Peter J. Sadowski

According to our database, Peter J. Sadowski authored at least 38 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Neural Surrogate HMC: Accelerated Hamiltonian Monte Carlo with a Neural Network Surrogate Likelihood.

Linnea M. Wolniewicz, Peter Sadowski, Claudio Corti

CoRR, 2024

BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI.

Arianna Bunnell, Kailee Hung, John A. Shepherd, Peter Sadowski

CoRR, 2024

WV-Net: A foundation model for SAR WV-mode satellite imagery trained using contrastive self-supervised learning on 10 million images.

Yannik Glaser, Justin E. Stopa, Linnea M. Wolniewicz, Ralph Foster, Douglas C. Vandemark, Alexis Mouche, Bertrand Chapron, Peter Sadowski

CoRR, 2024

Learning a Clinically-Relevant Concept Bottleneck for Lesion Detection in Breast Ultrasound.

Arianna Bunnell, Yannik Glaser, Dustin Valdez, Thomas K. Wolfgruber, Aleen Altamirano, Carol Zamora González, Brenda Y. Hernandez, Peter Sadowski, John A. Shepherd

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation.

Moseli Mots'oehli, Anton Nikolaev, Wawan B. IGede, John Lynham, Peter J. Mous, Peter Sadowski

Proceedings of the IEEE International Conference on Omni-layer Intelligent Systems, 2024

2023

Diffusion Models for High-Resolution Solar Forecasts.

Yusuke Hatanaka, Yannik Glaser, Geoff Galgon, Giuseppe Torri, Peter Sadowski

CoRR, 2023

2021

Deep Learning for Predicting Significant Wave Height From Synthetic Aperture Radar.

Brandon Quach, Yannik Glaser, Justin Edward Stopa, Alexis Aurelien Mouche, Peter J. Sadowski

IEEE Trans. Geosci. Remote. Sens., 2021

Tourbillon: a Physically Plausible Neural Architecture.

Mohammadamin Tavakoli, Peter J. Sadowski, Pierre Baldi

CoRR, 2021

Evolution-Informed Neural Networks for Microbiome Data Analysis.

Michael Ito, Yannik Glaser, Peter J. Sadowski

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021

2020

Sherpa: Robust hyperparameter optimization for machine learning.

Lars Hertel, Julian Collado, Peter J. Sadowski, Jordan Ott, Pierre Baldi

SoftwareX, 2020

Deep Sensing of Ocean Wave Heights with Synthetic Aperture Radar.

Brandon Quach, Yannik Glaser, Justin Stopa, Peter J. Sadowski

Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to, 2020

Deep Learning for Climate Models of the Atlantic Ocean.

Anton Nikolaev, Ingo Richter, Peter Sadowski

Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to, 2020

2019

Thermodynamic Computing.

Tom Conte, Erik DeBenedictis, Natesh Ganesh, Todd Hylton, John Paul Strachan, R. Stanley Williams, Alexander A. Alemi, Lee Altenberg, Gavin E. Crooks, James P. Crutchfield, Lídia del Rio, Josh Deutsch, Michael Robert DeWeese, Khari Douglas, Massimiliano Esposito, Michael P. Frank, Robert Fry, Peter Harsha, Mark D. Hill, Christopher T. Kello, Jeff Krichmar, Suhas Kumar, Shih-Chii Liu, Seth Lloyd, Matteo Marsili, Ilya Nemenman, Alex Nugent, Norman H. Packard, Dana Randall, Peter Sadowski, Narayana Santhanam, Robert Shaw, Adam Z. Stieg, Elan Stopnitzky, Christof Teuscher, Chris Watkins, David H. Wolpert, J. Joshua Yang, Yan Yufik

CoRR, 2019

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

Pierre Baldi, Peter J. Sadowski, Zhiqin Lu

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Learning in the machine: Recirculation is random backpropagation.

Pierre Baldi, Peter J. Sadowski

Neural Networks, 2018

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

Pierre Baldi, Peter J. Sadowski, Zhiqin Lu

Artif. Intell., 2018

2017

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

Pierre Baldi, Peter J. Sadowski, Zhiqin Lu

Neural Networks, 2017

Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning.

Peter J. Sadowski, Balint Radics, Ananya, Yasunori Yamazaki, Pierre Baldi

CoRR, 2017

Deep Learning in the Natural Sciences: Applications to Physics.

Peter J. Sadowski, Pierre Baldi

Proceedings of the Braverman Readings in Machine Learning. Key Ideas from Inception to Current State, 2017

2016

Deep Learning for Experimental Physics.

Peter J. Sadowski

PhD thesis, 2016

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

Pierre Baldi, Peter J. Sadowski

Neural Networks, 2016

Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction.

Peter J. Sadowski, David Fooshee, Niranjan Subrahmanya, Pierre Baldi

J. Chem. Inf. Model., 2016

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

Pierre Baldi, Peter J. Sadowski, Zhiqin Lu

CoRR, 2016

Parameterized Machine Learning for High-Energy Physics.

Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter J. Sadowski, Daniel Whiteson

CoRR, 2016

Theano: A Python framework for fast computation of mathematical expressions.

Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermüller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph P. Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

CoRR, 2016

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

Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan Sundaram, Jialin Liu, Peter J. Sadowski, Evan Racah, Surendra Byna, Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey

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.

Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat

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.

Pierre Baldi, Peter J. Sadowski

CoRR, 2015

Learning Activation Functions to Improve Deep Neural Networks.

Forest Agostinelli, Matthew D. Hoffman, Peter J. Sadowski, Pierre Baldi

Proceedings of the 3rd International Conference on Learning Representations, 2015

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

Michael Biehl, Peter J. Sadowski, Gyan Bhanot, Erhan Bilal, Adel Dayarian, Pablo Meyer, Raquel Norel, Kahn Rhrissorrakrai, Michael D. Zeller, Sahand Hormoz

Bioinform., 2015

2014

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

Pierre Baldi, Peter J. Sadowski, Daniel Whiteson

CoRR, 2014

The dropout learning algorithm.

Pierre Baldi, Peter J. Sadowski

Artif. Intell., 2014

Searching for Higgs Boson Decay Modes with Deep Learning.

Peter J. Sadowski, Daniel Whiteson, Pierre Baldi

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.

Peter J. Sadowski, Julian Collado, Daniel Whiteson, Pierre Baldi

Proceedings of the Workshop on High-energy Physics and Machine Learning, 2014

Deep autoencoder neural networks for gene ontology annotation predictions.

Davide Chicco, Peter J. Sadowski, Pierre Baldi

Proceedings of the 5th ACM Conference on Bioinformatics, 2014

2013

Small-Molecule 3D Structure Prediction Using Open Crystallography Data.

Peter J. Sadowski, Pierre Baldi

J. Chem. Inf. Model., 2013

Understanding Dropout.

Pierre Baldi, Peter J. Sadowski

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

2010

Bayesian and pairwise local similarity discriminant analysis.

Peter J. Sadowski, Luca Cazzanti, Maya R. Gupta

Proceedings of the 2nd International Workshop on Cognitive Information Processing, 2010