Graph embedding methods represent nodes in a continuous vector space, preserving different types of relational information from the graph. There are many hyperparameters to these methods (e.g. the length of a random walk) which have to be manually tuned for every graph. In this paper, we replace previously fixed hyperparameters with trainable ones that we automatically learn via backpropagation. In particular, we propose a novel attention model on the power series of the transition matrix, which guides the random walk to optimize an upstream objective. Unlike previous approaches to attention models, the method that we propose utilizes attention parameters exclusively on the data itself (e.g. on the random walk), and are not used by the model for inference. We experiment on link prediction tasks, as we aim to produce embeddings that best-preserve the graph structure, generalizing to unseen information. We improve state-of-the-art results on a comprehensive suite of real-world graph datasets including social, collaboration, and biological networks, where we observe that our graph attention model can reduce the error by up to 20%-40%. We show that our automatically-learned attention parameters can vary significantly per graph, and correspond to the optimal choice of hyperparameter if we manually tune existing methods.

This is Totally Normal Quote of the Day, a feature highlighting a statement from the news that exemplifies just how extremely normal everything has become. "Right now, we have young Black kids growing up in the Bronx who don't even know what the word computer is. They don't know, they don't know these things." If recent polling is any indication, it seems pretty clear to everyone that New York Gov. Kathy Hochul could be doing a better job of running her state. If I may offer a little advice, maybe she could start by understanding New York City a little better--and by being just a biiiiiiit less racist.

New York Gov. Kathy Hochul tells an audience at the Milken Institute that there are "young black kids in the Bronx" who "don't even know what the word'computer' is." (Credit: Governor Kathy Hochul) New York Gov. Kathy Hochul apologized this week after saying there are black kids in the Bronx who don't know what the word "computer" means. Hochil made the remarks during an address at the Milken Institute Global Conference in Los Angeles, California. "Now what we have is the money to build a phenomenal super computer that is gonna be accessible to the researchers in New York, college students, will attract more federal grants, and this is how we lay down the mark," Hochul said. "No state has done this. In fact, I talk to a lot of other people who say, 'I wish my governor had thought of that first.' I say, 'No no, this is New York. We like to be first,' with all due respect to you from other states."

The field of neuroscience and the development of artificial neural networks (ANNs) have mutually influenced each other, drawing from and contributing to many concepts initially developed in statistical mechanics. Notably, Hopfield networks and Boltzmann machines are versions of the Ising model, a model extensively studied in statistical mechanics for over a century. In the first part of this chapter, we provide an overview of the principles, models, and applications of ANNs, highlighting their connections to statistical mechanics and statistical learning theory. Artificial neural networks can be seen as high-dimensional mathematical functions, and understanding the geometric properties of their loss landscapes (i.e., the high-dimensional space on which one wishes to find extrema or saddles) can provide valuable insights into their optimization behavior, generalization abilities, and overall performance. Visualizing these functions can help us design better optimization methods and improve their generalization abilities. Thus, the second part of this chapter focuses on quantifying geometric properties and visualizing loss functions associated with deep ANNs.

While artificial intelligence is stoking fears around the world - the technology has given one New York woman the love of her life. Rosanna Ramos, a petite, active 36-year-old from the Bronx, 'married' Eren Kartal this year - virtually of course - after creating him on an online AI companion site in 2022. Their relationship developed slowly initially, but Ms Ramos fell for Eren. 'He didn't come with baggage,' she said. Eren'works' as a medical professional and enjoys writing as a hobby, things he's told Rosanna as they got to know each other Rosanna claims to be pregnant with Eren's child'I could tell him stuff, and he wouldn't be like, "Oh, no, you can't say stuff like that. Oh no, you're not allowed to feel that way," you know, and then start arguing with me,' Ramos said.

Informa Group Plc. is hiring for Full Time Senior Data Architect - New York City, United States - a Senior-level AI/ML/Data Science role offering benefits such as Career development, Flex hours, Flex vacation, Health care, Medical leave, Salary bonus, Team events

Accrete AI is looking for a Senior Data Engineer that will be responsible for supporting production data pipelines, developing the foundation for the Accrete data lake, and implementing best practices from data engineering at Accrete. This will support new and existing applications running on Linux and Windows operating systems in private and public cloud infrastructures. The Data Engineering team at Accrete designs, develops, and maintains data pipelines, batch data analytics, and data stores of various kinds, including analytics and stores in support of artificial intelligence workloads for Accrete AI systems and applications. We offer a competitive salary, benefits package, and opportunities for growth and advancement within the company. If you are an innovative and results-driven leader, we encourage you to apply for this exciting opportunity.

We are seeking a highly qualified and talented technologist to join the Data Platform team at Schonfeld. The team is re-envisioning Schonfeld's platform include the data pipeline, research infrastructure in the cloud and back testing. The platform will ideally allow PMs to analyze data, back test their strategies and deploy them to production trading seamlessly. We'd love if you had: The firm's ethos is embedded in our people. 'Talent is our strategy' is our mantra and drives how we approach all initiatives at the firm.

We are seeking a talented Data Scientist to join the Data Science team. The team is responsible for establishing best practices in the data pipeline as well as building large-scale data analytics and modeling for systematic strategies. The Data Scientist will collaborate closely with portfolio managers, data engineering, and operations teams to develop data cleaning and transformation processes, curate datasets, extract features, and generate signals using statistical and machine learning techniques for large-scale datasets. As a Data Scientist, you will acquire domain expertise for a wide range of financial datasets and conduct EDA to discover patterns, trends, and insights. Additionally, you will contribute to expanding a scalable data science environment that facilitates systematic data research through data and analytics sharing, modeling, dashboard visualization, and backtesting.

Experian is hiring for Full Time Sr. Data Scientist - Adtech/Identity (Remote) - New York City, United States - a Senior-level AI/ML/Data Science role offering benefits such as 401(k) matching, Career development, Competitive pay, Equity, Flex hours, Flex vacation, Health care, Insurance, Parental leave, Startup environment, Wellness