Learning Causal Laws
By Josh Tenenbaum, Sourabh A. Niyogi. 2003
57 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- T. Griffiths
- Josh Tenenbaum
- Yoshua Bengio
- Ruslan Salakhutdinov
- Akanksha Atrey
- Trevor Darrell
- Sergey Levine
- Kaleigh Clary
Citations in the corpus
- Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning (2019, background): “Current theory and evidence from cognitive science implies that humans learn complex processes, such as video games, by categorizing objects into abstract classes and by inferring causal relationships among instances of those classes (Tenenbaum & Niyogi, 2003; Dubey et al., 2018).”
- Two proposals for causal grammars (2007, methodology): “This sort of learning is certainly less general than discovering a full theory with new classes and causal laws, as in the experiments of Tenenbaum and Niyogi (2003), but it is also more general than just learning the parameters or structure of a single causal network (learning at the T0-level).”
- Theory-based causal induction. (2009, background): “sibility, has recently been explored by Tenenbaum and Niyogi (2003),”
- Bayesian models of cognition (2008, unknown): “…graphical models, using probabilistic forms of generative grammars, predicate logic, or other structured representations (Griffiths, 2005; Griffiths & Tenenbaum, 2007b; Mansinghka, Kemp, Tenenbaum, & Griffiths, 2006; Tenenbaum et al., 2006; Tenenbaum, Griffiths, & Niyogi, 2007; Tenenbaum & Niyogi, 2003).”
- Intuitive theories as grammars for causal inference (2007, background): “( Tenenbaum & Niyogi, 2003 ) has been so fruitful for us that it is worth discussing in some detail here, both to motivate the specific proposals we will offer and to provide more general suggestions for how future work on intuitive theories might proceed.”