Diogo Ribeiro Braga

📅 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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💡 Great insights on fine-tuning LLMs using Sparse Mixture of Experts (MoE)! This method optimizes performance by activating only specific parts of the model, which can significantly enhance efficiency and cost-effectiveness. 📊💸 The study’s focus on training efficacy, runtime metrics, GPU utilization, and cost modeling is crucial for managing cloud expenses and improving resource allocation. 🔍🚀 It’s exciting to see advancements that help us get more out of our compute resources and budget accurately. A valuable read for anyone involved in AI research and development! 🙌 #AI #MachineLearning #LLM #MixtureOfExperts #TechInnovation #CostEfficiency #AIResearch

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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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🚀 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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Additional research results are coming underlining the fact that longer #contextwindow might not be the right solution, at least not in the current way it is perceived, for scaling #LLM capabilities. (https://lnkd.in/gEsEETpv) Further, #longcontext window opens up #efficiency challenges and potential #securityrisks. Anthropic had earlier published a research paper on the risk of multi-shot #jailbreaking with long context LLMs. (https://lnkd.in/gv43Aky4) #genai #responsibleai #sustainableai

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Preprint analysis focusing on the use of AI as a tool in #OpenScience. Thinking about these AI-related challenges and opportunities at your organization? Delta Think can help you understand, define, and implement your next steps. Please reach out today to get started. #ScholComm #AI

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The lowest hanging fruit for mobilising LLM's in production is handling outputs via data structures and controlling outputs with validators. Jason Liu explains this well as he demonstrates that classic software engineering tools are being backfit to LLM development. AI at scale is on its way - keep it simple. https://lnkd.in/gAitudHR #mlops #llmops #genai

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#EarlyCareer #talk at #IJCAI2024 by🗣️Julia Neidhart, Technische Universität Wien: Transforming Recommender Systems: Balancing Personalization, Fairness, and Human Values ➡️https://lnkd.in/dK3Nieyr Abstract: Recent advancements in recommender systems highlight metrics beyond accuracy, including diversity, serendipity and fairness. This paper examines the integration of Large Language Models into recommender systems, focusing on challenges such as interpretability, bias, and user autonomy. LLMs offer enhanced personalization capabilities but also introduce concerns regarding transparency and ethical implications. This work discusses ongoing research efforts aiming to develop transparent, fair, and contextually aware systems. Our approach seeks to prioritize user wellbeing and inclusivity, contributing to a more equitable and functional digital environment through advanced technologies and interdisciplinary insights. #XAI #LLMs #InclusiveTech #NextGenAI #AI TU Wien Informatics

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Get clarity and better understand the use and the use and potential of einsum which is often used in codebases in the implementations of Deep Learning model architectures such as modern LLMs. 🖊️ by Marcello Politi | #AI #MachineLearning #DataScience #DeepLearning

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RAG Sytems are not just about using powerful LLMs. What do you do if pre-trained embedding models are not working well for retrieval? Full example from dataset creation to fine-tuning in Module 4: https://lnkd.in/e8nWipvd

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#mdpiforecasting #openaccess Article "Spatial Dependence of Average Prices for Product Categories and Its Change over Time: Evidence from Daily Data" https://lnkd.in/edQMeFsj #prices #spatialvariation #spatialautocorrelation

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Understanding the nuances of data-centric AI is becoming increasingly crucial. That's why we're sharing this insightful lecture by Dr. Ce Zhang, Associate Professor in Computer Science at ETH Zürich, delivered at Snorkel AI's The Future of Data-Centric AI event last year. In the lecture, Dr. Zhang delves into machine learning's cost problem, the interrelation of data and model problems, and how to discern valuable data problems from those that can needlessly consume time and resources. This talk is a must-see for anyone keen on gaining a deeper understanding of the current challenges and future directions in data-centric AI. Watch the full lecture here: #machinelearning #datacentricai #airesearch

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How to Build a Custom Optimizer in PyTorch: 5 Simple Steps via #TowardsAI → https://bit.ly/3Y2UXTB

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How to Build a Custom Optimizer in PyTorch: 5 Simple Steps via #TowardsAI → https://bit.ly/3Y2UXTB

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