| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Delora Baptista, João Correia, Bruno Pereira, Miguel Roch. Evaluating molecular representations in machine learning models for drug response prediction and interpretability. Journal of integrative bioinformatics. 2022-08-26. PMID:36017668. |
however, in recent years, end-to-end deep learning (dl) methods that can learn feature representations directly from line notations or molecular graphs have been proposed as alternatives to using precomputed features. |
2022-08-26 |
2023-08-14 |
Not clear |
| Hongyi Gu, Burhaneddin Yaman, Steen Moeller, Jutta Ellermann, Kamil Ugurbil, Mehmet Akçakay. Revisiting [Formula: see text]-wavelet compressed-sensing MRI in the era of deep learning. Proceedings of the National Academy of Sciences of the United States of America. vol 119. issue 33. 2022-08-08. PMID:35939712. |
using ideas such as algorithm unrolling and advanced optimization methods over large databases that dl algorithms utilize, along with conventional insights from wavelet representations and cs theory, we show that [formula: see text]-wavelet cs can be fine-tuned to a level close to dl reconstruction for accelerated mri. |
2022-08-08 |
2023-08-14 |
Not clear |
| Sergio J Sanabria, Amir M Pirmoazen, Jeremy Dahl, Aya Kamaya, Ahmed El Kaffa. Comparative Study of Raw Ultrasound Data Representations in Deep Learning to Classify Hepatic Steatosis. Ultrasound in medicine & biology. 2022-08-01. PMID:35914993. |
the aim of this work was to compare dl classification scores for liver steatosis using different data representations constructed from raw us data. |
2022-08-01 |
2023-08-14 |
Not clear |
| Wei Tang, Fazhi He, Yu Liu, Yansong Dua. MATR: Multimodal Medical Image Fusion via Multiscale Adaptive Transformer. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2022-07-28. PMID:35901003. |
with powerful feature representation ability, deep learning (dl)-based methods have improved such fusion results but still have not achieved satisfactory performance. |
2022-07-28 |
2023-08-14 |
Not clear |
| Tarek Berghout, Mohamed Benbouzid, Toufik Bentrcia, Yassine Amirat, Leïla-Hayet Mous. Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis. Entropy (Basel, Switzerland). vol 24. issue 7. 2022-07-27. PMID:35885232. |
in this specific topic, health deterioration modeling with deep learning (dl) is the widely studied representation learning tool due to its adaptation ability to rapid changes in data complexity and drift. |
2022-07-27 |
2023-08-14 |
Not clear |
| Tarek Berghout, Mohamed Benbouzid, Toufik Bentrcia, Yassine Amirat, Leïla-Hayet Mous. Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis. Entropy (Basel, Switzerland). vol 24. issue 7. 2022-07-27. PMID:35885232. |
in this context, the present paper proposes an investigation of further deeper representations by exposing dl models themselves to recurrent expansion with multiple repeats. |
2022-07-27 |
2023-08-14 |
Not clear |
| Tarek Berghout, Mohamed Benbouzid, Toufik Bentrcia, Yassine Amirat, Leïla-Hayet Mous. Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis. Entropy (Basel, Switzerland). vol 24. issue 7. 2022-07-27. PMID:35885232. |
such a recurrent expansion of dl (redl) allows new, more meaningful representations to be explored by repeatedly using generated feature maps and responses to create new robust models. |
2022-07-27 |
2023-08-14 |
Not clear |
| Nahed Tawfik, Heba A Elnemr, Mahmoud Fakhr, Moawad I Dessouky, Fathi E Abd El-Sami. Multimodal Medical Image Fusion Using Stacked Auto-encoder in NSCT Domain. Journal of digital imaging. 2022-06-29. PMID:35768753. |
the dl methods in image fusion have become an active topic due to their high feature extraction and data representation ability. |
2022-06-29 |
2023-08-14 |
human |
| Lu Chen, Chunchao Xia, Huaiqiang Su. Recent advances of deep learning in psychiatric disorders. Precision clinical medicine. vol 3. issue 3. 2022-06-13. PMID:35694413. |
different from conventional machine learning algorithm, dl is able to learn useful representations and features directly from raw data through hierarchical nonlinear transformations. |
2022-06-13 |
2023-08-14 |
Not clear |
| Zhiqiang Zhen. The Classification of Music and Art Genres under the Visual Threshold of Deep Learning. Computational intelligence and neuroscience. vol 2022. 2022-05-31. PMID:35634048. |
in recent times, deep learning (dl) models have been widely used due to their characteristics of automatic extracting advanced features and contextual representation from actual music or processed data. |
2022-05-31 |
2023-08-13 |
cat |
| Sang-Soo Baek, Eun-Young Jung, JongCheol Pyo, Yakov Pachepsky, Heejong Son, Kyung Hwa Ch. Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level. Water research. vol 218. 2022-05-06. PMID:35523035. |
