Jacek Tyrakowski

Multitask learning, a machine learning approach that enables computational models to learn multiple tasks simultaneously, presents challenges due to complex optimization scheme and higher labeling demands. This article explores how multitask learning could be utilized in partial supervision settings to overcome these challenges. https://bit.ly/3zwm2Fy Maxime Fontana, Michael Spratling, and Miaojing Shi, "When Multitask Learning Meets Partial Supervision: A Computer Vision Review," in Proceedings of the IEEE, vol. 112, no. 6, pp. 516-543, June 2024 #ComputerVision #DeepLearning #MultitaskLearning #AutonomousDriving #MedicalImaging

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Supercharge Your RAG Applications with Graph-Based Metadata Filtering! 🧠 Want to take your Retrieval-Augmented Generation (RAG) applications to the next level? This insightful blog post by Tomaz Bratanic from Neo4j explores how graph-based metadata filtering can significantly improve vector search accuracy and unlock the full potential of your knowledge graphs. 🤯 Challenges of traditional vector search: -While text embeddings and vector similarity search are powerful tools for finding relevant documents, they often struggle with structured criteria like dates, categories, or specific relationships between entities. This is where metadata filtering comes in! Benefits of graph-based metadata filtering: - Enhanced accuracy: Narrow down the search space using structured filters before applying vector similarity search, leading to more precise and relevant results. - Unlock hidden relationships: Leverage the power of graph databases to explore complex connections between entities and refine your search based on specific criteria. - Dynamic query generation: Use LLMs to generate Cypher statements on the fly, adapting to user input and retrieving the most relevant information from your knowledge graph. The blog post provides a practical example using LangChain and Neo4j, demonstrating how to: Build a custom tool that dynamically generates Cypher statements based on user input. Implement metadata filters for organization, country, sentiment, and topic. Integrate the tool into an OpenAI agent for a conversational RAG experience. This approach opens up exciting possibilities for building more powerful and efficient RAG applications across various domains. Read the full blog post: https://lnkd.in/gYUj4WEF #RAG #MetadataFiltering #GraphDatabases #LangChain #Neo4j #AI ✔️ Click "Follow" on the Cohorte page for daily AI engineering news.

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Fuel your machine learning models with high-quality text annotation services. From sentiment analysis to named entity recognition, ensure your algorithms understand and process text with #accuracy and context. https://hubs.la/Q02_41Pd0 #TextAnnotation #AITraining #NaturalLanguageProcessing #DataAnnotation #NLP #Annotation

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#ExplainableAI (XAI) is an emerging subset in #AI that focuses on the readability of #ML models. Want to further understand what #XAI is and how it works? Dive into our blog post: https://hubs.ly/Q02qHJgT0

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Check out the key features in this powerful module inside #subsurfaceAI https://lnkd.in/gPH64zAU Geoscientists without any coding skills in Python can take full advantages of the latest algorithms in #MachineLearning for improved reservoir characterization & property prediction. SubsurfaceAI Inc Mark Tobin Cindy Luo David Gray Rongfeng Zhang Renjun Wen

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Uncover how AI and machine learning are transforming game development and enhancing player experiences in 2024: https://buff.ly/3ys2Rw0 #AI #MachineLearning #GameDevelopment #GenerativeAI #DynamicStorytelling #IoA

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Ever wondered how to develop a #UNS semantic data hierarchy with #MQTT for real-time data sharing, optimizing processes & enhancing decision-making in #manufacturing? 🚀 Read our blog, where Kudzai Manditereza goes in-depth on this topic using a great example around #automotivemanufacturing. 🔗 https://loom.ly/X0JCnUI 🔗

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Vector databases are crucial to deploying generative AI at scale by efficiently storing and retrieving vector embeddings — numerical representations of data such as text, images, or user behavior. Qdrant is a popular vector database used not only in RAG-powered #GenAI, but also in recommendation systems, anomaly detection, advanced search and more. Discover how to integrate Qdrant with Ori’s AI Native cloud to help you build fast and responsive #AI systems -> https://lnkd.in/giSkfPPi

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This blog post helps set the stage for creating a digital twin of a CSTR and using Kafka and Faust as a PID controller while storing temperature data in InfluxDB. We use Telegraf to get data from the Kafka topic and Grafana to visualize the results. 🔎 https://bit.ly/3AGatvJ #InfluxDB #Grafana #DataViz

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This paper proposes #reconfigurable #intelligent #surface (#RIS) element allocation algorithms that #maximize the #minimum #rate or #sum #rate in a #multi-#user (#MU)-#multiple-#input #single-#output (#MISO)-#orthogonal #frequency #division #multiplexing (#OFDM) system aided with an RIS. Specifically, the authors first propose multiple metrics, which represent the minimum rate and sum rate, depending solely on the channel. They then propose #multiple #convex #optimization #algorithms to maximize the proposed metrics by updating the #RIS #matrix, validating that the metrics indeed represent the desired objectives successfully. Through the metrics, only a single RIS matrix update is required, contrast to existing works using alternating optimization.----Hyesang Cho, @Jeonghyun Moon, Junil Choi More details can be found at this link: https://lnkd.in/gf-nRTSi

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A Gentle Introduction to XGBoost Loss Functions https://lnkd.in/gfi3c7n

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Exploring Simscape Model Statistics Information to Analyze Model Complexity | Simscape Electrical The Statistics Viewer will aggregate Simscape model statistics for a given model with its solver configurations. Use it to evaluate model statistics to analyse models containing blocks and investigate the model complexity. https://lnkd.in/djyszDS7

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