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Found 18 papers, 10 papers with code
The VEP Booster: A Closed-Loop AI System for Visual EEG Biomarker Auto-generation
Effective visual brain-machine interfaces (BMI) is based on reliable and stable EEG biomarkers.
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators
Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible.
Understanding and Improving Training-free Loss-based Diffusion Guidance
Adding additional control to pretrained diffusion models has become an increasingly popular research area, with extensive applications in computer vision, reinforcement learning, and AI for science.
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
In this paper, we propose a framework for SNNs in time-series forecasting tasks, leveraging the efficiency of spiking neurons in processing temporal information.
Toward Open-ended Embodied Tasks Solving
Empowering embodied agents, such as robots, with Artificial Intelligence (AI) has become increasingly important in recent years.
AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine
However, selecting the appropriate edge feature to define patient similarity and construct the graph is challenging, given that each patient is depicted by high-dimensional features from diverse sources.
Energy-Efficient Visual Search by Eye Movement and Low-Latency Spiking Neural Network
Here, we carry out experiments to examine human visual search behaviors and establish the first SNN-based visual search model.
Habits and goals in synergy: a variational Bayesian framework for behavior
The habitual behavior is generated by using prior distribution of intention, which is goal-less; and the goal-directed behavior is generated by the posterior distribution of intention, which is conditioned on the goal.
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning
Recent studies propose leveraging the rich contextual information in data provenance to detect threats in a host.
Variational oracle guiding for reinforcement learning
How to make intelligent decisions is a central problem in machine learning and cognitive science.
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications
Experimental results show that DeepAID can provide high-quality interpretations for unsupervised DL models while meeting the special requirements of security domains.
Goal-Directed Planning by Reinforcement Learning and Active Inference
Habitual behavior, which is obtained from the prior distribution of ${z}$, is acquired by reinforcement learning.
Learning Memory-Dependent Continuous Control from Demonstrations
This paper builds on the idea of replaying demonstrations for memory-dependent continuous control, by proposing a novel algorithm, Recurrent Actor-Critic with Demonstration and Experience Replay (READER).
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks
This mechanism boosts information transfer carried by a propagating spike signal and thereby supports reliable spike signal and information transmission in a deep FFN.
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors
Many adversarial attacks have been proposed to evaluate the robustness of ML-based NIDSs.
Variational Recurrent Models for Solving Partially Observable Control Tasks
In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how to extract information from the raw observations to solve the task, and how to improve the policy.
Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning
We provide detailed theoretical analysis of the new algorithm that shows its efficiency and noise-tolerance inherited from Retrace and advantage learning.
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks
Furthermore, we show that the self-developed compositionality of the network enhances faster re-learning when adapting to a new task that is a re-composition of previously learned sub-goals, than when starting from scratch.
