Mario Meir-Huber

The Rails 8 feature set perfectly complements SQLite's power in creating resilient, high-performance production apps, but still the question lingers: Can I really go all-in on #SQLite? Join Stephen Margheim at #RailsWorld as he illustrates how to leverage Rails and SQLite's full potential in your next venture, and when SQLite does and doesn't make sense for your application. https://lnkd.in/eaBstScy

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AnchorGT: A Novel Attention Architecture for Graph Transformers as a Flexible Building Block to Improve the Scalability of a Wide Range of Graph Transformer Models Quick read: https://lnkd.in/gFiQyv8a Paper: https://lnkd.in/gsJqpi5F #machinelearning #ai

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🚀 Exciting News! 🚀 We are thrilled to announce that Kumo AI's position paper "Relational Deep Learning: Graph Representation Learning on Relational Databases," has been accepted at #ICML 2024! 🎉 Join us in Vienna from July 21-27, where our very own Matthias Fey will be presenting this exciting new research area. 🔗 https://icml.cc/ In collaboration with Stanford University, we introduce Relational Deep Learning (#RDL), a new research area for applying deep learning on relational databases by viewing them as temporal, heterogeneous graphs. This method eliminates the need for manual feature engineering, leading to more accurate models built faster. 🚀 We’ve also developed #RelBench, a benchmark suite to facilitate research in this new area, using diverse datasets from Stack Exchange discussions to Amazon book reviews. 📚 Stay tuned for more updates on our presentation format, and check out our paper here: https://lnkd.in/dk24vbSg #ICML2024 #MachineLearning #AI #GraphNeuralNetworks #RelationalDeepLearning #DataScience #KumoAI #StanfordCollaboration

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Ever looked at Support Vector Machines (SVMs) and thought it was like deciphering an alien language? You're not alone! Here’s my personal breakdown of why these complex-sounding beasts are actually fascinating tools that are reshaping the landscape of machine learning: Nonlinear Ninjas: Forget simple lines; SVMs excel where others falter, uncovering patterns in the most chaotic of data. They’re not afraid of the nonlinear battle. 🥋 Master Separators: With the precision of a skilled artist, SVMs draw not just lines, but superhighways between data points, ensuring minimal errors and maximum clarity. It's all about finding that perfect boundary. 🛣️ Versatility Vibes: From categorizing images to forecasting the next big market trend, SVMs are the Swiss Army knives in the toolkit of data scientists. Their adaptability is simply unmatched. 🔍 The Magic of Math: At their heart, SVMs are powered by something akin to mathematical sorcery, with dot products and the mesmerizing kernel trick enabling operations in expansive dimensional spaces as if it were a walk in the park. It's math, but not as you know it. ✨ Real-World Warriors: In the messy battlefield of real-world data, SVMs stand tall, using slack variables to gracefully manage imperfections. They’re not just algorithms; they’re adaptable heroes ready for whatever chaos comes their way. 🌪️ SVMs have transformed from a topic I once approached with trepidation to one of awe-inspiring appreciation. The blend of sophisticated mathematics and practical application makes them a standout star in the machine learning universe. Curious to unlock the potential of SVMs in your projects? Join me in exploring the depths of these powerful tools and discover how they can elevate your analytical abilities to new heights. #DataScience #TechInnovation

