113 citations · 138 across the 5 of their papers we have counts for
7 papers
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications
Dongqi Han, Zhiliang Wang, Wenqi Chen +6
Unsupervised Deep Learning (DL) techniques have been widely used in various security-related anomaly detection applications, owing to the great promise of being able to detect unfo…
Goal-Directed Planning by Reinforcement Learning and Active Inference
Dongqi Han, Kenji Doya, Jun Tani
What is the difference between goal-directed and habitual behavior? We propose a novel computational framework of decision making with Bayesian inference, in which everything is in…
Learning Memory-Dependent Continuous Control from Demonstrations
Siqing Hou, Dongqi Han, Jun Tani
Efficient exploration has presented a long-standing challenge in reinforcement learning, especially when rewards are sparse. A developmental system can overcome this difficulty by…
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks
Dongqi Han, Erik De Schutter, Sungho Hong
Feedforward networks (FFN) are ubiquitous structures in neural systems and have been studied to understand mechanisms of reliable signal and information transmission. In many FFNs,…
Variational Recurrent Models for Solving Partially Observable Control Tasks
Dongqi Han, Kenji Doya, Jun Tani
In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how…
Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning
Tadashi Kozuno, Dongqi Han, Kenji Doya
In real-world applications of reinforcement learning (RL), noise from inherent stochasticity of environments is inevitable. However, current policy evaluation algorithms, which pla…