Articles for category: AI Research

[2411.08760] Energy Dissipation Preserving Physics Informed Neural Network for Allen-Cahn Equations

[Submitted on 13 Nov 2024 (v1), last revised 12 Mar 2025 (this version, v2)] View a PDF of the paper titled Energy Dissipation Preserving Physics Informed Neural Network for Allen-Cahn Equations, by Mustafa K\”ut\”uk and Hamdullah Y\”ucel View PDF HTML (experimental) Abstract:This paper investigates a numerical solution of Allen-Cahn equation with constant and degenerate mobility, with polynomial and logarithmic energy functionals, with deterministic and random initial functions, and with advective term in one, two, and three spatial dimensions, based on the physics-informed neural network (PINN). To improve the learning capacity of the PINN, we incorporate the energy dissipation property of

Google recommendations for the U.S. AI Action Plan

AI isn’t just a scientific breakthrough — it’s a breakthrough in how we make breakthroughs. Already we’re seeing how AI can revolutionize healthcare, accelerate scientific discovery, and transform our economy for the better. But now it’s up to us to seize the opportunity. And to do that, we’ll need the right policy frameworks to secure America’s position as an AI powerhouse and support a new era of opportunity. We share some ideas on that front in our response to the Office of Science and Technology Policy’s Request for Information. Our response recommends focusing on three key areas: Invest in AI.

Reddit – Heart of the internet

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Reddit – Heart of the internet

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

Reddit – Heart of the internet

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

Reddit – Heart of the internet

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

Catastrophic forgetting

I fine tuned easyOCR ln IAM word level dataset, and the model suffered from terrible catastrophic forgetting, it doesn't work well on OCR anymore, but performs relatively okay on HTR, it has an accuracy of 71% but the loss plot shows that it is over fitting a little I tried freezing layers, i tried a small learning rate of 0.0001 using adam optimizer, but it doesn't really seem to work, mind you iterations here does not mean epoch, instead it means a run through a batch instead of the full dataset, so 30000 iterations here is about 25 epochs. The

[D] Resources for AI infrastructure for system design

I'm preparing for an in-domain system design interview and the recruiter told me that part of it would be about how key AI model classes (mostly GenAI, RecSys and ranking) behave when parallelised over such an AI infrastructure, including communication primitives, potential bottlenecks etc. I'm not very familiar with this side of ML and I would appreciate any useful resources for my level. I know DL and ML very well so that's not an issue. I'm rather more concerned with the other stuff. Example questions are optimizing a cluster of GPUs for training an ML model or designing and serving

Encoding graphs for large language models

GraphQA focuses on simple tasks related to graphs, like checking if an edge exists, calculating the number of nodes or edges, finding nodes that are connected to a specific node, and checking for cycles in a graph. These tasks might seem basic, but they require understanding the relationships between nodes and edges. By covering different types of challenges, from identifying patterns to creating new connections, GraphQA helps models learn how to analyze graphs effectively. These basic tasks are crucial for more complex reasoning on graphs, like finding the shortest path between nodes, detecting communities, or identifying influential nodes. Additionally, GraphQA

Ideas: Building AI for population-scale systems with Akshay Nambi

AKSHAY NAMBI: Thanks for having me. STETKIEWICZ: I’d like to begin by asking you to tell us your origin story. How did you get started on your path? Was there a big idea or experience that captured your imagination or motivated you to do what you’re doing today? NAMBI: If I look back, my journey into research wasn’t a straight line. It was more about discovering my passion through some unexpected opportunities and also finding purpose along the way. So before I started with my undergrad studies, I was very interested in electronics and systems. My passion for electronics, kind