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🚀 𝗢𝘂𝘁 𝗻𝗼𝘄: 𝗧𝘄𝗼 𝗵𝗲𝗶𝘀𝗲 𝗮𝗿𝘁𝗶𝗰𝗹𝗲𝘀 𝗼𝗻 𝗟𝗟𝗠 Alexander Schatten, senior researcher at SBA Research, and Marco Jeppe Reiser have published an interesting article: 📰 𝗪𝗵𝗮𝘁 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗮𝘁 𝗽𝗿𝗼𝗺𝗽𝘁 𝗹𝗲𝘃𝗲𝗹 𝗹𝗲𝗮𝗱𝘀 𝘁𝗼 Where can AI systems really help programmers, and is the approach taken here truly novel? This article examines their role. Programming involves interpreting real-world problems and processes, abstracting them, and then translating them into a deterministic language understood by technical systems. The goal is to automate tasks and processes and interact with people or other systems as needed. Besides understanding the real-world problem, programming also requires technical consideration of advantages and disadvantages, costs, legal frameworks, dependencies, and more. Actual coding is only a small part of the whole process. Alexander Schatten's second article on heise online explores the role of LLMs in software development. 📰 𝗟𝗟𝗠𝘀 𝗶𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗯𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝗳𝗮𝗶𝗹𝗲𝗱 Software systems are a combination of hardware, software, and the people who develop, maintain, and use these systems. This interaction of the three factors creates benefits and the possibility of further system development. Early on, software exhibited characteristics not observed in other technical systems. The first software crisis in the 1960s revealed that hardware and software capabilities were growing faster than those of human engineers, a crisis from which we have not yet fully emerged. One reason is the ever-increasing complexity of software itself and the complexity created by software and its increasing networking with other systems, affecting software further. Get the full articles here:   https://lnkd.in/gA2hHCBb   https://lnkd.in/giAV6f8C    Both articles will soon be published in iX magazine. 📷: Marcel Lehner 

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A #new version of #ASALIPY is coming after the summer break 🎉 Now, #ASALIPY can handle user defined kinetic models in .json format. This allows all of you modeling your reactors without the need of writing a #CHEMKIN or #CANTERA kinetic scheme. Let us know what you think about it!!! #Python #Anaconda #SoftwareEngineering #Chemical #Opensource

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🚨 Have you enrolled for the two new courses on federated learning, created in collaboration with DeepLearningAI and Flower? If not, this is your chance to dive into the fundamentals of #federatedlearning and explore the Federated Fine-tuning of Large Language Models (#LLMs) using private data. 📅 Join us on Monday 29th, at 16:00 UTC for an AMA session with everyone at Flower who worked on these courses. We'd be happy to answer your questions and talk more broadly about the topic. ➡️ How can you participate? Simply scan the QR code on this post or click here https://buff.ly/3YONpTy to access the session . 1. Join the Flower Slack 2. Join the #course-deeplearning-ai channel 🍿 Don’t miss out!

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📅 Mark your calendars for September 18th in #Vienna! At the #DataScience MeetUp, Andreas from Neo4j and Sebastian will talk about the relevance of #KnowledgeGraphs for Gen #AI, and explain why proper #GraphVisualization is critical for understanding your #data. We look forward to seeing you there! https://lnkd.in/e-EKZ9fZ

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A great read on the performance and financials of LLM fine-tuning. Really hoping the outcome is a tool that helps overcome the biggest elephant in the room, Mr. Cost! #llm #finetuning #finance #computaioncosts

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#IJCAItutorial T24: Demystifying RL for Large Language Models: A training paradigm shift #IJCAI2024 🗣️Florian STRUB, Olivier Pietquin ➡️https://lnkd.in/eTXYwKeZ Abstract: While Reinforcement Learning (RL) recently became essential to Large Language Models (LLMs) alignment, we still only scraped the surface of the potential impact of RL on LLMs. Beyond alignment to human preferences, RL genuinely trains LLMs to generate full completions from prompts, potentially outperforming standard supervised learning approaches based on next-token prediction. Contrary to popular belief, the structural properties of the language domain make applying RL a straightforward process. This tutorial thus aims to pedagogically dive into several RL-inspired methods to train language models efficiently. Taking an inductive approach, we use a summarization task as a support to demystify RL-based training: detailing underlying hypotheses underneath online RL(HF) and DPO-like algorithms, hinting at good practices and pitfalls before exploring original approaches such as language sequence modeling and self-play. We expect to democratize the usage of RL in the LLM community and intuite the emergence of new language modeling training paradigms. #LLMs #AI #Chatbot

