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Found 7 papers, 2 papers with code
Deep Policy Optimization with Temporal Logic Constraints
In our work, we consider the setting where the task is specified by an LTL objective and there is an additional scalar reward that we need to optimize.
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization
To be specific, we present theoretical evidence showing that the magnitude of score changes is related to the $\ell_1$ norm of the model's gradient with respect to the input image.
Adversarial Robustness
No-Reference Image Quality Assessment
+1
Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Method
Ensuring the robustness of NR-IQA methods is vital for reliable comparisons of different image processing techniques and consistent user experiences in recommendations.
On a Foundation Model for Operating Systems
This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes).
Adaptive Scheduling for Edge-Assisted DNN Serving
Based on this observation, we first design a novel scheduling algorithm to exploit the batching benefits of all requests that run the same DNN.
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation
In each learning iteration, it uses the current version of this model and an external abstract interpreter to construct a differentiable signal for provable robustness.
Safe Neurosymbolic Learning with Differentiable Symbolic Execution
We study the problem of learning worst-case-safe parameters for programs that use neural networks as well as symbolic, human-written code.
