scholarly journals Visual identification of individual Holstein-Friesian cattle via deep metric learning

2021 ◽  
Vol 185 ◽  
pp. 106133
Author(s):  
William Andrew ◽  
Jing Gao ◽  
Siobhan Mullan ◽  
Neill Campbell ◽  
Andrew W. Dowsey ◽  
...  
Keyword(s):  
Metric Learning ◽  
Visual Identification ◽  
Holstein Friesian ◽  
Deep Metric Learning ◽  
Friesian Cattle

Dysarthric Speech Recognition Based on Deep Metric Learning

2020 ◽  
Author(s):  
Yuki Takashima ◽  
Ryoichi Takashima ◽  
Tetsuya Takiguchi ◽  
Yasuo Ariki
Keyword(s):  
Speech Recognition ◽  
Metric Learning ◽  
Deep Metric Learning ◽  
Dysarthric Speech

scholarly journals Ranked List Loss for Deep Metric Learning

2021 ◽  
pp. 1-1
Author(s):  
Xinshao Wang ◽  
Yang Hua ◽  
Elyor Kodirov ◽  
Neil M Robertson
Keyword(s):  
Metric Learning ◽  
Deep Metric Learning ◽  
Ranked List

scholarly journals Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 572
Author(s):  
Alan M. Luu ◽  
Jacob R. Leistico ◽  
Tim Miller ◽  
Somang Kim ◽  
Jun S. Song
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Cytotoxic T Cells ◽  
Metric Learning ◽  
Binding Specificity ◽  
Class I ◽  
Multimodal Learning ◽  
Binding Prediction ◽  
Deep Metric Learning ◽  
Epitope Binding

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

A deep metric learning approach for weakly supervised loan default prediction1

2021 ◽  
pp. 1-13
Author(s):  
Kai Zhuang ◽  
Sen Wu ◽  
Xiaonan Gao
Keyword(s):  
Default Risk ◽  
Metric Learning ◽  
Real Life ◽  
Risk Scores ◽  
Loan Default ◽  
Default Prediction ◽  
Heterogeneous Features ◽  
Deep Metric Learning ◽  
Weakly Supervised ◽  
Decision Boundaries

To deal with the systematic risk of financial institutions and the rapid increasing of loan applications, it is becoming extremely important to automatically predict the default probability of a loan. However, this task is non-trivial due to the insufficient default samples, hard decision boundaries and numerous heterogeneous features. To the best of our knowledge, existing related researches fail in handling these three difficulties simultaneously. In this paper, we propose a weakly supervised loan default prediction model WEAKLOAN that systematically solves all these challenges based on deep metric learning. WEAKLOAN is composed of three key modules which are used for encoding loan features, learning evaluation metrics and calculating default risk scores. By doing so, WEAKLOAN can not only extract the features of a loan itself, but also model the hidden relationships in loan pairs. Extensive experiments on real-life datasets show that WEAKLOAN significantly outperforms all compared baselines even though the default loans for training are limited.

scholarly journals Deep Metric Learning for Cervical Image Classification

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 53266-53275
Author(s):  
Anabik Pal ◽  
Zhiyun Xue ◽  
Brian Befano ◽  
Ana Cecilia Rodriguez ◽  
L. Rodney Long ◽  
...  
Keyword(s):  
Image Classification ◽  
Metric Learning ◽  
Deep Metric Learning
Sign in / Sign up

Export Citation Format

Share Document