End-to-end Learning Approach for Autonomous Driving: A Convolutional Neural Network Model

AbstractCytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Traditional approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large CyTOF studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model and identified a CD27-CD94+ CD8+ T cell population significantly associated with latent CMV infection. Finally, we provide a tutorial for creating, training and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (github.com/hzc363/DeepLearningCyTOF).

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End-to-end autonomous driving based on the convolution neural network model

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) ◽

10.1109/apsipaasc47483.2019.9023105 ◽

2019 ◽

Author(s):

Yuanfang Zhao ◽

Yunli Chen

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Autonomous Driving ◽

Convolution Neural Network ◽

End To End

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Using Transfer Learning Approach to Implement Convolutional Neural Network model to Recommend Airline Tickets by Using Online Reviews

2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA ◽

10.1109/smap49528.2020.9248443 ◽

2020 ◽

Author(s):

Maryam Heidari ◽

Setareh Rafatirad

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Network Model ◽

Neural Network Model ◽

Online Reviews ◽

Learning Approach ◽

Airline Tickets

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A robust and interpretable end-to-end deep learning model for cytometry data

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2003026117 ◽

2020 ◽

Vol 117 (35) ◽

pp. 21373-21380

Author(s):

Zicheng Hu ◽

Alice Tang ◽

Jaiveer Singh ◽

Sanchita Bhattacharya ◽

Atul J. Butte

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Learning Model ◽

Deep Convolutional Neural Network ◽

Significant Information ◽

End To End ◽

Deep Learning Model

Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large cytometry by time-of-flight mass spectrometry or mass cytometry (CyTOF) studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model. We were able to identify a CD27- CD94+ CD8+ T cell population significantly associated with latent CMV infection, confirming the findings in previous studies. Finally, we provide a tutorial for creating, training, and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (https://github.com/hzc363/DeepLearningCyTOF).

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A graph-convolutional neural network model for the prediction of chemical reactivity

Chemical Science ◽

10.1039/c8sc04228d ◽

2019 ◽

Vol 10 (2) ◽

pp. 370-377 ◽

Cited By ~ 110

Author(s):

Connor W. Coley ◽

Wengong Jin ◽

Luke Rogers ◽

Timothy F. Jamison ◽

Tommi S. Jaakkola ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Supervised Learning ◽

Network Model ◽

Neural Network Model ◽

Chemical Reactivity ◽

Organic Reactions ◽

Learning Approach

We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).

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KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

Jurnal Ilmiah Informatika Komputer ◽

10.35760/ik.2019.v24i3.2364 ◽

2019 ◽

Vol 24 (3) ◽

pp. 220-228

Author(s):

Gusti Alfahmi Anwar ◽

Desti Riminarsih

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Input Layer ◽

Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

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