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Found 6 papers, 3 papers with code
Reinforcement learning guided fuzz testing for a browser's HTML rendering engine
Generation-based fuzz testing can uncover various bugs and security vulnerabilities.
Learning Robust Controllers Via Probabilistic Model-Based Policy Search
Model-based Reinforcement Learning estimates the true environment through a world model in order to approximate the optimal policy.
Model-based Reinforcement Learning
reinforcement-learning
+1
Odd-One-Out Representation Learning
The effective application of representation learning to real-world problems requires both techniques for learning useful representations, and also robust ways to evaluate properties of representations.
Probabilistic selection of inducing points in sparse Gaussian processes
Sparse Gaussian processes and various extensions thereof are enabled through inducing points, that simultaneously bottleneck the predictive capacity and act as the main contributor towards model complexity.
Recurrent Neural Networks for Fuzz Testing Web Browsers
Generation-based fuzzing is a software testing approach which is able to discover different types of bugs and vulnerabilities in software.
A Topic Model Approach to Multi-Modal Similarity
Calculating similarities between objects defined by many heterogeneous data modalities is an important challenge in many multimedia applications.
