Peter J. Sadowski
According to our database^{1},
Peter J. Sadowski
authored at least 26 papers
between 2013 and 2019.
Collaborative distances:
Collaborative distances:
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at orcid.org
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Bibliography
2019
Learning in the Machine: Random Backpropagation and the Deep Learning Channel (Extended Abstract).
Proceedings of the TwentyEighth 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 KNearest Neighbor on Distributed Architectures.
CoRR, 2016
Learning in the Machine: Random Backpropagation and the Learning Channel.
CoRR, 2016
Parameterized Machine Learning for HighEnergy Physics.
CoRR, 2016
PANDA: Extreme Scale Parallel KNearest 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
Interspecies 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 Highenergy Physics and Machine Learning, 2014
Deep autoencoder neural networks for gene ontology annotation predictions.
Proceedings of the 5th ACM Conference on Bioinformatics, 2014
2013
SmallMolecule 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 58, 2013