When optimizing transportation systems, anticipating traffic flows is a central element. Yet, computing such traffic equilibria remains computationally expensive. Against this background, we introduce a novel combinatorial optimization augmented neural network architecture that allows for fast and accurate traffic flow predictions. We propose WardropNet, a neural network that combines classical layers with a subsequent equilibrium layer: the first ones inform the latter by predicting the parameterization of the equilibrium problem's latency functions. Using supervised learning we minimize the difference between the actual traffic flow and the predicted output. We show how to leverage a Bregman divergence fitting the geometry of the equilibria, which allows for end-to-end learning. WardropNet outperforms pure learning-based approaches in predicting traffic equilibria for realistic and stylized traffic scenarios. On realistic scenarios, WardropNet improves on average for time-invariant predictions by up to 72% and for time-variant predictions by up to 23% over pure learning-based approaches.

The FI Data & Analytics Technology team is seeking an enthusiastic machine learning engineer to work on AI/ML solutions in support of our FI Business partners. Our team provides modern cloud-based data solutions for the broader FI organization that enables the Sales, Marketing, Finance, and AI/ML Analytics capabilities. This role will be a key contributor of solutions that support the Data Science Agile teams in the FI Advanced Analytics Solutions Product Area. As a Machine Learning Engineer, you will be responsible for developing cloud "infrastructure as code" components, shared Python frameworks, and data pipelines that integrate AI/ML model predictions with employee and customer facing applications. As a Software Engineer focused on AI/ML Model training and inference pipelines, you will utilize your technical capabilities along with your domain expertise to develop innovative solutions.

Research Internships at Microsoft provide a dynamic environment for research careers with a network of world-class research labs led by globally-recognized scientists and engineers. Our researchers and engineers pursue innovation in a range of scientific and technical disciplines to help solve complex challenges in diverse fields, including computing, healthcare, economics, and the environment. The Machine Learning and Statistics and AutoML groups at Microsoft Research New England are hiring multiple interns to work alongside leading researchers and engineers to advance the state-of-the-art in the field. Projects include applied and theoretical machine learning research on topics including (but not limited to): auto-ML, transfer learning, domain adaptation, program synthesis, meta-learning, sign language modeling, statistics, approximate inference, distribution compression, weather and climate forecasting, causal inference, causal machine learning, ML for health, adversarial ML, learning and incentives, reinforcement learning, multi-agent reinforcement learning, deep learning, supervised learning, and unsupervised learning. Responsibilities: Interns put inquiry and theory into practice.

Dotdash Meredith is the largest premium publisher in the world. Every day tens of millions of people come to us for help and inspiration. Our library of hundreds of thousands of articles help our users, and help us understand the evolving needs of a world recovering from the pandemic and dealing with day to day challenges. As a classification expert, you will work with this incredible dataset to group and classify our content, and understand our users' needs. This understanding will be used to power our content investments, user facing initiatives, and how we work with partners.

This paper analyzes the impact of COVID-19 related lockdowns in the Atlanta, Georgia metropolitan area by examining commuter patterns in three periods: prior to, during, and after the pandemic lockdown. A cellular phone location dataset is utilized in a novel pipeline to infer the home and work locations of thousands of users from the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The coordinates derived from the clustering are put through a reverse geocoding process from which word embeddings are extracted in order to categorize the industry of each work place based on the workplace name and Point of Interest (POI) mapping. Frequencies of commute from home locations to work locations are analyzed in and across all three time periods. Public health and economic factors are discussed to explain potential reasons for the observed changes in commuter patterns.

The proliferation of smartphones has accelerated mobility studies by largely increasing the type and volume of mobility data available. One such source of mobility data is from GPS technology, which is becoming increasingly common and helps the research community understand mobility patterns of people. However, there lacks a standardized framework for studying the different mobility patterns created by the non-Work, non-Home locations of Working and Nonworking users on Workdays and Offdays using machine learning methods. We propose a new mobility metric, Daily Characteristic Distance, and use it to generate features for each user together with Origin-Destination matrix features. We then use those features with an unsupervised machine learning method, $k$-means clustering, and obtain three clusters of users for each type of day (Workday and Offday). Finally, we propose two new metrics for the analysis of the clustering results, namely User Commonality and Average Frequency. By using the proposed metrics, interesting user behaviors can be discerned and it helps us to better understand the mobility patterns of the users.

Role requiring'No experience data provided' months of experience in None If you want to know more about the job, please contact me. Role requiring'No experience data provided' months of experience in None You'll find it at Relevate! Relevate, one of the leading providers of membership, billing, and Single Sign On (SSO) Dashboards for REALTOR associations and MLS organizations, is actively recruiting a Full Stack Web Developer to work for our 100% remote company. We don't care where you are; if you're great at building reliable, intuitive software, we want you on our team! Full Web Developer responsibilities include working with product management to design, build, and maintain an application for users to engage with their membership organization.