Peter J. Sadowski

According to our database1, Peter J. Sadowski authored at least 26 papers between 2013 and 2019.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
Other 

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Bibliography

2019
Learning in the Machine: Random Backpropagation and the Deep Learning Channel (Extended Abstract).
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018
Learning in the machine: Recirculation is random backpropagation.
Neural Networks, 2018

Learning in the machine: Random backpropagation and the deep learning channel.
Artif. Intell., 2018

2017
Learning in the machine: The symmetries of the deep learning channel.
Neural Networks, 2017

Learning in the Machine: the Symmetries of the Deep Learning Channel.
CoRR, 2017

Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning.
CoRR, 2017

Deep Learning in the Natural Sciences: Applications to Physics.
Proceedings of the Braverman Readings in Machine Learning. Key Ideas from Inception to Current State, 2017

2016
Deep Learning for Experimental Physics.
PhD thesis, 2016

A theory of local learning, the learning channel, and the optimality of backpropagation.
Neural Networks, 2016

Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction.
Journal of Chemical Information and Modeling, 2016

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks.
CoRR, 2016

PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.
CoRR, 2016

Learning in the Machine: Random Backpropagation and the Learning Channel.
CoRR, 2016

Parameterized Machine Learning for High-Energy Physics.
CoRR, 2016

PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.
Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium, 2016

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks.
Proceedings of the 15th IEEE International Conference on Machine Learning and Applications, 2016

2015
The Ebb and Flow of Deep Learning: a Theory of Local Learning.
CoRR, 2015

Learning Activation Functions to Improve Deep Neural Networks.
Proceedings of the 3rd International Conference on Learning Representations, 2015

Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge.
Bioinformatics, 2015

2014
Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning.
CoRR, 2014

The dropout learning algorithm.
Artif. Intell., 2014

Searching for Higgs Boson Decay Modes with Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Deep Learning, Dark Knowledge, and Dark Matter.
Proceedings of the Workshop on High-energy Physics and Machine Learning, 2014

Deep autoencoder neural networks for gene ontology annotation predictions.
Proceedings of the 5th ACM Conference on Bioinformatics, 2014

2013
Small-Molecule 3D Structure Prediction Using Open Crystallography Data.
Journal of Chemical Information and Modeling, 2013

Understanding Dropout.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013


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