3 ms·

The relative performance in err/watts/time compared to deep learning for feature selection instead of principal component analysis and standard xgboost or tabul

The relative performance in err/watts/time compared to deep learning for feature selection instead of principal component analysis and standard xgboost or tabular xt TODO for optimization given the indicating features. XAI: Explainable AI: https://en.wikipedia.org/wiki/Explainable_artificial_intelligence#Explainability_and_interpretability_techniques https://en.wikipedia.org/wiki/Explainable_artificial_intelli... /? XAI , #XAI , Explain, EXPLAIN PLAN , error/energy/time

westurner 2y ago
From "Interpretable graph neural networks for tabular data" (2023) https://news.ycombinator.com/item?id=37269881 https://news.ycombinator.com/item?id=37269881 : > TabPFN: https://github.com/automl/TabPFN https://github.com/automl/TabPFN .. https://x.com/FrankRHutter/status/1583410845307977733 https://x.com/FrankRHutter/status/1583410845307977733 [2022] "TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second" (2022) https://arxiv.org/abs/2308.08945 https://arxiv.org/abs/2308.08945 > FWIU TabPFN is Bayesian-calibrated/trained with better performance than xgboost for non-categorical data
westurner 2y ago
From https://news.ycombinator.com/item?id=34619013 https://news.ycombinator.com/item?id=34619013 : > /? awesome "explainable ai" https://www.google.com/search?q=awesome+%22explainable+ai%22 https://www.google.com/search?q=awesome+%22explainable+ai%22 - (Many other great resources) - https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master/data/model-analysis-interpretation-explainability.md#post-model-creation-analysis-ml-interpretationexplainability https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master... : > Post model-creation analysis, ML interpretation/explainability > /? awesome "explainable ai" "XAI"
- westurner 2y ago
  "A Survey of Privacy-Preserving Model Explanations: Awesome Privacy-Preserving Explainable AI" https://awesome-privex.github.io/ https://awesome-privex.github.io/

westurner 2y ago

From "Interpretable graph neural networks for tabular data" (2023) https://news.ycombinator.com/item?id=37269881 https://news.ycombinator.com/item?id=37269881 : > TabPFN: https://github.com/automl/TabPFN https://github.com/automl/TabPFN .. https://x.com/FrankRHutter/status/1583410845307977733 https://x.com/FrankRHutter/status/1583410845307977733 [2022] "TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second" (2022) https://arxiv.org/abs/2308.08945 https://arxiv.org/abs/2308.08945 > FWIU TabPFN is Bayesian-calibrated/trained with better performance than xgboost for non-categorical data

westurner 2y ago

From https://news.ycombinator.com/item?id=34619013 https://news.ycombinator.com/item?id=34619013 : > /? awesome "explainable ai" https://www.google.com/search?q=awesome+%22explainable+ai%22 https://www.google.com/search?q=awesome+%22explainable+ai%22 - (Many other great resources) - https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master/data/model-analysis-interpretation-explainability.md#post-model-creation-analysis-ml-interpretationexplainability https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master... : > Post model-creation analysis, ML interpretation/explainability > /? awesome "explainable ai" "XAI"

westurner 2y ago
"A Survey of Privacy-Preserving Model Explanations: Awesome Privacy-Preserving Explainable AI" https://awesome-privex.github.io/ https://awesome-privex.github.io/