Daniel Lopes

Generative models have emerged as transformative tools in several domains, including computer vision and natural language processing, by learning data distributions and generating samples from them..... #compressing #Diffusion #latent #LDM #LDPruner #method #models #Nota #performancepreserving #present #pruning #Researchers #Structured

Particulate matter forecast and prediction in Curitiba using machine learning Frontiers https://lnkd.in/gA-S8bv2

👉🏼 Exploring the capacities of ChatGPT: A comprehensive evaluation of its accuracy and repeatability in addressing Helicobacter pylori-related queries 🤓 Yongkang Lai 👇🏻 https://lnkd.in/eG-2XQ8s 🔍 Focus on data insights: - ChatGPT responses to H. pylori-related questions were generally satisfactory, with 61.9% marked as "completely correct" and 33.33% as "correct but inadequate." - The repeatability of ChatGPT responses to H. pylori-related questions was 95.23%. - Responses related to prevention (comprehensive: 75%) had the best response rate, followed by treatment (comprehensive: 66.7%), basic knowledge (comprehensive: 60%), and diagnosis (comprehensive: 50%). 💡 Main outcomes and implications: - ChatGPT can provide correct answers to the majority of H. pylori-related queries. - It exhibited good reproducibility and delivered easily comprehensible responses to patients. - Further enhancements in real-time information updates and correction of inaccurate data can make ChatGPT an essential tool for providing accurate H. pylori-related health information. 📚 Field significance: - Improving ChatGPT's knowledge on H. pylori resistance and the use of sensitive antibiotics can enhance its utility in providing comprehensive health information to patients. 🗄️: [#Helicobacter #ChatGPT #Hpylori #DataInsights]

👉🏼 Evaluating Large Language Models on their Accuracy and Completeness: Immune Checkpoint Inhibitors and their Ocular Toxicities 🤓 Camellia Edalat 👇🏻 https://lnkd.in/eXu_uEfz 🔍 Focus on data insights: - 📊 ChatGPT, Bard, and LLaMA demonstrated comparable performance in accuracy

[NEW COUPON] Forecasting Models & Time Series Analysis for Business in R Course Time Series Analysis for Data Science & Analytics in R programming. Demand Planning & Forecasting. Prophet, ARIMA Created by Diogo Alves de Resende | 9 hours on-demand video course | 18 downloadable resources Forecasting Models Course Overview Forecasting Models & Time Series Analysis for Business in R Course Coupon Code. Welcome to the most exciting online course about Forecasting Models and Time Series in R. I will show everything you need to know to understand the now and predict the future. Forecasting is always sexy – knowing what will happen usually drops jaws and earns admiration. On top, it is fundamental in the business world. Companies always provide Revenue growth and EBIT estimates, which are based on forecasts. Get Udemy Coupon Code: https://lnkd.in/dkvBHsrd #Forecasting

👉🏼 Large language model to multimodal large language model: A journey to shape the biological macromolecules to biological sciences and medicine 🤓 Manojit Bhattacharya 👇🏻 https://lnkd.in/e8BAuTz2 🔍 Focus on data insights: - 📈 The increasing adoption of large language models

A Practical Guide on Scikit-learn for Time Series Forecasting https://lnkd.in/d8ACJhE5

👉🏼 GENEVIC: GENetic data exploration and visualization via intelligent interactive console 🤓 Anindita Nath 👇🏻 https://lnkd.in/ejQG-BX5 🔍 Focus on data insights: - 📊 GENEVIC automates the analysis of genetic data, significantly reducing the time researchers spend on data interpretation. - 🔍 It integrates multiple data sources, allowing for comprehensive searches across PubMed, Google Scholar, and arXiv, enhancing literature review efficiency. - 🧬 The tool prioritizes genetic variants linked to complex diseases, aiding in targeted research and potential therapeutic developments. 💡 Main outcomes and implications: - 🚀 By streamlining the process of genetic data exploration, GENEVIC empowers researchers to make informed decisions quickly, potentially accelerating biomedical discoveries. - 🛠️ The user-friendly interface democratizes access to advanced genetic analysis tools, making them available to researchers without specialized training. - 🔒 With HIPAA-compliant infrastructure, GENEVIC ensures that sensitive genetic data is handled securely, fostering trust in its use within the biomedical community. 📚 Field significance: - 🌐 GENEVIC represents a significant advancement in bioinformatics, bridging the gap between data generation and actionable biomedical insights. - 🧩 The integration of generative AI into genetic research tools marks a transformative shift in how researchers interact with complex datasets. - 📈 As a prototype, GENEVIC sets a precedent for future innovations in genetic data analysis, paving the way for more sophisticated tools in the field. 🗄️: [#bioinformatics #geneticdata #AI #biomedicalresearch #dataanalysis #geneticvariants #healthcare #machinelearning]

👉🏼 LitAI: Enhancing Multimodal Literature Understanding and Mining with Generative AI 🤓 Gowtham Medisetti 👇🏻 https://lnkd.in/e6gBeT_T 🔍 Focus on data insights: - 📊 The integration of generative AI tools significantly enhances the retrieval accuracy of multimodal information from literature. - 🔍 Empirical evaluations reveal that LitAI outperforms established baselines, indicating its potential for improved information extraction. - 💡 Specific prompts developed for in-context learning allow for tailored and efficient extraction of data from diverse formats. 💡 Main outcomes and implications: - 🚀 LitAI demonstrates superior capability in processing texts, tables, and figures, streamlining literature review processes for researchers. - 🌍 The approach can potentially revolutionize how scientific literature is accessed and analyzed, making knowledge discovery more efficient. - 🛠️ The tool is made accessible through a public repository, encouraging further development and adaptation in the research community. 📚 Field significance: - 📖 Improves the methodologies used in literature information retrieval, paving the way for advancements in data mining techniques. - 🔗 Enhances cross-disciplinary research by providing robust tools for integrating and analyzing information across various types of data sources. - 🧠 Contributes to the growing body of work on generative AI applications, promoting innovative solutions for complex problem-solving in science. 🗄️: [#dataretrieval] [#generativeAI] [#multimodal] [#literaturemining] [#informationextraction] [#researchinnovation] [#knowledge_discovery]

Submitted session #16 From Zero to Hero on Microsoft Fabric Spark Compute by Armando Lacerda The "no-code" approach of SQL server SSIS can find a close parallel in Microsoft Fabric Pipelines and Data Flows. There are cases, though, when we do need to code some complex, out of the curve, very specific data transformations. In this session, you will learn the foundations of Fabric Spark Compute and how to benefit from it through many demos. #sqlsatla #sqlsaturday #sqlfamily #fabricspark #azure #nocode #datapipelines #dataflows

👉🏼 ChatGPT as an effective tool for quality evaluation of radiomics research 🤓 Ismail Mese 👇🏻 https://lnkd.in/eGks8btb 🔍 Focus on data insights: - 📊 ChatGPT-4o achieved a median radiomics quality score

DeepMind’s PEER scales language models with millions of tiny experts DeepMind has introduced Parameter Efficient Expert Retrieval (PEER) to scale Mixture-of-Experts (MoE) models, addressing current limitations of MoE architectures. MoE enhances large language models (LLMs) by routing data to specialized “expert” modules, thus increasing model capacity without escalating computational costs. However, traditional MoE is restricted to a small number of experts. PEER overcomes this by using a learned index to route input data efficiently to millions of tiny expert modules, each with a single neuron in the hidden layer. This design ensures improved parameter efficiency and knowledge transfer.