Peter J. Sadowski

发表

Deep Learning for Experimental Physics

Peter J. Sadowski, Peter Sadowski, 2016 .

Learning in the machine: Random backpropagation and the deep learning channel

Pierre Baldi, Peter J. Sadowski, Zhiqin Lu, 2016, Artif. Intell..

Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction

Pierre Baldi, Niranjan A. Subrahmanya, Peter J. Sadowski, 2016, J. Chem. Inf. Model..

The dropout learning algorithm

Pierre Baldi, Peter J. Sadowski, P. Baldi, 2014, Artif. Intell..

A theory of local learning, the learning channel, and the optimality of backpropagation

Pierre Baldi, Peter J. Sadowski, P. Baldi, 2015, Neural Networks.

Deep Learning, Dark Knowledge, and Dark Matter

Pierre Baldi, Daniel Whiteson, Peter J. Sadowski, 2014, HEPML@NIPS.

Parameterized Machine Learning for High-Energy Physics pdf

Pierre Baldi, Daniel Whiteson, Kyle Cranmer, 2016, ArXiv.

Efficient antihydrogen detection in antimatter physics by deep learning pdf

Pierre Baldi, Yasunori Yamazaki, Peter J. Sadowski, 2017, ArXiv.

Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge

Gyan Bhanot, Michael Biehl, Sahand Hormoz, 2015, Bioinform..

Learning in the machine: Recirculation is random backpropagation

Pierre Baldi, Peter J. Sadowski, P. Baldi, 2018, Neural Networks.

Learning Activation Functions to Improve Deep Neural Networks pdf

Pierre Baldi, Matthew D. Hoffman, Forest Agostinelli, 2014, ICLR.

Deep Learning in the Natural Sciences: Applications to Physics

Pierre Baldi, Peter J. Sadowski, P. Baldi, 2017, Braverman Readings in Machine Learning.

Understanding Dropout

Pierre Baldi, Peter J. Sadowski, P. Baldi, 2013, NIPS.

Learning in the machine: The symmetries of the deep learning channel

Pierre Baldi, Peter J. Sadowski, Zhiqin Lu, 2017, Neural Networks.

Learning in the Machine: Random Backpropagation and the Deep Learning Channel (Extended Abstract)

Pierre Baldi, Peter J. Sadowski, Zhiqin Lu, 2019, IJCAI.

Searching for Higgs Boson Decay Modes with Deep Learning

Pierre Baldi, Daniel Whiteson, Peter J. Sadowski, 2014, NIPS.

Learning in the Machine: Random Backpropagation and the Learning Channel pdf

Pierre Baldi, Peter J. Sadowski, Zhiqin Lu, 2016, ArXiv.

PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures

Surendra Byna, Prabhat, Pradeep Dubey, 2016, 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning pdf

Pierre Baldi, Daniel Whiteson, Peter J. Sadowski, 2014, ArXiv.

Small-Molecule 3D Structure Prediction Using Open Crystallography Data

Pierre Baldi, Peter J. Sadowski, P. Baldi, 2013, J. Chem. Inf. Model..

The Ebb and Flow of Deep Learning: a Theory of Local Learning pdf

Pierre Baldi, Peter J. Sadowski, P. Baldi, 2015, ArXiv.

Parameterized neural networks for high-energy physics

Pierre Baldi, Daniel Whiteson, Kyle Cranmer, 2016, The European Physical Journal C.

Deep autoencoder neural networks for gene ontology annotation predictions

Pierre Baldi, Davide Chicco, Peter J. Sadowski, 2014, BCB.

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks

Prabhat, Pierre Baldi, Sang-Yun Oh, 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).