13 citations · 42 across the 9 of their papers we have counts for
6 papers · 1 filter
eGAN: Unsupervised approach to class imbalance using transfer learning
Ademola Okerinde, Lior Shamir, William Hsu +2
Class imbalance is an inherent problem in many machine learning classification tasks. This often leads to trained models that are unusable for any practical purpose. In this study…
AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines
Ademola Okerinde, Lior Shamir, William Hsu +1
Ceramic insulators are important to electronic systems, designed and installed to protect humans from the danger of high voltage electric current. However, insulators are not immor…
Feature Selection for Learning to Predict Outcomes of Compute Cluster Jobs with Application to Decision Support
Adedolapo Okanlawon, Huichen Yang, Avishek Bose +3
We present a machine learning framework and a new test bed for data mining from the Slurm Workload Manager for high-performance computing (HPC) clusters. The focus was to find a me…
Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies
Vahid Behzadan, William Hsu
This paper proposes a novel scheme for the watermarking of Deep Reinforcement Learning (DRL) policies. This scheme provides a mechanism for the integration of a unique identifier w…
Adversarial Exploitation of Policy Imitation
Vahid Behzadan, William Hsu
This paper investigates a class of attacks targeting the confidentiality aspect of security in Deep Reinforcement Learning (DRL) policies. Recent research have established the vuln…
Analysis and Improvement of Adversarial Training in DQN Agents With Adversarially-Guided Exploration (AGE)
Vahid Behzadan, William Hsu
This paper investigates the effectiveness of adversarial training in enhancing the robustness of Deep Q-Network (DQN) policies to state-space perturbations. We first present a form…