PURR Registry

Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data

10.4231/R7N58J9Z

Baichuan Zhang , Bartlomiej P. Rajwa , Murat Dundar , Qiang Kou , Yicheng He

10/01/2015

A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity

bacterial colonies BARDOT Biomedical Engineering Computer Science deep learning Elastic light scattering Interdisciplinary Research K-Means Clustering Life Sciences Machine Learning representation learning

Raman spectra-based deep learning – A tool to identify microbial contamination

10.4231/HQGB-K827

Amir Hossein Raffiee , Arezoo M. Ardekani , Denilson Mendes De Oliveira , Mohit S. Verma , Murali K. Maruthamuthu

09/03/2020

Raman spectroscopic dataset for the potential microbes contaminating the pharmaceutical industry.

Agricultural and Biological Engineering deep learning microbial contamination Raman

Wheat spike blast image classification using deep convolutional neural networks

10.4231/P0Y7-3428

Carlos Gongora , Christian D Cruz , Darcy Telenko , Jian Jin , Mariela Fernandez-Campos , Mohammad Jahanshahi , Tao Wang , Yuting Huang

04/30/2021

The folder includes i) a wheat spike blast image classification CNN model trained to automatically quantify and classify disease severity, ii) the generated datasets that include images of wheat spike blast severity levels under controlled...

Blast Botany and Plant Pathology CNN deep learning disease Magnaporthe oryzae plant disease phenotyping Plant phenotyping Python Wheat wheat blast

Visualized layer-wise visual features in deep residual neural network

10.4231/R7PR7T1G

Haiguang Wen , Junxing Shi , Wei Chen , Zhongming Liu

06/29/2017

Deep residual neural network is a brain-inspired computational model. 50 layers of neuron-like computational units are stacked into a bottom-up hierarchy. Features encoded at units are visualized for intuitively understanding the internal...

Biomedical Engineering deep learning natural vision Neural encoding Object Recognition

Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 1

10.4231/R7X63K3M

Haiguang Wen , Jiayue Cao , Junxing Shi , Kun-Han Lu , Yizhen Zhang , Zhongming Liu

09/15/2017

This is a video-fMRI dataset for subject 1 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI),

Biomedical Engineering deep learning fMRI LIBI natural vision neural decoding Neural encoding Neuroscience

Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 2

10.4231/R7NS0S1F

Haiguang Wen , Jiayue Cao , Junxing Shi , Kun-Han Lu , Yizhen Zhang , Zhongming Liu

09/18/2017

This is a video-fMRI dataset for subject 2 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI).

Biomedical Engineering deep learning fMRI LIBI natural vision neural decoding Neural encoding Neuroscience

Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 3

10.4231/R7J101BV

Haiguang Wen , Jiayue Cao , Junxing Shi , Kun-Han Lu , Yizhen Zhang , Zhongming Liu

09/15/2017

This is a video-fMRI dataset for subject 3 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI).

Biomedical Engineering deep learning fMRI LIBI movie stimuli natural vision neural decoding Neural encoding Neuroscience

Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Stimuli

10.4231/R71Z42KK

Haiguang Wen , Jiayue Cao , Junxing Shi , Kun-Han Lu , Yizhen Zhang , Zhongming Liu

09/15/2017

This is a video-fMRI dataset contains the videos with stimuli acquired by the Laboratory of Integrated Brain Imaging (LIBI).

Biomedical Engineering deep learning fMRI LIBI movie stimuli natural vision neural decoding Neural encoding Neuroscience

Source code for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

10.4231/R7V98675

Haiguang Wen , Jiayue Cao , Junxing Shi , Kun-Han Lu , Yizhen Zhang , Zhongming Liu

09/25/2017

This document includes the main source code (Matlab or Python) related to our study.

Biomedical Engineering deep learning FMR1 LIBI Matlab natural vision neural decoding Neural encoding Neuroscience Python Code Source Code

Source code for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

10.4231/R7N58JJ3

Haiguang Wen , Jiayue Cao , Junxing Shi , Kun-Han Lu , Yizhen Zhang , Zhongming Liu

11/17/2017

This document includes the main source code (Matlab or Python) related to our study.

Biomedical Engineering deep learning FMR1 LIBI Matlab natural vision neural decoding Neural encoding Neuroscience Python Code Source Code

Datasets

Subject

Creator

Type

Date

Total is 14 Results

Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data

Raman spectra-based deep learning – A tool to identify microbial contamination

Wheat spike blast image classification using deep convolutional neural networks

Visualized layer-wise visual features in deep residual neural network

Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 1

Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 2

Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 3

Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Stimuli

Source code for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

Source code for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

Datasets

All fields DOI Author Subject Search

Subject

Creator

Type

Date

Total is 14 Results