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 Summer Semester 2024 is Monday 11.00-12.00. See the below schedule for upcoming and past meetings.
Who? If you would like to join us, please contact Tom Sterkenburg (tom.sterkenburglmu.de) or Timo Freiesleben (timo.freieslebenweb.de).

Summer 2024

Date Reading
Mon 10 June, 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."