PhilSci Archive

Items where Subject is "Specific Sciences > Artificial Intelligence > Machine Learning"

Up a level
Export as [feed] Atom [feed] RSS 1.0 [feed] RSS 2.0
Group by: Creators | Item Type
Jump to: B | C | D | F | G | J | K | L | N | R | S | T | Z
Number of items at this level: 30.


Babic, Boris and Gerke, Sara and Evgeniou, Theodoros and Cohen, Glenn (2019) Algorithms on Regulatory Lockdown in Medicine. Science.

Boge, Florian J. and Grünke, Paul (2019) Computer simulations, machine learning and the Laplacean demon: Opacity in the case of high energy physics∗. [Preprint]

Buckner, Cameron (2019) Deep Learning: A Philosophical Introduction. [Preprint]


Cabrera, Frank (2020) Correlation Isn’t Good Enough: Causal Explanation and Big Data. Metascience. ISSN 0815-0796

Casacuberta, David and Estany, Anna (2019) Convergence between experiment and theory in the processes of invention and innovation. THEORIA. An International Journal for Theory, History and Foundations of Science, 34 (3). pp. 373-387. ISSN 2171-679X

Climenhaga, Nevin (2019) The Structure of Epistemic Probabilities. Philosophical Studies. pp. 1-30. ISSN 0031-8116

Creel, Kathleen A. (2019) Transparency in Complex Computational Systems. [Preprint]


Dotan, Ravit (2020) Theory Choice, Non-epistemic Values, and Machine Learning. [Preprint]


Facchin, Marco (2021) Are generative models structural representations? [Preprint]

Facchin, Marco (2021) Are generative models structural representations?


Gerke, Sara and Babic, Boris and Evgeniou, Theodoros and Cohen, Glenn (2020) The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. Nature Digital Medicine.

Grimsley, Christopher (2020) Causal and Non-Causal Explanations of Artificial Intelligence. In: UNSPECIFIED.


Jebari, Karim and Lundborg, Joakim (2019) Artificial superintelligence and its limits: why AlphaZero cannot become a general agent. [Preprint]

Jebeile, Julie and Lam, Vincent and Räz, Tim (2020) Understanding Climate Change with Statistical Downscaling and Machine Learning. [Preprint]

Johnson, Gabbrielle (2020) Algorithmic Bias: On the Implicit Biases of Social Technology. [Preprint]


Korbak, Tomasz (2019) Unsupervised learning and the natural origins of content. [Preprint]


Landgrebe, Jobst and Smith, Barry (2019) Making AI meaningful again. Synthese. ISSN 1573-0964

López-Rubio, Ezequiel (2020) The Big Data razor. [Preprint]

López-Rubio, Ezequiel (2020) Throwing light on black boxes: emergence of visual categories from deep learning. [Preprint]

López-Rubio, Ezequiel and Ratti, Emanuele (2019) Data science and molecular biology: prediction and mechanistic explanation. [Preprint]


Noichl, Maximilian (2019) Modeling the Structure of Recent Philosophy. [Preprint]


Ratti, Emanuele (2020) What Kind of Novelties Can Machine Learning Possibly Generate? The Case of Genomics. [Preprint]

Rosenstock, Sarita (2020) Learning from the Shape of Data. In: UNSPECIFIED.

Räz, Tim (2020) Understanding Deep Learning With Statistical Relevance. [Preprint]


Sterkenburg, Tom F. and De Heide, Rianne (2021) On the Truth-Convergence of Open-Minded Bayesianism. The Review of Symbolic Logic.

Sterkenburg, Tom F. and Grünwald, Peter D. (2020) The No-Free-Lunch Theorems of Supervised Learning. [Preprint]

Stinson, Catherine (2019) From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence. [Preprint]

Sullivan, Emily (2019) Understanding from Machine Learning Models. British Journal for the Philosophy of Science. ISSN 1464-3537


Thompson, Jessica A. F. (2018) Towards a common theory of explanation for artificial and biological intelligence. [Preprint]


Zednik, Carlos and Boelsen, Hannes (2020) The Exploratory Role of Explainable Artificial Intelligence. In: UNSPECIFIED.

This list was generated on Thu Apr 15 14:17:40 2021 EDT.