| What? |
We meet online every two-three weeks to discuss a (recent) paper in the philosophy of machine learning, with a focus, but not an exclusive focus, on epistemological themes. |
| When? |
The timeslot for the winter semester 2024-2025 is Friday 15.00-16.00. See the below schedule for upcoming and past meetings. |
| Who? |
If you would like to join us, please contact Tom Sterkenburg (tom.sterkenburg lmu.de) or Timo Freiesleben (timo.freieslebenweb.de). |
Winter 2024/2025
| Date |
Reading |
| Fri 13 Dec, 15-16 |
Colbrook, Antun, and Hansen (2022), "The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale's 18th problem." |
Fri 29 Nov, 15-16 |
Butlin et al. (2023), "Consciousness in artificial intelligence: Insights from the science of consciousness." |
| Fri 8 Nov, 15-16 |
Jebeile, Lam, and Räz (2021), "Understanding climate change with statistical downscaling and machine learning." |
| Fri 25 Oct, 15-16 |
Van Rooij et al. (2024), "Reclaiming AI as a theoretical tool for cognitive science." |
Summer 2024
| Date |
Reading |
| Mon 15 Jul, 11-12 |
Herrmann et al. (2024), "Position: Why we must rethink empirical research in machine learning." With first author. |
| Mon 1 Jul, 11-12 |
Kieval and Westerblad (2024), "Deep learning as method-learning: Pragmatic understanding, epistemic strategies and design-rules." |
| Mon 10 Jun, 11-12 |
Liu et al. (2024), "KAN: Kolmogorov-Arnold networks." |
| Mon 27 May, 11-12 |
Norelli et al. (2022), "Explanatory learning: Beyond empiricism in neural networks." |
| Mon 6 May, 11-12 |
Kawamleh (2021), "Can machines learn how clouds work? The epistemic implications of machine learning methods in climate science." |
Winter 2023/24
| Date |
Reading |
| Fri 15 Mar, 16-17 |
Griffiths et al. (2023), "Bayes in the age of intelligent machines." |
Fri 9 Feb, 16-17 |
Corfield (2010), "Varieties of justification in machine learning." |
Fri 26 Jan, 16-17 |
Bilodeau et al. (2024), "Impossibility theorems for feature attribution." |
| Fri 12 Jan, 16-17 |
Hüllermeier and Waegeman (2021), "Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods." |
| Fri 8 Dec, 16-17 |
Gurnee and Tegmark (2023), "Language models represent space and time." |
| Fri 17 Nov, 16-17 |
Varma et al. (2023), "Explaining grokking through circuit efficiency." |
| Fri 27 Oct, 16-17 |
Delétang et al. (2023), "Language modeling is compression." |
Summer 2023
| Date |
Reading |
| Tue 19 Sep, 16-17 |
D'Amour et al. (2021), "Underspecification presents challenges for credibility in modern machine learning." |
| Fri 14 Jul, 16-17 |
Fokkema et al. (2023), "The risks of recourse in binary classification," with first author. |
| Tue 20 Jun, 16-17 |
Delétang et al. (2023), "Neural networks and the Chomsky hierarchy." |
| Tue 6 Jun, 16-17 |
Guest et al. (2018), "Deep learning and its application to LHC physics." |
| Tue 23 May, 16-17 |
Bahri et al. (2020), "Statistical mechanics of deep learning." |
| Tue 2 May, 16-17 |
Belkin (2021), "Fit without fear: Remarkable mathematical phenomena of deep learning through the prism of interpolation." |
Winter 2022/23
| Date |
Reading |
| Thu 6 Apr, 15-16 |
Pavlick (2022), "Semantic structure in deep learning". |
| Thu 16 Mar, 15-16 |
Chen et al. (2021), "NeuralLog: Natural language inference with joint neural and logical reasoning.". |
| Thu 2 Mar, 15-16 |
Willig et al. (2022), "Can foundation models talk causality?" |
| Thu 16 Feb, 15-16 |
Cruttwell et al. (2022), "Categorical foundations of gradient-based learning." |
| Thu 2 Feb, 15-16 |
Mohsin et al. (2022), "Learning to design fair and private voting rules." |
| Tue 19 Jan, 15-16 |
Hasson et al. (2020), "Direct fit to nature: An evolutionary perspective on biological and artificial neural networks." |
| Thu 22 Dec, 15-16 |
Xie et al. (2021), "An explanation of in-context learning as implicit Bayesian inference". |
| Thu 8 Dec, 15-16 |
Vaswane et al. (2017), "Attention is all you need.". |
| Thu 24 Nov, 15-16 |
Buchholz and Raidl (in press), "A falsificationist account of artificial neural networks," with first author. |
| Thu 3 Nov, 15-16 |
Schwöbel and Remmers (2022), "The long arc of fairness: Formalisations and ethical discourse." |
Summer 2022
| Date |
Reading |
| Mon 12 Sep, 16-17 |
Hedden (2021), "On statistical criteria of algorithmic fairness." |
| Mon 8 Aug, 16-17 |
Seth (2015), "The cybernetic Bayesian brain." |
| Mon 11 Jul, 16-17 |
Dreyfus (2007), "Why Heideggerian AI failed and how fixing it would require making it more Heideggerian." |
| Mon 20 Jun, 16-17 |
Chollet (2019), "On the measure of intelligence." |
| Mon 30 May, 16-17 |
Chauhan et al. (2020), "Automated machine learning: The new wave of machine learning." |
| Mon 16 May, 16-17 |
Bowers et al. (2022), "Deep problems with neural network models of human vision." |
| Mon 25 Apr, 16-17 |
Buckner (2020), "Understanding adversarial examples requires a theory of artefacts for deep learning". |
| Mon 4 Apr, 16-17 |
Boge (2022), "Two dimensions of opacity and the deep learning predicament." |
| Mon 14 Mar, 16-17 |
Enni and Herrie (2021), "Turning biases into hypotheses through method: A logic of scientific discovetery for machine learning." |
