ResearchTrend.AI
Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice

Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice

Kuldeep S. Meel
S. Akshay
ArXiv (abs)PDFHTML

Papers citing "Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice"

5 / 5 papers shown
Title
Systematic Parameter Decision in Approximate Model Counting
Systematic Parameter Decision in Approximate Model Counting
Jinping Lei
Toru Takisaka
Junqiang Peng
Mingyu Xiao
106
0
0
08 Apr 2025
Locally-Minimal Probabilistic Explanations
Locally-Minimal Probabilistic Explanations
Yacine Izza
Kuldeep S. Meel
Sasha Rubin
63
3
0
19 Dec 2023
Projected Model Counting: Beyond Independent Support
Projected Model Counting: Beyond Independent Support
Jiong Yang
Supratik Chakraborty
Kuldeep S. Meel
45
4
0
18 Oct 2021
Arjun: An Efficient Independent Support Computation Technique and its Applications to Counting and Sampling
Arjun: An Efficient Independent Support Computation Technique and its Applications to Counting and Sampling
Mate Soos
Kuldeep S. Meel
94
21
0
18 Oct 2021
Partition Function Estimation: A Quantitative Study
Partition Function Estimation: A Quantitative Study
Durgesh Kumar Agrawal
Yash Pote
Kuldeep S. Meel
71
11
0
24 May 2021