AIMC Topic: Learning

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Language models can learn complex molecular distributions.

Nature communications
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility of generative models for the inverse design of novel functional compo...

A Study on Mobile Resources for Language Education of Preschool Children Based on Wireless Network Technology in Artificial Intelligence Context.

Computational and mathematical methods in medicine
Preschool language education is a requirement of basic education reform as well as a requirement for children's growth in all aspects of body and mind. It is extremely important and valuable in encouraging the entire growth of preschool education as ...

A Learning-Rate Modulable and Reliable TiO Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Realization of memristor-based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system-level. In this sense, uniform and reliable titanium oxide (TiO ) memristor...

Graph Transformer Networks: Learning meta-path graphs to improve GNNs.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have been widely applied to various fields due to their powerful representations of graph-structured data. Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homoge...

Zero-Shot Deep Domain Adaptation With Common Representation Learning.

IEEE transactions on pattern analysis and machine intelligence
Domain Adaptation aims at adapting the knowledge learned from a domain (source-domain) to another (target-domain). Existing approaches typically require a portion of task-relevant target-domain data a priori. We propose an approach, zero-shot deep do...

APANet: Auto-Path Aggregation for Future Instance Segmentation Prediction.

IEEE transactions on pattern analysis and machine intelligence
Despite the remarkable progress achieved in conventional instance segmentation, the problem of predicting instance segmentation results for unobserved future frames remains challenging due to the unobservability of future data. Existing methods mainl...

Adaptive Contrastive Learning with Label Consistency for Source Data Free Unsupervised Domain Adaptation.

Sensors (Basel, Switzerland)
Unsupervised domain adaptation, which aims to alleviate the domain shift between source domain and target domain, has attracted extensive research interest; however, this is unlikely in practical application scenarios, which may be due to privacy iss...

A Path-Planning Approach Based on Potential and Dynamic Q-Learning for Mobile Robots in Unknown Environment.

Computational intelligence and neuroscience
The path-planning approach plays an important role in determining how long the mobile robots can travel. To solve the path-planning problem of mobile robots in an unknown environment, a potential and dynamic Q-learning (PDQL) approach is proposed, wh...

Deep Graph Learning for Anomalous Citation Detection.

IEEE transactions on neural networks and learning systems
Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, that is, anomaly detection in a citation network. Cit...

Automated Anomaly Detection via Curiosity-Guided Search and Self-Imitation Learning.

IEEE transactions on neural networks and learning systems
Anomaly detection is an important data mining task with numerous applications, such as intrusion detection, credit card fraud detection, and video surveillance. However, given a specific complicated task with complicated data, the process of building...