Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
A comprehensive survey on transfer learning
Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …
transferring the knowledge contained in different but related source domains. In this way, the …
A decade survey of transfer learning (2010–2020)
Transfer learning (TL) has been successfully applied to many real-world problems that
traditional machine learning (ML) cannot handle, such as image processing, speech …
traditional machine learning (ML) cannot handle, such as image processing, speech …
A general knowledge distillation framework for counterfactual recommendation via uniform data
Recommender systems are feedback loop systems, which often face bias problems such as
popularity bias, previous model bias and position bias. In this paper, we focus on solving the …
popularity bias, previous model bias and position bias. In this paper, we focus on solving the …
Transfer adaptation learning: A decade survey
The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …
environment. Domain is referred to as the state of the world at a certain moment. A research …
Curriculum meta-learning for next POI recommendation
Next point-of-interest (POI) recommendation is a hot research field where a recent emerging
scenario, next POI to search recommendation, has been deployed in many online map …
scenario, next POI to search recommendation, has been deployed in many online map …
Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of planetary gearboxes
The domain adaptation (DA) model, aiming to solve the task of unlabeled or less-labeled
target domain fault classification through the training of labeled source domain fault data, is …
target domain fault classification through the training of labeled source domain fault data, is …
Combining a single shot multibox detector with transfer learning for ship detection using sentinel-1 SAR images
Y Wang, C Wang, H Zhang - Remote sensing letters, 2018 - Taylor & Francis
With the capabilities of constant use in any weather condition and a wide coverage area,
synthetic aperture radar (SAR) technology is widely used in marine transportation safety and …
synthetic aperture radar (SAR) technology is widely used in marine transportation safety and …
CARM: Confidence-aware recommender model via review representation learning and historical rating behavior in the online platforms
The recommendation systems in the online platforms often suffer from the rating data
sparseness and information overload issues. Previous studies on this topic often leverage …
sparseness and information overload issues. Previous studies on this topic often leverage …
Fuzzy multiple-source transfer learning
Transfer learning is gaining increasing attention due to its ability to leverage previously
acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy …
acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy …