Siri and Alexa, and more applications.

The different types of neural networks in deep learning, such as convolutional neural networks (CNN), recurrent neural networks (RNN), artificial neural networks (ANN), etc. are changing the way we interact with the world. These different types of neural networks are at the core of the deep learning revolution, powering applications like unmanned aerial vehicles, self-driving cars, speech recognition, etc.