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Found 15 papers, 4 papers with code
Omega-Regular Decision Processes
Regular decision processes (RDPs) are a subclass of non-Markovian decision processes where the transition and reward functions are guarded by some regular property of the past (a lookback).
Omega-Regular Reward Machines
Reinforcement learning (RL) is a powerful approach for training agents to perform tasks, but designing an appropriate reward mechanism is critical to its success.
Recursive Reinforcement Learning
Recursion is the fundamental paradigm to finitely describe potentially infinite objects.
Alternating Good-for-MDP Automata
The surprising answer is that we have to pay significantly less when we instead expand the good-for-MDP property to alternating automata: like the nondeterministic GFM automata obtained from deterministic Rabin automata, the alternating good-for-MDP automata we produce from deterministic Streett automata are bi-linear in the the size of the deterministic automaton and its index, and can therefore be exponentially more succinct than minimal nondeterministic B\"uchi automata.
Enhancing Adversarial Training with Second-Order Statistics of Weights
In this paper, we show that treating model weights as random variables allows for enhancing adversarial training through \textbf{S}econd-Order \textbf{S}tatistics \textbf{O}ptimization (S$^2$O) with respect to the weights.
Weight Expansion: A New Perspective on Dropout and Generalization
While dropout is known to be a successful regularization technique, insights into the mechanisms that lead to this success are still lacking.
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
The increasing use of Machine Learning (ML) components embedded in autonomous systems -- so-called Learning-Enabled Systems (LESs) -- has resulted in the pressing need to assure their functional safety.
Mungojerrie: Reinforcement Learning of Linear-Time Objectives
Reinforcement learning synthesizes controllers without prior knowledge of the system.
Model-free Reinforcement Learning for Branching Markov Decision Processes
We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs).
Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles
The utilisation of Deep Learning (DL) is advancing into increasingly more sophisticated applications.
Detecting Operational Adversarial Examples for Reliable Deep Learning
The utilisation of Deep Learning (DL) raises new challenges regarding its dependability in critical applications.
Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features
Intensive research has been conducted on the verification and validation of deep neural networks (DNNs), aiming to understand if, and how, DNNs can be applied to safety critical applications.
Simple Stochastic Games with Almost-Sure Energy-Parity Objectives are in NP and coNP
We study stochastic games with energy-parity objectives, which combine quantitative rewards with a qualitative $\omega$-regular condition: The maximizer aims to avoid running out of energy while simultaneously satisfying a parity condition.
Computer Science and Game Theory Logic in Computer Science
How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?
This paper studies the novel concept of weight correlation in deep neural networks and discusses its impact on the networks' generalisation ability.
How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks
This paper studies the novel concept of weight correlation in deep neural networks and discusses its impact on the networks' generalisation ability.
