A survey on curriculum learning
Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …
easier data to harder data, which imitates the meaningful learning order in human curricula …
Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review
The adoption of smart digital technologies in the education system has grown exponentially
over the years, creating new possibilities to improve teaching and enhance learning. Against …
over the years, creating new possibilities to improve teaching and enhance learning. Against …
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Recent deep networks are capable of memorizing the entire data even when the labels are
completely random. To overcome the overfitting on corrupted labels, we propose a novel …
completely random. To overcome the overfitting on corrupted labels, we propose a novel …
Active bias: Training more accurate neural networks by emphasizing high variance samples
Self-paced learning and hard example mining re-weight training instances to improve
learning accuracy. This paper presents two improved alternatives based on lightweight …
learning accuracy. This paper presents two improved alternatives based on lightweight …
Self-paced ARIMA for robust time series prediction
For time series prediction tasks, the autoregressive integrated moving average (ARIMA)
model is one of the most classical and popular linear models, and extended applications …
model is one of the most classical and popular linear models, and extended applications …
TSASNet: Tooth segmentation on dental panoramic X-ray images by Two-Stage Attention Segmentation Network
Tooth segmentation acts as a crucial and fundamental role in dentistry for doctors to make
diagnosis and treatment plans. In this paper, we propose a Two-Stage Attention …
diagnosis and treatment plans. In this paper, we propose a Two-Stage Attention …
Leveraging prior-knowledge for weakly supervised object detection under a collaborative self-paced curriculum learning framework
Weakly supervised object detection is an interesting yet challenging research topic in
computer vision community, which aims at learning object models to localize and detect the …
computer vision community, which aims at learning object models to localize and detect the …
SLNL: a novel method for gene selection and phenotype classification
One of the central tasks of genome research is to predict phenotypes and discover some
important gene biomarkers. However, there are three main problems in analyzing genomics …
important gene biomarkers. However, there are three main problems in analyzing genomics …
Few-example object detection with model communication
In this paper, we study object detection using a large pool of unlabeled images and only a
few labeled images per category, named “few-example object detection”. The key challenge …
few labeled images per category, named “few-example object detection”. The key challenge …
Adaptive reverse graph learning for robust subspace learning
Subspace learning decreases the dimensions for high-dimensional data by projecting the
original data into a low-dimensional subspace, as well as preserving the similarity among …
original data into a low-dimensional subspace, as well as preserving the similarity among …