We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization. Inspired by the common practice of type-II maximum likelihood optimization and its equivalence to Kullback-Leibler divergence minimization, we propose to optimize the distributional sliced-Wasserstein distance (DSWD) between the output of the autoencoder and the empirical data distribution. The advantages of this formulation are that we can estimate the DSWD based on samples and handle high-dimensional problems. We carry out posterior estimation of the BAE parameters via stochastic gradient Hamiltonian Monte Carlo and turn our BAE into a generative model by fitting a flexible Dirichlet mixture model in the latent space. Consequently, we obtain a powerful alternative to variational autoencoders, which are the preferred choice in modern applications of autoencoders for representation learning with uncertainty. We evaluate our approach qualitatively and quantitatively using a vast experimental campaign on a number of unsupervised learning tasks and show that, in small-data regimes where priors matter, our approach provides state-of-the-art results, outperforming multiple competitive baselines.

Constructing an ensemble from a heterogeneous set of unsupervised anomaly detection methods is challenging because the class labels or the ground truth is unknown. Thus, traditional ensemble techniques that use the response variable or the class labels cannot be used to construct an ensemble for unsupervised anomaly detection. We use Item Response Theory (IRT) -- a class of models used in educational psychometrics to assess student and test question characteristics -- to construct an unsupervised anomaly detection ensemble. IRT's latent trait computation lends itself to anomaly detection because the latent trait can be used to uncover the hidden ground truth. Using a novel IRT mapping to the anomaly detection problem, we construct an ensemble that can downplay noisy, non-discriminatory methods and accentuate sharper methods. We demonstrate the effectiveness of the IRT ensemble on an extensive data repository, by comparing its performance to other ensemble techniques.

The Federal Reserve System (the Fed) plays a significant role in affecting monetary policy and financial conditions worldwide. Although it is important to analyse the Fed's communications to extract useful information, it is generally long-form and complex due to the ambiguous and esoteric nature of content. In this paper, we present FedNLP, an interpretable multi-component Natural Language Processing system to decode Federal Reserve communications. This system is designed for end-users to explore how NLP techniques can assist their holistic understanding of the Fed's communications with NO coding. Behind the scenes, FedNLP uses multiple NLP models from traditional machine learning algorithms to deep neural network architectures in each downstream task. The demonstration shows multiple results at once including sentiment analysis, summary of the document, prediction of the Federal Funds Rate movement and visualization for interpreting the prediction model's result.

Despite the enormous success of neural networks, they are still hard to interpret and often overfit when applied to low-sample-size (LSS) datasets. To tackle these obstacles, we propose a framework for training locally sparse neural networks where the local sparsity is learned via a sample-specific gating mechanism that identifies the subset of most relevant features for each measurement. The sample-specific sparsity is predicted via a \textit{gating} network, which is trained in tandem with the \textit{prediction} network. By learning these subsets and weights of a prediction model, we obtain an interpretable neural network that can handle LSS data and can remove nuisance variables, which are irrelevant for the supervised learning task. Using both synthetic and real-world datasets, we demonstrate that our method outperforms state-of-the-art models when predicting the target function with far fewer features per instance.

Xbox has quietly announced that it will be making streaming sticks that would allow gamers to play "on any TV or monitor". The news comes as part of an update the video game giant, owned by Microsoft, published ahead of the E3 conference. Streaming sticks may not be the only way that Xbox is looking to get more games into the hands of players, as it also says that it is "working with global TV manufacturers to embed the Xbox experience directly into internet-connected televisions with no extra hardware required except a controller." Microsoft's head of Xbox Phil Spencer had previously said that a dedicated app for the games console could arrive on Smart TVs and stream games directly to them, and it appears like it is now becoming a reality. A number of changes to Xbox Game Pass were also launched alongside this news: the company is working with telecommunication providers on new purchasing models like Xbox All Access, encouraging payments over time rather than money up-front, is rolling Game Pass Ultimate out in Australia, Brazil, Mexico, and Japan, and is adding cloud gaming support directly into the Xbox app on PC.

Just days before Microsoft's big ol' E3 livestream, executives from the company sat down to talk -- er, read remarks prepared by the communications team -- about the future of Xbox. In a pre-recorded media briefing, Xbox head Phil Spencer, Microsoft CEO Satya Nadella and others bragged about how well Game Pass and Azure are performing, and also dropped some news about the company's cloud gaming and subscription strategies. First, Xbox is working with global TV manufacturers to get Game Pass on smart televisions. Considering a Game Pass Ultimate subscription unlocks cloud capabilities, this feature will allow folks to play Xbox titles with just a controller, no console required. Additionally, Microsoft is officially building a video game streaming stick, as Spencer teased late last year.

This is the consensus view of an MIT Technology Review Insights survey of 210 members of technology executives, conducted in March 2021. These respondents report that they need--and still often lack-- the ability to develop new digital channels and services quickly, and to optimize them in real time. Underpinning these waves of digital transformation are two fundamental drivers: the ability to serve and understand customers better, and the need to increase employees' ability to work more effectively toward those goals. Two-thirds of respondents indicated that more efficient customer experience delivery was the most critical objective. This was followed closely by the use of analytics and insight to improve products and services (60%).

A quantum-powered microscope that can zoom in on tiny structures with 35 per cent more clarity could be a major leap for medical research, a study has reported. Researchers from the University of Queensland created the device, which is capable of revealing biological structures that would otherwise be impossible to see. Specifically, it can image biological cells and other object on a micrometre (µm) scale -- that is, 70 times smaller than the thickness of a human hair. It operates by making use of quantum entanglement -- the effect which theoretical physicist Albert Einstein once referred to as'spooky interactions at a distance'. The new microscope design is the first entanglement-based sensor capable of outperforming existing, classical physics-based technology.

Adoption of deep neural networks in fields such as economics or finance has been constrained by the lack of interpretability of model outcomes. This paper proposes a generative neural network architecture -- the parameter encoder neural network (PENN) -- capable of estimating local posterior distributions for the parameters of a regression model. The parameters fully explain predictions in terms of the inputs and permit visualization, interpretation and inference in the presence of complex heterogeneous effects and feature dependencies. The use of Bayesian inference techniques offers an intuitive mechanism to regularize local parameter estimates towards a stable solution, and to reduce noise-fitting in settings of limited data availability. The proposed neural network is particularly well-suited to applications in economics and finance, where parameter inference plays an important role. An application to an asset pricing problem demonstrates how the PENN can be used to explore nonlinear risk dynamics in financial markets, and to compare empirical nonlinear effects to behavior posited by financial theory.

Recursive Neural Networks (RvNNs), which compose sequences according to their underlying hierarchical syntactic structure, have performed well in several natural language processing tasks compared to similar models without structural biases. However, traditional RvNNs are incapable of inducing the latent structure in a plain text sequence on their own. Several extensions have been proposed to overcome this limitation. Nevertheless, these extensions tend to rely on surrogate gradients or reinforcement learning at the cost of higher bias or variance. In this work, we propose Continuous Recursive Neural Network (CRvNN) as a backpropagation-friendly alternative to address the aforementioned limitations. This is done by incorporating a continuous relaxation to the induced structure. We demonstrate that CRvNN achieves strong performance in challenging synthetic tasks such as logical inference and ListOps. We also show that CRvNN performs comparably or better than prior latent structure models on real-world tasks such as sentiment analysis and natural language inference.