| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Kazi Ashraf Moinuddin, Felix Havugimana, Rakib Al-Fahad, Gavin M Bidelman, Mohammed Yeasi. Unraveling Spatial-Spectral Dynamics of Speech Categorization Speed Using Convolutional Neural Networks. Brain sciences. vol 13. issue 1. 2023-01-21. PMID:36672055. |
our framework includes (but is not limited to): (i) a data augmentation technique designed to reduce noise and control the overall variance of eeg dataset; (ii) bandpower topomaps to learn the spatial-spectral representation using cnn; (iii) large-scale bayesian hyper-parameter optimization to find best performing cnn model; (iv) anova and posthoc analysis on guided-gradcam activation values to measure the effect of neural regions and frequency bands on behavioral responses. |
2023-01-21 |
2023-08-14 |
Not clear |
| Hao Chen, Yan Zhao, Shigang Wan. Person Re-Identification Based on Contour Information Embedding. Sensors (Basel, Switzerland). vol 23. issue 2. 2023-01-21. PMID:36679571. |
in the study, we found that pedestrian contour feature is not enough in the representation of cnn. |
2023-01-21 |
2023-08-14 |
Not clear |
| Xingyu Tang, Peijie Zheng, Yuewu Liu, Yuhua Yao, Guohua Huan. LangMoDHS: A deep learning language model for predicting DNase I hypersensitive sites in mouse genome. Mathematical biosciences and engineering : MBE. vol 20. issue 1. 2023-01-18. PMID:36650801. |
the cnn and the bi-lstm were stacked in a parallel manner, which was helpful to accumulate multiple-view representations from primary dna sequences. |
2023-01-18 |
2023-08-14 |
mouse |
| Juhyeon Lee, Minyoung Jung, Niv Lustig, Jong-Hwan Le. Neural representations of the perception of handwritten digits and visual objects from a convolutional neural network compared to humans. Human brain mapping. 2023-01-13. PMID:36637109. |
we investigated neural representations for visual perception of 10 handwritten digits and six visual objects from a convolutional neural network (cnn) and humans using functional magnetic resonance imaging (fmri). |
2023-01-13 |
2023-08-14 |
human |
| Juhyeon Lee, Minyoung Jung, Niv Lustig, Jong-Hwan Le. Neural representations of the perception of handwritten digits and visual objects from a convolutional neural network compared to humans. Human brain mapping. 2023-01-13. PMID:36637109. |
once our cnn model was fine-tuned using a pre-trained vgg16 model to recognize the visual stimuli from the digit and object categories, representational similarity analysis (rsa) was conducted using neural activations from fmri and feature representations from the cnn model across all 16 classes. |
2023-01-13 |
2023-08-14 |
human |
| Juhyeon Lee, Minyoung Jung, Niv Lustig, Jong-Hwan Le. Neural representations of the perception of handwritten digits and visual objects from a convolutional neural network compared to humans. Human brain mapping. 2023-01-13. PMID:36637109. |
the encoded neural representation of the cnn model exhibited the hierarchical topography mapping of the human visual system. |
2023-01-13 |
2023-08-14 |
human |
| Juhyeon Lee, Minyoung Jung, Niv Lustig, Jong-Hwan Le. Neural representations of the perception of handwritten digits and visual objects from a convolutional neural network compared to humans. Human brain mapping. 2023-01-13. PMID:36637109. |
there was a surprising similarity between the neural representations from the cnn model and the neural representations for human visual perception in the context of the perception of digits versus objects, particularly in the primary visual and associated areas. |
2023-01-13 |
2023-08-14 |
human |
| Juhyeon Lee, Minyoung Jung, Niv Lustig, Jong-Hwan Le. Neural representations of the perception of handwritten digits and visual objects from a convolutional neural network compared to humans. Human brain mapping. 2023-01-13. PMID:36637109. |
unlike the cnn model, the neural representation of digits and objects for humans is more widely distributed across the whole brain, including the frontal and temporal areas. |
2023-01-13 |
2023-08-14 |
human |
| Di Tang, Weijie Jin, Dawei Liu, Jingqi Che, Yin Yan. Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking. Sensors (Basel, Switzerland). vol 23. issue 1. 2023-01-08. PMID:36617099. |
in addition, a lightweight siamese cnn with cross stage partial (csp) provided the representations of features learned from massive face images, allowing the target similarity in data association to be guaranteed. |
2023-01-08 |
2023-08-14 |
Not clear |
| Aditi Jha, Joshua C Peterson, Thomas L Griffith. Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks. Cognitive science. vol 47. issue 1. 2023-01-08. PMID:36617318. |
here, we attempt to estimate the number of dimensions in cnn representations that are required to capture human psychological representations in two ways: (1) directly, using human similarity judgments and (2) indirectly, in the context of categorization. |
