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Found 15 papers, 1 papers with code
Scaling Learning based Policy Optimization for Temporal Tasks via Dropout
We show how this learning problem is similar to training recurrent neural networks (RNNs), where the number of recurrent units is proportional to the temporal horizon of the agent's task objectives.
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning
We consider classical distribution-agnostic framework and algorithms minimising empirical risks and potentially subjected to some weights regularisation.
Safety Under Uncertainty: Tight Bounds with Risk-Aware Control Barrier Functions
We propose a novel class of risk-aware control barrier functions (RA-CBFs) for the control of stochastic safety-critical systems.
A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systems
In this paper, we present a model for the verification of Neural Network (NN) controllers for general STL specifications using a custom neural architecture where we map an STL formula into a feed-forward neural network with ReLU activation.
Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives
In this paper, we consider the problem of synthesizing a controller in the presence of uncertainty such that the resulting closed-loop system satisfies certain hard constraints while optimizing certain (soft) performance objectives.
Risk-Bounded Control with Kalman Filtering and Stochastic Barrier Functions
Our goal is to design controllers that bound the probability of a system failure in finite-time to a given desired value.
Neural Network Repair with Reachability Analysis
Formally verifying the safety and robustness of well-trained DNNs and learning-enabled systems under attacks, model uncertainties, and sensing errors is essential for safe autonomy.
Reachability Analysis of Convolutional Neural Networks
Besides the computation of reachable sets, our approach is also capable of backtracking to the input domain given an output reachable set.
Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Components
Additionally, we present three driving scenarios and demonstrate how our requirements-driven testing framework can be used to identify critical system behaviors, which can be used to support the development process.
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection
To demonstrate the superiority and generality of the proposed method, we evaluate the proposed method on five crack datasets and compare it with state-of-the-art crack detection, edge detection, semantic segmentation methods.
Fast Construction of Correcting Ensembles for Legacy Artificial Intelligence Systems: Algorithms and a Case Study
This paper presents a technology for simple and computationally efficient improvements of a generic Artificial Intelligence (AI) system, including Multilayer and Deep Learning neural networks.
Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic
Freeway merging in congested traffic is a significant challenge toward fully automated driving.
Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics
In many robotic applications, some aspects of the system dynamics can be modeled accurately while others are difficult to obtain or model.
Multi-level Contextual RNNs with Attention Model for Scene Labeling
Context in image is crucial for scene labeling while existing methods only exploit local context generated from a small surrounding area of an image patch or a pixel, by contrast long-range and global contextual information is ignored.
MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking
Variations in the appearance of a tracked object, such as changes in geometry/photometry, camera viewpoint, illumination, or partial occlusion, pose a major challenge to object tracking.
