Triangular flows, also known as Kn\"{o}the-Rosenblatt measure couplings, comprise an important building block of normalizing flow models for generative modeling and density estimation, including popular autoregressive flow models such as real-valued non-volume preserving transformation models (Real NVP). We present statistical guarantees and sample complexity bounds for triangular flow statistical models. In particular, we establish the statistical consistency and the finite sample convergence rates of the Kullback-Leibler estimator of the Kn\"{o}the-Rosenblatt measure coupling using tools from empirical process theory. Our results highlight the anisotropic geometry of function classes at play in triangular flows, shed light on optimal coordinate ordering, and lead to statistical guarantees for Jacobian flows. We conduct numerical experiments on synthetic data to illustrate the practical implications of our theoretical findings.

We propose to model matrix time series based on a tensor CP-decomposition. Instead of using an iterative algorithm which is the standard practice for estimating CP-decompositions, we propose a new and one-pass estimation procedure based on a generalized eigenanalysis constructed from the serial dependence structure of the underlying process. A key idea of the new procedure is to project a generalized eigenequation defined in terms of rank-reduced matrices to a lower-dimensional one with full-ranked matrices, to avoid the intricacy of the former of which the number of eigenvalues can be zero, finite and infinity. The asymptotic theory has been established under a general setting without the stationarity. It shows, for example, that all the component coefficient vectors in the CP-decomposition are estimated consistently with the different error rates, depending on the relative sizes between the dimensions of time series and the sample size. The proposed model and the estimation method are further illustrated with both simulated and real data; showing effective dimension-reduction in modelling and forecasting matrix time series.

The engagement of employees at the workplace is one of the main ingredients for company growth. Therefore, the motivational systems that encourage engagement in the staff can significantly boost the realization of development aids. With the births ranging from the late 1990s till 2010s, the persons from Generation Z started or soon will start their first jobs in companies. High productivity of employees from this generation can be achieved by crafting a proper motivation system. Such a system must also be designed to tie the employee with the company since otherwise, the experience will be lost during the work rotation.

This thesis reviews numerical optimization methods with machine learning problems in mind. Since machine learning models are highly parametrized, we focus on methods suited for high dimensional optimization. We build intuition on quadratic models to figure out which methods are suited for non-convex optimization, and develop convergence proofs on convex functions for this selection of methods. With this theoretical foundation for stochastic gradient descent and momentum methods, we try to explain why the methods used commonly in the machine learning field are so successful. Besides explaining successful heuristics, the last chapter also provides a less extensive review of more theoretical methods, which are not quite as popular in practice. So in some sense this work attempts to answer the question: Why are the default Tensorflow optimizers included in the defaults?

An improvement in technology is linearly related to time and time-relevant problems. It has been seen that as time progresses, the number of problems humans face also increases. However, technology to resolve these problems tends to improve as well. One of the earliest existing problems which started with the invention of vehicles was parking. The ease of resolving this problem using technology has evolved over the years but the problem of parking still remains unsolved. The main reason behind this is that parking does not only involve one problem but it consists of a set of problems within itself. One of these problems is the occupancy detection of the parking slots in a distributed parking ecosystem. In a distributed system, users would find preferable parking spaces as opposed to random parking spaces. In this paper, we propose a web-based application as a solution for parking space detection in different parking spaces. The solution is based on Computer Vision (CV) and is built using the Django framework written in Python 3.0. The solution works to resolve the occupancy detection problem along with providing the user the option to determine the block based on availability and his preference. The evaluation results for our proposed system are promising and efficient. The proposed system can also be integrated with different systems and be used for solving other relevant parking problems.

Machine learning systems often experience a distribution shift between training and testing. In this paper, we introduce a simple variational objective whose optima are exactly the set of all representations on which risk minimizers are guaranteed to be robust to any distribution shift that preserves the Bayes predictor, e.g., covariate shifts. Our objective has two components. First, a representation must remain discriminative for the task, i.e., some predictor must be able to simultaneously minimize the source and target risk. Second, the representation's marginal support needs to be the same across source and target. We make this practical by designing self-supervised learning methods that only use unlabelled data and augmentations to train robust representations. Our objectives achieve state-of-the-art results on DomainBed, and give insights into the robustness of recent methods, such as CLIP. It is hard to build machine learning (ML) systems that are robust to distribution shifts ...

There are many concerns about increasing reliance on technology. Nevertheless, as a society, we continue to push technology to new heights. From how we order food to how we provide healthcare, machine learning, and artificial intelligence continue to help us surpass our wildest dreams. This is very important, especially if early detection and treatment can bring the best results. Using such an algorithm can literally save lives. Some universities have found through the creation of databases that their artificial intelligence can do as well as doctors in the diagnosis process but also do better in early detection. Artificial Intelligence can also give diagnosis suggestions based on the structured data entered by symptoms, give medication suggestions based on the diagnosis code, and predict adverse drug reactions based on other medications taken. Organizations such as Health Catalyst are using artificial intelligence to reduce hospital-acquired infections (HAI). If we can detect these dangerous infections early, we can reduce the mortality and morbidity associated with them. With the help of machine learning, the company is working to understand medical issues through crowdsourcing better. Having a larger and more diverse database can help the research be more accurate.

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and a few more.

When machine learning initially emerged, many speculated that it would spark another industrial revolution. Fast forward to today and many would say that it's nothing more than a buzzword. Machine learning is a useful tool, but it's nothing more than that. And it's a stretch to say that it's anything like a swiss army knife -- I'd think of it more like a water jet (something rather niche). From my experiences, there are certainly a number of applications where machine learning shines.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Active Learning is one of the teaching strategies which engage learners (e.g.