AIMC Topic: Learning

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Disentangled Representation Learning for Multiple Attributes Preserving Face Deidentification.

IEEE transactions on neural networks and learning systems
Face is one of the most attractive sensitive information in visual shared data. It is an urgent task to design an effective face deidentification method to achieve a balance between facial privacy protection and data utilities when sharing data. Most...

Multipath Cross Graph Convolution for Knowledge Representation Learning.

Computational intelligence and neuroscience
In the past, most of the entity prediction methods based on embedding lacked the training of local core relationships, resulting in a deficiency in the end-to-end training. Aiming at this problem, we propose an end-to-end knowledge graph embedding re...

Clustering by Errors: A Self-Organized Multitask Learning Method for Acoustic Scene Classification.

Sensors (Basel, Switzerland)
Acoustic scene classification (ASC) tries to inference information about the environment using audio segments. The inter-class similarity is a significant issue in ASC as acoustic scenes with different labels may sound quite similar. In this paper, t...

Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese-English Parallel Corpus Noise Processing.

Computational intelligence and neuroscience
This paper uses neural network as a predictive model and genetic algorithm as an online optimization algorithm to simulate the noise processing of Chinese-English parallel corpus. At the same time, according to the powerful random global search mecha...

A Multi-RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency-Based Scientific Influence Modeling.

Computational intelligence and neuroscience
Computer science discipline includes many research fields, which mutually influence and promote each other's development. This poses two great challenges of predicting the research topics of each research field. One is how to model fine-grained topic...

Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials.

Communications biology
Robotic assistance via motorized robotic arm manipulators can be of valuable assistance to individuals with upper-limb motor disabilities. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. Howeve...

End-to-End Autonomous Exploration with Deep Reinforcement Learning and Intrinsic Motivation.

Computational intelligence and neuroscience
Developing artificial intelligence (AI) agents is challenging for efficient exploration in visually rich and complex environments. In this study, we formulate the exploration question as a reinforcement learning problem and rely on intrinsic motivati...

A Novel Training and Collaboration Integrated Framework for Human-Agent Teleoperation.

Sensors (Basel, Switzerland)
Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operati...

Social learning in swarm robotics.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
In this paper, we present an implementation of social learning for swarm robotics. We consider social learning as a distributed online reinforcement learning method applied to a collective of robots where sensing, acting and coordination are performe...

Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
We survey and reflect on how learning (in the form of individual learning and/or culture) can augment evolutionary approaches to the joint optimization of the body and control of a robot. We focus on a class of applications where the goal is to evolv...