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Found 9 papers, 2 papers with code
Network Inversion of Binarised Neural Nets
While the deployment of neural networks, yielding impressive results, becomes more prevalent in various applications, their interpretability and understanding remain a critical challenge.
Exact ASP Counting with Compact Encodings
In recent years, there has been growing interest in problems beyond satisfiability, such as model counting, in the context of ASP.
Auditable Algorithms for Approximate Model Counting
Since $n$ is often large, we ask if the count of variables in the certificate can be reduced -- a crucial question for potential implementation.
Projected Model Counting: Beyond Independent Support
We show that in several such cases, we can identify a set of variables, called upper bound support (UBS), that is not necessarily a subset of the projection set, and yet counting models projected on UBS guarantees an upper bound of the true projected model count.
Synthesizing Pareto-Optimal Interpretations for Black-Box Models
For a given black-box, our approach yields a set of Pareto-optimal interpretations with respect to the correctness and explainability measures.
A Normal Form Characterization for Efficient Boolean Skolem Function Synthesis
Additionally, a specification admits a polynomial-sized functional solution iff there exists a semantically equivalent polynomial-sized SAUNF representation.
Constrained Sampling and Counting: Universal Hashing Meets SAT Solving
Constrained sampling and counting are two fundamental problems in artificial intelligence with a diverse range of applications, spanning probabilistic reasoning and planning to constrained-random verification.
Approximate Probabilistic Inference via Word-Level Counting
Techniques based on bit-level (or Boolean) hash functions require these problems to be propositionalized, making it impossible to leverage the remarkable progress made in SMT (Satisfiability Modulo Theory) solvers that can reason directly over words (or bit-vectors).
Distribution-Aware Sampling and Weighted Model Counting for SAT
We present a novel approach that works with a black-box oracle for weights of assignments and requires only an {\NP}-oracle (in practice, a SAT-solver) to solve both the counting and sampling problems.
