Search Results for author:
Found 7 papers, 3 papers with code
Recalibrating classifiers for interpretable abusive content detection
We investigate the use of machine learning classifiers for detecting online abuse in empirical research.
Exposing Previously Undetectable Faults in Deep Neural Networks
Existing methods for testing DNNs solve the oracle problem by constraining the raw features (e. g. image pixel values) to be within a small distance of a dataset example for which the desired DNN output is known.
Evaluating Robustness to Context-Sensitive Feature Perturbations of Different Granularities
We cannot guarantee that training datasets are representative of the distribution of inputs that will be encountered during deployment.
Learning Concise Models from Long Execution Traces
Abstract models of system-level behaviour have applications in design exploration, analysis, testing and verification.
Formal Languages and Automata Theory Software Engineering
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives.
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
+5
Adaptive Generation of Unrestricted Adversarial Inputs
Neural networks are vulnerable to adversarially-constructed perturbations of their inputs.
Automatic Heap Layout Manipulation for Exploitation
In this paper we present the first automatic approach to the problem, based on pseudo-random black-box search.
Cryptography and Security Programming Languages
