We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner. GraphCL learns node embeddings by maximizing the similarity between the representations of two randomly perturbed versions of the intrinsic features and link structure of the same node's local subgraph. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. In both transductive and inductive learning setups, we demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks.

Anti-money laundering (AML) is expensive and labour-intensive, and artificial intelligence (AI) can offer improved efficiency gains. Could they be a match made in heaven? This Risk.net webinar, in association with NICE Actimize, took place amid the strain on banks' back offices driven by the lockdown in response to the global Covid‑19 pandemic, and explores this potential pairing Today's evolving regulatory environment and criminal typologies have influenced AML compliance teams to adopt AI technologies such as machine learning to improve detection and better focus analyst workloads. The marriage of AI to existing compliance processes and risk modelling techniques has the potential to eliminate backlogs and create new efficiencies. But there may be some risks and question marks for those in the early stages of adoption. The strain on many financial institutions has only increased in 2020 due to the unexpected arrival of Covid‑19.

Scientists have recorded the first documented evidence of a great white shark attacking and killing an enormous humpback whale. Video taken from a drone off the coast of South Africa shows the 13-foot long shark hunting the whale which was around 33ft long and in ill health. Ryan Johnson, a marine biologist who observed the massacre, says the ordeal lasted about 50 minutes before the whale eventually died. Mr Johnson says the great white was very strategic in taking down the behemoth, and targeted its most vulnerable area, on the tail, before drowning the ailing whale. The great white is believed to be a shark called Helen which was named and tagged as part of a 2013 study by Mr Johnson.

The Machine Learning report provides independent information about the Machine Learning industry supported by extensive research on factors such as industry segments size & trends, inhibitors, dynamics, drivers, opportunities & challenges, environment & policy, cost overview, porter's five force analysis, and key companies profiles including business overview and recent development. The research report on Machine Learning market thoroughly investigates historical data of this business sphere to lay out the future roadmap of the industry. The study attempts to predict a long-term picture of the market scenario with respect to the various growth indicators, hindrances, and opportunities that determine the industry expansion. Moreover, the report provides an exhaustive synopsis of the industry at a global and regional level. In addition, it covers the impact of COVID-19 pandemic on the leading industry players and various market segmentations.

Despite the success of existing tensor factorization methods, most of them conduct a multilinear decomposition, and rarely exploit powerful modeling frameworks, like deep neural networks, to capture a variety of complicated interactions in data. More important, for highly expressive, deep factorization, we lack an effective approach to handle streaming data, which are ubiquitous in real-world applications. To address these issues, we propose SPIDER, a Streaming ProbabilistIc Deep tEnsoR factorization method. We first use Bayesian neural networks (NNs) to construct a deep tensor factorization model. We assign a spike-and-slab prior over the NN weights to encourage sparsity and prevent overfitting. We then use Taylor expansions and moment matching to approximate the posterior of the NN output and calculate the running model evidence, based on which we develop an efficient streaming posterior inference algorithm in the assumed-density-filtering and expectation propagation framework. Our algorithm provides responsive incremental updates for the posterior of the latent factors and NN weights upon receiving new tensor entries, and meanwhile select and inhibit redundant/useless weights. We show the advantages of our approach in four real-world applications.

Characterising intractable high-dimensional random variables is one of the fundamental challenges in stochastic computation. The recent surge of transport maps offers a mathematical foundation and new insights for tackling this challenge by coupling intractable random variables with tractable reference random variables. This paper generalises a recently developed functional tensor-train (FTT) approximation of the inverse Rosenblatt transport [14] to a wide class of high-dimensional nonnegative functions, such as unnormalised probability density functions. First, we extend the inverse Rosenblatt transform to enable the transport to general reference measures other than the uniform measure. We develop an efficient procedure to compute this transport from a squared FTT decomposition which preserves the monotonicity. More crucially, we integrate the proposed monotonicity-preserving FTT transport into a nested variable transformation framework inspired by deep neural networks. The resulting deep inverse Rosenblatt transport significantly expands the capability of tensor approximations and transport maps to random variables with complicated nonlinear interactions and concentrated density functions. We demonstrate the efficacy of the proposed approach on a range of applications in statistical learning and uncertainty quantification, including parameter estimation for dynamical systems and inverse problems constrained by partial differential equations.

In this paper, we investigate the limiting behavior of a continuous-time counterpart of the Stochastic Gradient Descent (SGD) algorithm applied to two-layer overparameterized neural networks, as the number or neurons (ie, the size of the hidden layer) $N \to +\infty$. Following a probabilistic approach, we show 'propagation of chaos' for the particle system defined by this continuous-time dynamics under different scenarios, indicating that the statistical interaction between the particles asymptotically vanishes. In particular, we establish quantitative convergence with respect to $N$ of any particle to a solution of a mean-field McKean-Vlasov equation in the metric space endowed with the Wasserstein distance. In comparison to previous works on the subject, we consider settings in which the sequence of stepsizes in SGD can potentially depend on the number of neurons and the iterations. We then identify two regimes under which different mean-field limits are obtained, one of them corresponding to an implicitly regularized version of the minimization problem at hand. We perform various experiments on real datasets to validate our theoretical results, assessing the existence of these two regimes on classification problems and illustrating our convergence results.

The first five months of 2020 sent a parade of "wicked problems" around the globe, including a plague of locusts in Asia and Africa, bushfires in Australia and, of course, the COVID-19 pandemic. Wicked problems can be defined as problems that no one knows how to solve without creating further problems. We struggle to mitigate them because they transcend borders and generations. During and after World War II, policymakers also confronted significant problems, such as how to keep the peace, encourage recovery and prevent starvation. They tackled these problems by creating collaborative institutions and rules, and by providing generous aid and technical assistance.

Automating tasks previously done by hand to simplify and enhance production is nothing new. Humans have been doing it since 350 BCE when the first waterwheels for processing grain were recorded in Syria and Egypt. Today, many businesses are embracing technology to automate manual processes, generating a 30-200 per cent return of investment in the first year. With 74 per cent of organisations actively looking for new use cases for automation it's no surprise that by 2022 it's estimated that 42 per cent of total task hours will be completed by machines. There are several ways in which businesses can harness the power of automation to achieve competitive advantage, and it is important that businesses gain a clearer understanding of automation; its benefits and applications.

Researchers from Google's DeepMind and the University of Oxford recommend that AI practitioners draw on decolonial theory to reform the industry, put ethical principles into practice, and avoid further algorithmic exploitation or oppression. The researchers detailed how to build AI systems while critically examining colonialism and colonial forms of AI already in use in a preprint paper released Thursday. The paper was coauthored by DeepMind research scientists William Isaac and Shakir Mohammed and Marie-Therese Png, an Oxford doctoral student and DeepMind Ethics and Society intern who previously provided tech advice to the United Nations Secretary General's High-level Panel on Digital Cooperation. The researchers posit that power is at the heart of ethics debates and that conversations about power are incomplete if they do not include historical context and recognize the structural legacy of colonialism that continues to inform power dynamics today. They further argue that inequities like racial capitalism, class inequality, and heteronormative patriarchy have roots in colonialism and that we need to recognize these power dynamics when designing AI systems to avoid perpetuating such harms.