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Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition
TLDR
DABLC combines the advantages of both external dictionary resources and deep attention neural networks to aid the identification of rare diseases and complex disease names; moreover, it reduces the impact of tagging inconsistency. Expand
Catboost-based Framework with Additional User Information for Social Media Popularity Prediction
TLDR
To make full use of the dataset for model training, a dataset augmentation strategy based on pseudo labels is proposed, which achieves the 2nd place in the leader board of the Grand Challenge of Social Media Prediction. Expand
Active Transfer Learning
TLDR
The orthogonal projection matrix and the weight coefficient vector are introduced to extend maximum mean discrepancy (MMD) so that it can minimize MMD and simultaneously eliminate the negative transfer. Expand
Joint sparse representation and locality preserving projection for feature extraction
TLDR
A novel unsupervised feature extraction method, i.e., joint sparse representation and locality preserving projection (JSRLPP), in which the graph construction and feature extraction are simultaneously carried out, which adaptively learns the similarity matrix by sparse representation, and at the same time, learns the projection matrix by preserving local structure. Expand
Random Forest Exploiting Post-related and User-related Features for Social Media Popularity Prediction
TLDR
This paper proposes to use multi-aspect features combined with the random forest (RF) model for popularity predictions and achieves the 4nd place in the leader board of the Grand Challenge of SMHP in ACM Multimedia 2018. Expand
Learning Shared Semantic Space with Correlation Alignment for Cross-Modal Event Retrieval
TLDR
The effectiveness of S3CA, which aligns nonlinear correlations of multimodal data distributions in deep neural networks designed for heterogeneous data, is outperforming the state-of-the-art methods. Expand
Cross-domain Beauty Item Retrieval via Unsupervised Embedding Learning
TLDR
Unsupervised embedding learning (UEL) is proposed for cross-domain beauty and personal care product retrieval to finetune the convolutional neural network (CNN) and utilizes the non-parametric softmax to train the CNN model as instance-level classification. Expand
Deep Semantic Space with Intra-class Low-rank Constraint for Cross-modal Retrieval
In this paper, a novel Deep Semantic Space learning model with Intra-class Low-rank constraint (DSSIL) is proposed for cross-modal retrieval, which is composed of two subnetworks forExpand
Improving cross-dimensional weighting pooling with multi-scale feature fusion for image retrieval
TLDR
This paper aggregate multi-scale features extracted by convolutional neural networks using the proposed fully cross-dimensional weighting pooling (FCroW) method, taking into account multiple aspects of visual features captured by the networks. Expand
Supervised Group Sparse Representation via Intra-class Low-Rank Constraint
TLDR
This paper proposes a novel supervised group sparse representation via intra-class low-rank constraint (GSRILC), which attempts to use the compact projection features in a new subspace for data reconstruction. Expand
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