Explore tweets tagged as #ExplainableML
How can we circumvent data scarcity in the time series domain?. We propose to leverage pretrained ViTs (e.g., CLIP, DINOv2) for time series classification and outperform time series foundation models (TSFMs). 📄 Preprint: 💻 Code:
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I am grateful for this chance to advance our research and shape the future of @ExplainableML. Let's embrace this journey together! 🚀✨We're on the lookout for exceptional talent to join our research groups—stay tuned for opportunities!
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Personal news: I have defended my PhD thesis “Explaining Representations in Deep Neural Networks” at TU Berlin with summa cum laude (with distinction)!. From August, I’ll start a Postdoc at @TU_Muenchen in @ExplainableML group focusing on Mechanistic Interpretability ✨
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Boosting nonlinear penalized least squares #Techtonique #DataScience #Python #rstats #MachineLearning
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New activation functions in mlsauce's LSBoost #Techtonique #DataScience #Python #rstats #MachineLearning
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Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability #Techtonique #DataScience #Python #rstats #MachineLearning
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Boosting nonlinear penalized least squares #Techtonique #DataScience #Python #rstats #MachineLearning
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We are looking for two postdoctoral researchers in our @ExplainableML group @TU_Muenchen @HelmholtzMunich @ELLISforEurope, application deadline is November 19th (fully funded, flexible starting dates, flexible topics, duration negotiable). More details 👇
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Prediction intervals (not only) for Boosted Configuration Networks in Python #Techtonique #DataScience #Python #rstats #MachineLearning
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Explaining a Keras _neural_ network predictions with the-teller #Techtonique #DataScience #Python #rstats #MachineLearning
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Happy to announce our work is now in the esteemed #InformationFusion Journal!🎉In #ExplainableML, we've pushed for interpretable models that are expressive & adhere to task-specific constraints - introducing "Constrainable Neural Additive Models.”👩💻📈 Interested? Give it a read!
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Explaining a Keras _neural_ network predictions with the-teller #Techtonique #DataScience #Python #rstats #MachineLearning
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LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) #Techtonique #DataScience #Python #rstats #MachineLearning
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New activation functions in mlsauce's LSBoost #Techtonique #DataScience #Python #rstats #MachineLearning
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📊 Overcoming multi-class imbalance: class weights vs. sampling 🚀 Learn to balance models for fair predictions in ML. #DataScience #ImbalancedDatasets. Heimdall ML will report precision, recall & F-1 scores for all classification problems #explainableML
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Explaining xgboost predictions with the teller #Techtonique #DataScience #Python #rstats #MachineLearning
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Tests for the significance of marginal effects in the teller #Techtonique #DataScience #Python #rstats #MachineLearning
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