our work was an explicit representation of the taxonomic and ecological hierarchy of the dl model structure. |
2022-05-06 |
2023-08-13 |
Not clear |
| Kate Duffy, Thomas J Vandal, Weile Wang, Ramakrishna R Nemani, Auroop R Gangul. A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing. IEEE transactions on neural networks and learning systems. vol PP. 2022-05-05. PMID:35511836. |
recent successes of machine learning methods, especially deep learning (dl), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in earth systems. |
2022-05-05 |
2023-08-13 |
Not clear |
| Sheryl Parakkal, Riya Datta, Dibyendu Da. DeepBBBP: High accuracy Blood-Brain-Barrier Permeability Prediction with a Mixed Deep Learning Model. Molecular informatics. 2022-04-08. PMID:35393777. |
these succinct vector representations are utilized as inputs to the mixed dl model that is used for bbbp predictions. |
2022-04-08 |
2023-08-13 |
Not clear |
| Areej A Malibari, Fahd N Al-Wesabi, Marwa Obayya, Mimouna Abdullah Alkhonaini, Manar Ahmed Hamza, Abdelwahed Motwakel, Ishfaq Yaseen, Abu Sarwar Zaman. Arithmetic Optimization with RetinaNet Model for Motor Imagery Classification on Brain Computer Interface. Journal of healthcare engineering. vol 2022. 2022-04-04. PMID:35368960. |
though several approaches have been available in the literature for learning eeg signal feature, the deep learning (dl) models need to further explore for generating novel representation of eeg features and accomplish enhanced outcomes for mi classification. |
2022-04-04 |
2023-08-13 |
Not clear |
| Areej A Malibari, Fahd N Al-Wesabi, Marwa Obayya, Mimouna Abdullah Alkhonaini, Manar Ahmed Hamza, Abdelwahed Motwakel, Ishfaq Yaseen, Abu Sarwar Zaman. Arithmetic Optimization with RetinaNet Model for Motor Imagery Classification on Brain Computer Interface. Journal of healthcare engineering. vol 2022. 2022-04-04. PMID:35368960. |
the proposed aorndl-mic technique initially exploits multiscale principal component analysis (mspca) approach for the eeg signal denoising and continuous wavelet transform (cwt) is exploited for the transformation of 1d-eeg signal into 2d time-frequency amplitude representation, which enables to utilize the dl model via transfer learning approach. |
2022-04-04 |
2023-08-13 |
Not clear |
| Haoli Zhao, Zhenni Li, Wuhui Chen, Zibin Zheng, Shengli Xi. Accelerated Partially Shared Dictionary Learning With Differentiable Scale-Invariant Sparsity for Multi-View Clustering. IEEE transactions on neural networks and learning systems. vol PP. 2022-03-07. PMID:35254997. |
however, most existing multiview dl algorithms are facing problems in fully utilizing consistent and complementary information simultaneously in the multiview data and learning the most precise representation for multiview clustering because of gaps between views. |
2022-03-07 |
2023-08-13 |
Not clear |
| Hyun Kil Shi. Topological Distance-Based Electron Interaction Tensor to Apply a Convolutional Neural Network on Drug-like Compounds. ACS omega. vol 6. issue 51. 2022-01-05. PMID:34984306. |
given that the choice of molecular representation determines the architecture of the dl model to apply, a novel way of molecular representation can open a way to apply diverse dl networks developed and used in other fields. |
2022-01-05 |
2023-08-13 |
Not clear |
| Licheng Jiao, Dan Wang, Yidong Bai, Puhua Chen, Fang Li. Deep Learning in Visual Tracking: A Review. IEEE transactions on neural networks and learning systems. vol PP. 2021-12-30. PMID:34968181. |
from the beginning of the research on the automatic acquisition of high abstract feature representation, dl has gone deep into all aspects of tracking to date, to name a few, similarity metric, data association, and bounding box estimation. |
2021-12-30 |
2023-08-13 |
Not clear |
| Licheng Jiao, Dan Wang, Yidong Bai, Puhua Chen, Fang Li. Deep Learning in Visual Tracking: A Review. IEEE transactions on neural networks and learning systems. vol PP. 2021-12-30. PMID:34968181. |
in this article, we overview the critical improvements brought to the field by dl: deep feature representations, network architecture, and four crucial issues in visual tracking (spatiotemporal information integration, target-specific classification, target information update, and bounding box estimation). |
2021-12-30 |
2023-08-13 |
Not clear |
| Mario Flores, Zhentao Liu, Tinghe Zhang, Md Musaddaqui Hasib, Yu-Chiao Chiu, Zhenqing Ye, Karla Paniagua, Sumin Jo, Jianqiu Zhang, Shou-Jiang Gao, Yu-Fang Jin, Yidong Chen, Yufei Huan. Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis. Briefings in bioinformatics. 2021-12-20. PMID:34929734. |
specifically, we establish a unified mathematical representation of variational autoencoder, autoencoder, generative adversarial network and supervised dl models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. |
2021-12-20 |
2023-08-13 |
Not clear |