Advances in Logical Frameworks and Probabilistic Modeling

The field of logical frameworks and probabilistic modeling is witnessing significant advancements, driven by the development of innovative techniques and tools. One of the key areas of focus is the creation of efficient and scalable methods for learning Bayesian networks, with researchers exploring ensemble approaches and divide-and-conquer strategies to improve accuracy and reduce computational costs. Additionally, there is a growing interest in the application of logical frameworks to natural language semantics, with diagrammatic calculi and functional models being investigated. Another important area of research is the development of new probabilistic models, such as binned semiparametric Bayesian networks, which offer improved performance and efficiency. Noteworthy papers in this area include the introduction of BayesL, a logical framework for specifying and verifying Bayesian networks, and the development of scalable structure learning algorithms for Bayesian networks. Overall, these advancements have the potential to significantly impact a range of applications, from artificial intelligence and machine learning to natural language processing and decision-making under uncertainty. Notable papers include: BayesL, which introduces a novel logical framework for Bayesian networks, and Scalable Structure Learning of Bayesian Networks by Learning Algorithm Ensembles, which proposes an ensemble approach to learning Bayesian networks.

Sources

Binned semiparametric Bayesian networks

Scott's Representation Theorem and the Univalent Karoubi Envelope

Computation by infinite descent made explicit

Using SBPF to Accelerate Kernel Memory Access From Userspace

Questions as cognitive filters

Model-theoretic Forcing in Transition Algebra

Scalable Structure Learning of Bayesian Networks by Learning Algorithm Ensembles

Threadbox: Sandboxing for Modular Security

BayesL: Towards a Logical Framework for Bayesian Networks

Have Object-Oriented Languages Missed a Trick with Class Function and its Subclasses?

A Diagrammatic Calculus for a Functional Model of Natural Language Semantics

Data Classification with Dynamically Growing and Shrinking Neural Networks

Globality and Regions

A Proof-Theoretic View of Basic Intuitionistic Conditional Logic (Extended Version)

Subtyping in DHOL -- Extended preprint

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