Giusi Moffa on LinkedIn: The Dual PC Algorithm and the Role of Gaussianity for Structure Learning… (2024)

What happens when you want to optimize a plethora of different objectives instead of just only the prediction error or prediction time of machine learning models with respect to their hyperparameter values? You have what is known as a many objective problem.You can use lots of techniques coming from the many objective optimization field also in Bayesian optimization. Discover some early research in this topic here and DM me if you are interested on this field. https://lnkd.in/diQucnjC

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Vectorization can provide focus. Even more important is the tie to local:private data while still leveraging foundational more generic models and getting both types of lift. Often we see an attention to vector databases. The ability to tie refence and relevance to knowledge graphs provides manifold additional leverage toward guardrails and reality.#LLM #focus #attention #nohallucination#RAG #knowledgegraphHere is a quick primer of such a utility: https://lnkd.in/gX8MxbQV

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Independent Component AnalysisLet's delve into one of the techniques used to overcome the "curse of dimensionality"Definition: ICA is a statistical and computational technique used in machine learning to separate a multivariate signal into its independent non-Gaussian component. The technique aims to find a linear transformation of the data such that the transformed data is as close to being statistically independent as possible.Axioms: -It asserts that the source signals are statistically independent - Each source signal does not follow a Gaussian distribution

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#Mathematics #Particularinterest #Highlycited Article by Josmar Mazucheli(et al.)Vasicek Quantile and #Mean #Regression Models for #Bounded #Data: New Formulation, Mathematical Derivations, and Numerical Applications https://buff.ly/40uxB9b @MDPIOpenAccess @ComSciMath_Mdpi

In Machine Learning Algorithm, selection of the evaluation metrics are very crucial to check the performance of the algorithm.Being restricted to the Regression Analysis, there are various model evaluation metrics are there:👉 MSE or RMSE👉 MAE👉 R- Square👉 Adjusted R-squareIt is very difficult to decide when to use which metrics.Based on the dataset and the problem statement, one can decide it.Follow the given PPT for the use of MAE and MSE. 👇 #machinelearning #evaluationmetrics #dataanalytics #mse #mae #regressionanalysis

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#Mathematics #Particularinterest #Highlycited Article by Josmar Mazucheli(et al.)Vasicek Quantile and #Mean #Regression Models for #Bounded #Data: New Formulation, Mathematical Derivations, and Numerical Applications https://buff.ly/40uxB9b @MDPIOpenAccess @ComSciMath_Mdpi

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Improving Model Accuracy with Stochastic Gradient Descent📣 Check out our latest blog post on Improving Model Accuracy with Stochastic Gradient Descent! 🚀In the field of machine learning, building accurate models is crucial for reliable predictions. That's where Stochastic Gradient Descent (SGD) comes in. Our new article explores how SGD can enhance model accuracy, including techniques to optimize its performance.Understanding Stochastic Gradient Descent:Learn how SGD, an iterative optimization algorithm, minimizes the loss function by updating model parameters in small steps. Unlike traditional gradient descent, SGD randomly selects mini-batches of data to compute gradients. This random approach allows SGD to escape local minima and converge faster.Advantages of Stochastic Gradient Descent:Discover the advantages of using SGD, including its efficiency in handling large datasets, faster convergence speed compared to traditional gradient descent, and its ability to prevent overfitting and improve model accuracy.Techniques to Improve SGD Performance:Explore techniques such as learning rate scheduling, momentum, regularization, batch normalization, and early stopping. These techniques enhance SGD's performance and help achieve better results.Conclusion:SGD remains a fundamental tool for improving model accuracy and building reliable predictive models. By leveraging its efficiency, convergence speed, and generalization capabilities, you can train accurate machine learning models with large datasets.Ready to dive deeper? Read our blog post here: [Improving Model Accuracy with Stochastic Gradient Descent](https://ift.tt/4BDdxTc)#machinelearning #datascience #SGD #optimization #modelaccuracyhttps://ift.tt/4BDdxTc

improvement to Bayesian inference technique https://lnkd.in/gucpsE2Z

pub.towardsai.net: Spectral clustering is a graph-theoretic technique that leverages the connectivity of data points for clustering. It is a powerful method for identifying clusters in complex datasets.

🔍 Unveiling the Power of OKS in Keypoint Detection! 🔍📣 Excited to share our latest technical blog post: "Object Keypoint Similarity in Keypoint Detection." Dive into the world of keypoints and discover the significance of OKS in mAP for keypoints problems.🔑 Just as IoU plays a crucial role in object detection, OKS steps up for keypoints. 🎯 Learn how OKS meticulously assesses the quality of keypoints predictions, providing a deeper understanding of their accuracy.💡 Mastering machine learning metrics is essential, and understanding OKS opens doors to enhanced keypoints model performance. Don't miss out on this insightful read!Read the full article here:https://lnkd.in/gPH9xW6h#KeypointDetection #MachineLearning #ObjectKeypointSimilarity #OKS #ComputerVision #DeepLearning

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