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𝗔𝗜 𝗔𝗦𝗦𝗜𝗦𝗧𝗘𝗗 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥𝗜𝗡𝗚 🧠💡 𝗛𝗼𝘄 𝗰𝗮𝗻 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗶𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗯𝗲 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜? 👉️ 𝗙𝗶𝗻𝗱 𝗼𝘂𝘁 𝗶𝗻 𝗼𝘂𝗿 𝗹𝗮𝘁𝗲𝘀𝘁 HANSER automotive 𝗮𝗿𝘁𝗶𝗰𝗹𝗲. Since the release of ChatGPT by OpenAI 1.5 years ago, the growing impact of generative AI, especially large language models (LLMs), has become evident across various fields. From natural sciences and engineering to computer science, generative AI is transforming education, research, and development. One area with particularly high efficiency potential is #softwaredevelopment. 💪 Our ITK Engineering experts Stefan Held and Dr. Dominik Holling explain how #generativeAI can be leveraged in software development to boost the performance. Check out our article now (only available in German) 📃 #ITKengineering #ITKtech #hanserautomotive #AI #AIassistedengineering #softwaredevelopment

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The Uncertain Art of Accelerating ML Models with Sylvain Gugger: Sylvain Gugger is a former math teacher who fell into machine learning via a MOOC and became an expert in the low-level performance details of neural networks. He’s now on the ML infrastructure team at Jane Street, where he helps traders speed up their models. In this episode, Sylvain and Ron go deep on learning rate schedules; the subtle performance bugs PyTorch lets you write; how to keep a hungry GPU well-fed; and lots more, including the foremost importance of reproducibility in training runs. They also discuss some of the unique challenges of doing ML in the world of trading, like the unusual size and shape of market data and the need to do inference at shockingly low latencies. You can find the transcript for this episode  on our website. Some links to topics that came up in the discussion: * “Practical Deep Learning for Coders,” a FastAI MOOC by Jeremy Howard, and the book, of which Sylvain is a co-author. * The Stanford DAWNBench competition that Sylvain participated in. * HuggingFace, and the Accelerate library that Sylvain wrote there. * Some of the languages/systems for expression ML models that were discussed: PyTorch, TensorFlow, Jax, Mojo, and Triton * CUDA graphs and streams * Hogwild concurrency #OCaml #OCamlPlanet

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baiosphere publishes the #MunichAILecture by Professor Alexei Efros of the University of California, Berkeley. Efros has made a name for himself by optimising #machinelearning methods and researching #unlabelleddata used for #computervision or #patternrecognition. Efros gave his lecture in Munich under the thesis that far too little credit is generally paid to data, but listen for yourself: https://lnkd.in/dbFhqKXd #bigdata, #artificialintelligence, #ai, #KI, #artificialintelligence, #research, #researchwithKI, #IT4Science, #discoverLRZ Artificial Intelligence News, Artificial Intelligence, Hochschule Bayern e.V., Universität Bayern e.V., Digitalverbund Bayern, Erlangen National High Performance Computing Center (NHR@FAU), FAU Erlangen-Nürnberg, Technische Universität Nürnberg, TUM.ai, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Data Science Women, Data Science AI Learner Community, IT4Innovations National Supercomputing Center

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#mdpisymmetry Check this newly published article "Explicit and Implicit Preference for Symmetry Across Object Categories" at https://brnw.ch/21wOqXR Authors: Marco Roccato et al. Università degli Studi di Padova #symmetry #preference #aesthetics #landscapes

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📅 On 07.11.2024, we invite you to the ScaDS.AI Dresden/Leipzig training "Big Data Processing on HPC" with Apurv Kulkarni and Wenyu Zhang. Apache Spark and Apache Flink are two typical #BigData analytics frameworks. Their APIs allow the development and testing of an application on a local workstation and later, without changing the source code of the application, distribute work to many computers when the local workstation is not sufficient anymore due to limited resources. The course focuses on the step from a local workstation to an #HPC environment and presents how the typical Big Data analysis workflow can be organized in an HPC environment. In this course participants will be introduced to running a data pipeline and data processing along with managing the configurations on the HPC environment, using Apache Flink and Apache Spark. Register here: 👉 https://lnkd.in/eYfmiMT8

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