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Prediction of drug-target interactions through multi-task learning.

Scientific reports
Identifying the binding between the target proteins and molecules is essential in drug discovery. The multi-task learning method has been introduced to facilitate knowledge sharing among tasks when the amount of information for each task is small. Ho...

ReLMole: Molecular Representation Learning Based on Two-Level Graph Similarities.

Journal of chemical information and modeling
Molecular representation is a critical part of various prediction tasks for physicochemical properties of molecules and drug design. As graph notations are common in expressing the structural information of chemical compounds, graph neural networks (...

What and Where: Learn to Plug Adapters via NAS for Multidomain Learning.

IEEE transactions on neural networks and learning systems
As an important and challenging problem, multidomain learning (MDL) typically seeks a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network. Usually, existing ways of adapter plugging and structure...

Orientation-Preserving Rewards' Balancing in Reinforcement Learning.

IEEE transactions on neural networks and learning systems
Auxiliary rewards are widely used in complex reinforcement learning tasks. However, previous work can hardly avoid the interference of auxiliary rewards on pursuing the main rewards, which leads to the destruction of the optimal policy. Thus, it is c...

Frequency Principle in Broad Learning System.

IEEE transactions on neural networks and learning systems
Deep neural networks have achieved breakthrough improvement in various application fields. Nevertheless, they usually suffer from a time-consuming training process because of the complicated structures of neural networks with a huge number of paramet...

Towards Transfer Learning Techniques-BERT, DistilBERT, BERTimbau, and DistilBERTimbau for Automatic Text Classification from Different Languages: A Case Study.

Sensors (Basel, Switzerland)
The Internet of Things is a paradigm that interconnects several smart devices through the internet to provide ubiquitous services to users. This paradigm and Web 2.0 platforms generate countless amounts of textual data. Thus, a significant challenge ...

Co-Learning Computational and Design Thinking Using Educational Robotics: A Case of Primary School Learners in Namibia.

Sensors (Basel, Switzerland)
In a two-day educational robotics workshop in a Namibian primary boarding school, learners with no programming skills managed to apply both computational and design thinking skills with the aid of educational robotics. Educational robotics has proved...

A Routing Optimization Method for Software-Defined Optical Transport Networks Based on Ensembles and Reinforcement Learning.

Sensors (Basel, Switzerland)
Optical transport networks (OTNs) are widely used in backbone- and metro-area transmission networks to increase network transmission capacity. In the OTN, it is particularly crucial to rationally allocate routes and maximize network capacities. By em...

ExpGCN: Review-aware Graph Convolution Network for explainable recommendation.

Neural networks : the official journal of the International Neural Network Society
Existing works in recommender system have widely explored extracting reviews as explanations beyond user-item interactions, and formulated the explanation generation as a ranking task to enhance item recommendation performance. To associate explanati...

Anomaly Detection in Industrial IoT Using Distributional Reinforcement Learning and Generative Adversarial Networks.

Sensors (Basel, Switzerland)
Anomaly detection is one of the biggest issues of security in the Industrial Internet of Things (IIoT) due to the increase in cyber attack dangers for distributed devices and critical infrastructure networks. To face these challenges, the Intrusion D...