Michael Panchenko

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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Welcome to read the article "Integrated Gradients for Feature Assessment in Point Cloud-Based Data Sets" which is written by Mr. Markus Schwegler, Prof. Christoph Müller and Alexander Reiterer. This excellent article was published in issue 7 in 2023 and has been viewed 1313 times and cited 3 times! Article link: https://lnkd.in/gMCsed5z MDPI Fraunhofer IPM Furtwangen University Albert-Ludwigs-Universität Freiburg (Albert Ludwig University of Freiburg) #pointcloud #neuralnetwork #integratedgradients

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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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🤔 Facing challenges with traditional Retrieval-Augmented Generation systems in your LLM projects? 🚀 Introducing GraphRAG: A Solution to Power Up LLMs with Knowledge Graphs: https://lnkd.in/gUpyKgSy Traditional retrieval systems often struggle with the complexity and volume of data, leading to suboptimal integration of information into LLMs. GraphRAG, developed by Microsoft Research, revolutionizes this by leveraging knowledge graphs to enhance data structuring, relevance, and retrieval efficiency. 📅 Join us at the DataHack Summit 2024 for an in-depth session: Topic: "GraphRAG: Powering Up LLMs with Knowledge Graphs" The speaker of this session will be Siddhant Agarwal, Developer Relations Lead - APAC at Neo4j. In this session, Siddhant will explore how GraphRAG addresses these challenges by: ➡️ Creating Knowledge Graphs: Extract entities and relationships to form a structured knowledge base. ➡️ Semantic Clustering: Organize the graph into semantic communities for better data cohesion. ➡️ Enhanced Query Processing: Generate comprehensive, diverse answers relevant to user queries. #AnalyticsVidhya #DataHackSummit2024 #DHS2024

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Kolmogorov–Arnold Networks seem like a paradigm shift in ML architectures. Curious to see how they evolve. "While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. " https://lnkd.in/gdPk_qCQ

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Exciting news for the ones who are interested in quantum, now there is an alternate to virtual qubits, called mechanical qubits that has been recently developed by ETH Zürich, very interesting paper to read. From the way they explained how it is built, it sounds rather complex and expensive process to scale up, but nevertheless solves the problem of virtual qubits to disappear in no time. https://lnkd.in/eZCfUUCX

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Retrieval Augmented Generation (RAG) is a straightforward concept that any company can implement with little effort. You can think of RAG as an NN-based document summarization, like merging the results of a simple elastic search/apache solr query with a text summary. The original concept involved retraining the Llm to have a similar latent space, however in applied concepts the retraining has been dropped and replaced with a vector database and prompt engineering that can be paired with any Llm that exists. Based on Meta's LLAMA3, LlamaIndex, Qdrant, Ollama, Angular, and FastAPI, I built the RAG demo about funding lines for universities of applied sciences in hesse. The demo is available at (not well optimized for mobile use): https://lnkd.in/eBYQwKh4 This demo is a weekend project when the little ones were asleep, so it took me less than 3 days. Imagine your company creating a bot like this with more manpower and effort. Best of all, it's completely free and costs you nothing. Your data stays in your company without any security or GDPR violations. P.S.: On my own behalf: I also work as a speaker and consultant on AI topics. Through my research in context-aware fault diagnosis in industrial production lines, I know the state of the art in AI and have implemented various AI use cases myself using different neural networks under industry constraints. If you need some advice, just get in touch with me. I'm looking forward to talking with you about self-built solutions without buying expensive cloud services.

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Deep Dive into xLSTM with Max Beck at ML Week Europe Join us at Machine Learning Week Europe for a keynote by Maximilian Beck from Johannes Kepler Universität Linz and our NXAI team. Max will talk about “xLSTM: New Architectures for Large Language Models”, sharing his latest breakthroughs in efficient and scalable AI models. 👉 Ready to explore the future of AI? Join Max and the NXAI team in Munich at Machine Learning Week! #MachineLearning #xLSTM #AI #MLWeekEurope #NXAI Peter Seeberg, Martin Szugat

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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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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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Quantum Machine Learning Algorithms for Anomaly Detection: a Survey by Sebastiano Corli, Lorenzo Moro, Daniele Dragoni, Massimiliano Dispenza, Enrico Prati via quant-ph updates on arXiv.org ([Global] Quantum Computing) URL: https://ift.tt/mhjaexu arXiv:2408.11047v1 Announce Type: new Abstract: The advent of quantum computers has justified the development of quantum machine learning algorithms , based on the adaptation of the principles of machine learning to the formalism of qubits. Among such quantum algorithms, anomaly detection represents an important problem crossing several disciplines from cybersecurity, to fraud detection to particle physics. We summarize the key concepts involved in quantum computing, introducing the formal concept of quantum speed up. The survey provides a structured map of anomaly detection based on quantum machine learning. We have grouped existing algorithms according to the different learning methods, namely quantum supervised, quantum unsupervised and quantum reinforcement learning, respectively. We provide an estimate of the hardware resources to provide sufficient computational power in the future. The survey provides a systematic and compact understanding of the techniques belonging to each category. We eventually provide a discussion on the computational complexity of the learning methods in real application domains. https://ift.tt/E7w4vux

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Retrieval-Augmented Generation (RAG) enables answering functional, technical and domain-related questions about large codebases using LLMs, thus supporting developers in onboarding, feature development, or error analysis. In traditional RAG, code fragments relevant to the query are selected using similarity search. However, this method can miss critical code segments that are not directly related to the query but are crucial for correctly answering the question. As part of her master's thesis at itestra, Helena is researching an extension of RAG with enhanced code retrieval capabilities based on LLM agents. This aims to deliver more precise code snippets, significantly improving the quality of the answers. Helena uses Llama 3 70B and runs the entire setup on-premise on our own Nvidia RTX 6000 Ada, making it suitable for projects where the source code should not be processed in the cloud. #SoftwareEngineering #CodeAnalysis #ArtificialIntelligence #GenerativeAI #RetrievalAugmentedGeneration #LargeLanguageModel

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🚀 Looking to streamline fine-tuning for models like Llama 3, Mistral, and Mixtral? Our latest blog shows how Anyscale simplifies LLMs fine-tuning with Ray: 🔑 Key takeaways: • Scalable fine-tuning with LLM-Forge • Model Deployment with RayLLM Read more: https://lnkd.in/gQwD4A2U

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The paper "Scalable MatMul-free Language Modeling" by Rui-Jie Zhu et al. introduces a method to eliminate matrix multiplication (MatMul) operations in large language models (LLMs), significantly reducing memory usage and computational cost. Their MatMul-free models perform comparably to state-of-the-art transformers, scaling effectively up to 2.7 billion parameters. They achieved memory reductions of up to 61% during training and over 10x during inference by using a custom FPGA hardware solution, which highlights the efficiency of lightweight operations for future language model accelerators. For further details, you can read the full paper https://lnkd.in/eZidqJuk

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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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