Charles Siegel

According to our database1, Charles Siegel authored at least 18 papers between 2016 and 2020.

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Bibliography

2020
Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing.
Future Gener. Comput. Syst., 2020

2019
Recombination of Artificial Neural Networks.
CoRR, 2019

2018
ColdRoute: effective routing of cold questions in stack exchange sites.
Data Min. Knowl. Discov., 2018

Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction.
CoRR, 2018

GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent.
CoRR, 2018

How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision, 2018

Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Effective Machine Learning Based Format Selection and Performance Modeling for SpMV on GPUs.
Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium Workshops, 2018

Desh: deep learning for system health prediction of lead times to failure in HPC.
Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing, 2018

2017
ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction.
CoRR, 2017

SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties.
CoRR, 2017

User-transparent Distributed TensorFlow.
CoRR, 2017

Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models.
CoRR, 2017

Evaluating On-Node GPU Interconnects for Deep Learning Workloads.
Proceedings of the High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation, 2017

What does fault tolerant deep learning need from MPI?
Proceedings of the 24th European MPI Users' Group Meeting, 2017

A Learning Framework for Control-Oriented Modeling of Buildings.
Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, 2017

2016
Distributed TensorFlow with MPI.
CoRR, 2016

Adaptive neuron apoptosis for accelerating deep learning on large scale systems.
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016


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