2023-01-08 |
2023-08-14 |
human |
| Aditi Jha, Joshua C Peterson, Thomas L Griffith. Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks. Cognitive science. vol 47. issue 1. 2023-01-08. PMID:36617318. |
in both cases, we find that low-dimensional projections of cnn representations are sufficient to predict human behavior. |
2023-01-08 |
2023-08-14 |
human |
| Hailiang Zhang, Zhenbo Xu, Xiaqiong Fan, Yue Wang, Qiong Yang, Jinyu Sun, Ming Wen, Xiao Kang, Zhimin Zhang, Hongmei L. Fusion of Quality Evaluation Metrics and Convolutional Neural Network Representations for ROI Filtering in LC-MS. Analytical chemistry. 2023-01-04. PMID:36597722. |
in this study, a deep fused filter of rois (dffroi) was proposed to improve the accuracy of roi extraction by combining the handcrafted evaluation metrics with convolutional neural network (cnn)-learned representations. |
2023-01-04 |
2023-08-14 |
Not clear |
| Sihui Wang, Ailian Jiang, Xiaotian Li, Yanfang Qiu, Mengyang Li, Feixiang L. DPBET: A dual-path lung nodules segmentation model based on boundary enhancement and hybrid transformer. Computers in biology and medicine. vol 151. issue Pt B. 2022-11-30. PMID:36450216. |
it is combined with cnn to form a hybrid architecture to generate a global representation of the target lesion. |
2022-11-30 |
2023-08-14 |
Not clear |
| Anja Philippsen, Sho Tsuji, Yukie Naga. Quantifying developmental and individual differences in spontaneous drawing completion among children. Frontiers in psychology. vol 13. 2022-11-28. PMID:36438392. |
additionally, the drawings were quantified using feature representations extracted with a deep convolutional neural network (cnn), which allowed an analysis of the drawings at different perceptual levels (i.e., local or global). |
2022-11-28 |
2023-08-14 |
Not clear |
| Kokila Bharti Jaiswal, T Meenpa. Heart rate estimation network from facial videos using spatiotemporal feature image. Computers in biology and medicine. vol 151. issue Pt A. 2022-11-20. PMID:36403356. |
the proposed model projects a new motion representation to cnn derived using wavelets, which enables estimation of hr under heterogeneous lighting condition and continuous motion. |
2022-11-20 |
2023-08-14 |
Not clear |
| Hyeon-Ju Jeon, Hae Gyun Lim, K Kirk Shung, O-Joun Lee, Min Gon Ki. Automated cell-type classification combining dilated convolutional neural networks with label-free acoustic sensing. Scientific reports. vol 12. issue 1. 2022-11-18. PMID:36400803. |
subsequently, denoised backscattered signals were classified into specific cell types using convolutional neural network (cnn) models for three types of signal data representations, including 1d cnn models for waveform and frequency spectrum analysis and two-dimensional (2d) cnn models for spectrogram analysis. |
2022-11-18 |
2023-08-14 |
Not clear |
| Sarita Limbu, Sivanesan Dakshanamurth. Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method. Sensors (Basel, Switzerland). vol 22. issue 21. 2022-11-11. PMID:36365881. |
the hnn-cancer included a new smiles feature representation method by modifying our previous 3d array representation of 1d smiles simulated by the convolutional neural network (cnn). |
2022-11-11 |
2023-08-14 |
Not clear |
| Bomin Wei, Yue Zhang, Xiang Gon. DeepLPI: a novel deep learning-based model for protein-ligand interaction prediction for drug repurposing. Scientific reports. vol 12. issue 1. 2022-10-28. PMID:36307509. |
we first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two resnet-based 1d cnn modules to derive features, respectively. |
2022-10-28 |
2023-08-14 |
Not clear |
| Uzair Saeed, Ammar Armghan, Wang Quanyu, Fayadh Alenezi, Sun Yue, Prayag Tiwar. One-shot many-to-many facial reenactment using Bi-Layer Graph Convolutional Networks. Neural networks : the official journal of the International Neural Network Society. vol 156. 2022-10-24. PMID:36274526. |
using bi-layer with convolutional neural network (cnn), we named our model bi-layer graph convolutional layers (bgcln) which utilized to create the latent vector's optical flow representation. |
2022-10-24 |
2023-08-14 |
Not clear |
| Naifeng Wen, Guanqun Liu, Jie Zhang, Rubo Zhang, Yating Fu, Xu Ha. A fingerprints based molecular property prediction method using the BERT model. Journal of cheminformatics. vol 14. issue 1. 2022-10-22. PMID:36271394. |
then, the encoded molecular representation by the fp-bert is input to the convolutional neural network (cnn) to extract higher-level abstract features, and the predicted properties of the molecule are finally obtained through fully connected layer for distinct classification or regression mpp tasks. |
2022-10-22 |
2023-08-14 |
Not clear |