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Found 9 papers, 1 papers with code
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Our attack technique targets the inherent vulnerabilities of NLP models, allowing us to generate samples even without interacting with the victim NLP model, as long as it is based on pre-trained language models (PLMs).
Quantifying Robustness to Adversarial Word Substitutions
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations.
Can Graph Neural Networks Learn to Solve MaxSAT Problem?
With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability (SAT), which shows the potential in bridging the gap between machine learning and symbolic reasoning.
ε-weakened Robustness of Deep Neural Networks
This paper introduces a notation of $\varepsilon$-weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs).
Counting the Number of Solutions to Constraints
Compared with constraint satisfaction problems, counting problems have received less attention.
ACFNet: Attentional Class Feature Network for Semantic Segmentation
Recent works have made great progress in semantic segmentation by exploiting richer context, most of which are designed from a spatial perspective.
A New Probabilistic Algorithm for Approximate Model Counting
Constrained counting is important in domains ranging from artificial intelligence to software analysis.
Diversified Top-k Partial MaxSAT Solving
We introduce a diversified top-k partial MaxSAT problem, a combination of partial MaxSAT problem and enumeration problem.
A Tool for Computing and Estimating the Volume of the Solution Space of SMT(LA)
There are already quite a few tools for solving the Satisfiability Modulo Theories (SMT) problems.
