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Found 19 papers, 10 papers with code
Cross-Domain Few-Shot Learning via Adaptive Transformer Networks
Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications.
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients.
Distributed Semi-supervised Fuzzy Regression with Interpolation Consistency Regularization
In this work, we proposed a distributed Fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR.
Self-Awareness Safety of Deep Reinforcement Learning in Road Traffic Junction Driving
Autonomous driving has been at the forefront of public interest, and a pivotal debate to widespread concerns is safety in the transportation system.
CogniFNN: A Fuzzy Neural Network Framework for Cognitive Word Embedding Evaluation
Word embeddings can reflect the semantic representations, and the embedding qualities can be comprehensively evaluated with human natural reading-related cognitive data sources.
CDE-GAN: Cooperative Dual Evolution Based Generative Adversarial Network
In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multi-objective optimization.
Weak Human Preference Supervision For Deep Reinforcement Learning
The current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed preference between pairs of trajectory segments.
An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification
In this study, we proposed a deep learning framework guided by the visual evoked potentials, called the Event-Related Potential (ERP)-Long short-term memory (LSTM) framework, extracted by EEG signals for visual classification.
A General Approach for Using Deep Neural Network for Digital Watermarking
Technologies of the Internet of Things (IoT) facilitate digital contents such as images being acquired in a massive way.
A Rule-Based EEG Classification System for Discrimination of Hand Motor Attempts in Stroke Patients
Then, we proposed a rule-based EEG classification system for identifying the side in motion for stroke patients.
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications
Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis
Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge.
Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface
This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes application to three Brain Computer Interface (BCI) applications.
Reinforcement Learning from Hierarchical Critics
Within the actor-critic RL, we introduce multiple cooperative critics from two levels of the hierarchy and propose a reinforcement learning from hierarchical critics (RLHC) algorithm.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Extraction of SSVEPs-based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients
We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the inter-ictal phase but a reverse trend in patients in the pre-ictal phase.
Human-Computer Interaction
Effects of Repetitive SSVEPs on EEG Complexity using Multiscale Inherent Fuzzy Entropy
These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.
Signal Processing
Semi-supervised Feature Learning For Improving Writer Identification
In this study, a semi-supervised feature learning pipeline was proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously.
Inherent fuzzy entropy for the improvement of EEG complexity evaluation
In recent years, the concept of entropy has been widely used to measure the dynamic complexity of signals.
