I gave a chat within the workshop on how the synthesis of logic and equipment Understanding, Particularly areas which include statistical relational Finding out, can enable interpretability.
Thinking about synthesizing the semantics of programming languages? We now have a different paper on that, recognized at OOPSLA.
The paper tackles unsupervised software induction more than blended discrete-constant data, which is acknowledged at ILP.
I attended the SML workshop in the Black Forest, and discussed the connections in between explainable AI and statistical relational Discovering.
An write-up on the organizing and inference workshop at AAAI-18 compares two distinctive techniques for probabilistic arranging by way of probabilistic programming.
I gave a talk on our the latest NeurIPS paper in Glasgow though also masking other methods at the intersection of logic, Discovering and tractability. As a result of Oana to the invitation.
Thinking about coaching neural networks with rational constraints? We've got a whole new paper that aims in direction of entire pleasure of Boolean and linear arithmetic constraints on teaching at AAAI-2022. Congrats to Nick and Rafael!
Bjorn And that i are advertising and marketing a 2 year postdoc on integrating causality, reasoning and knowledge graphs for misinformation detection. See in this article.
Just lately, he has consulted with major financial institutions on explainable AI and its impact in fiscal establishments.
, to allow techniques to know faster and even more correct types of the earth. We have an interest in producing computational frameworks that will be able to demonstrate their conclusions, modular, re-usable
Extended abstracts of our NeurIPS paper (on PAC-Studying in first-get logic) as well as journal paper on abstracting probabilistic styles was acknowledged to KR's lately published investigate observe.
The paper discusses how to handle nested capabilities and quantification in relational probabilistic graphical versions.
The primary introduces a primary-get language for reasoning about probabilities in dynamical domains, and the second considers the automated fixing of likelihood complications https://vaishakbelle.com/ specified in organic language.
Conference link Our Focus on symbolically interpreting variational autoencoders, as well as a new learnability for SMT (satisfiability modulo theory) formulation acquired acknowledged at ECAI.