Djohra IBERRAKEN’s Post

Monge’s Displacement Theory: Simplifying Soft Body Dynamics, Full article link 👇🏻👇🏻 https://lnkd.in/d5zFN78n The Monge Problem Revisited: Elastic Costs Introduction If we are given a pair of probability measures supported on Rdmathbb{R}^dRd, the Monge problem aims to find a most efficient way to "map" one distribution into the other. This optimal mapping is quantified via a cost function between samples from the source and the target data. In […] #artificialintelligence #machinelearning #ML #AI

We are happy to share a new article by our PhD student Jonas, who recently attended the International Conference on Machine Learning ([ICML] Int'l Conference on Machine Learning) in ☕Vienna☕! Read his report about the vibrant ML community, talks, poster and workshops that rounded off this remarkable event for him. https://lnkd.in/eqbH6pkw #ICML2024 #AIforScience #MachineLearning #HertieAI

Monge’s Displacement Theory: Simplifying Soft Body Dynamics, Full article link 👇🏻👇🏻 https://lnkd.in/dXF2czDr The Monge Problem Revisited: Elastic Costs Introduction If we are given a pair of probability measures supported on Rdmathbb{R}^dRd, the Monge problem aims to find a most efficient way to "map" one distribution into the other. This optimal mapping is quantified via a cost function between samples from the source and the target data. In […] #artificialintelligence #machinelearning #ML #AI

Gradient-based explanation methods are often perceived as the gold standard in machine learning interpretability. In this blog post, I explain why I believe they barely qualify as a rusty iron standard. Enjoy!

AI sniffs out whiskey flavor notes as well as the pros - Science News: A machine learning algorithm identified the top five flavor notes in 16 types of whiskey. Each matched the aggregate of what a panel of human pros ... http://dlvr.it/TGw1Hn

Hello network, I am pleased to share with you the first publication in a series that might interest you, especially if you are fascinated by forecasting and machine learning. In this case, it is the presentation of the Structured Radial Basis Function Network, which will be published in this paper as part of the proceedings of the 'AI-2024 Forty-fourth SGAI International Conference on Artificial Intelligence CAMBRIDGE, ENGLAND 17-19 DECEMBER 2024,' in Springer, Lecture Notes in Artificial Intelligence. https://lnkd.in/dJXsjSh9 Link to the paper while is not in Springer: https://lnkd.in/dNehps4h The most important aspect is that this model allows for tuning the degree of diversity in ensemble learning with an a priori parameter for multi-hypothesis prediction, which is highly valuable for multimodal regressions or non-stationary environments like financial markets. It is trained in two phases: first, the structured dataset is formed by the predictions of the individual predictors, where diversity in ensemble learning is controlled through a parametric rule based on the individual predictors' hypotheses and their loss functions or predictive performance. Then, the ensemble can be trained analytically using least squares, providing a computational advantage compared to most structured ensemble models that require numerical methods for optimization. Experiments show that the diversity parameter can control the degree of generalization in ensemble predictions, outperforming other proposed solutions. If you are in Cambridge at that time happy to have a chat with you and anyone interested in the conference follow the link!.

Why Random Forests Dominate: Insights from the University of Cambridges Groundbreaking Machine Learning Research! In machine learning, the effectiveness of tree ensembles, such as random forests, has long been acknowledged. These ensembles, which pool the predictive power of multiple decision trees, stand out for their remarkable accuracy across various applica... https://lnkd.in/dsdkPeSi #AI #ML #Automation

John Elder returns to Machine Learning Week this June to deliver his acclaimed workshop on ML techniques and a special plenary session, "The Reliability of Backpropagation Is Worse Than You Think." View all the details and sign up: https://lnkd.in/gXWwXJHS

I'm proud to share that I have been invited for a poster presentation at NeurIPS 2024. 🚀 It feels surreal to be invited for a poster presentation at two of the most prestigious AI/ML conferences in the first year of my Master's. I would've refused to believe you if you had told me this one year ago. 🙂 With three friends Christina Isaicu, Jesse Wonnink, and Helia Ghasemi, we reproduced and extended a paper and submitted it to the Machine Learning Reproducibility Challenge 2023. Once accepted, it was published in Transactions on Machine Learning Research and added to the reproducibility challenge workshop at NeurIPS this year. Now what exactly is it about? Temporal graphs are general representations of evolving networks—broadly applied from fraud detection to modeling biological ecosystems. The method, TGNNExplainer, exists to explain temporal graph predictions. This method is model-agnostic and can be used with any temporal graph prediction model. TGNNExplainer reduces the full graph to a pruned subgraph (G^k) which maximizes the prediction probability of the predicted event (e_k), Almost always finding a subgraph that better explains the event than the full graph. If you want to know more, check out our paper 👇 Reproducibility Study of “Explaining Temporal Graph Models Through an Explorer-Navigator Framework", https://lnkd.in/eruAhjnj