What? We meet 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 2023/24 is Friday 16.00-17.00. 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.freieslebencampus.lmu.de).

Winter 2023/24

Date Reading
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."