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ORCIDPeter J. Sadowski
Person information
- affiliation: University of Hawai'i at Mānoa, USA
- affiliation (former): University of California, Irvine, Department of Computer Science
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2020 – today
- 2024
[c15]Moseli Mots'oehli, Anton Nikolaev, Wawan B. IGede, John Lynham, Peter J. Mous, Peter Sadowski:
FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation. COINS 2024: 1-7- [c14]Arianna Bunnell, Yannik Glaser, Dustin Valdez
, Thomas K. Wolfgruber, Aleen Altamirano, Carol Zamora González, Brenda Y. Hernandez, Peter Sadowski, John A. Shepherd:
Learning a Clinically-Relevant Concept Bottleneck for Lesion Detection in Breast Ultrasound. MICCAI (3) 2024: 650-659 - [i18]Moseli Mots'oehli, Anton Nikolaev, Wawan B. IGede, John Lynham, Peter J. Mous, Peter Sadowski:
FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation. CoRR abs/2403.10916 (2024) - [i17]Yannik Glaser, Justin E. Stopa, Linnea M. Wolniewicz, Ralph Foster, Douglas C. Vandemark, Alexis Mouche, Bertrand Chapron, Peter Sadowski:
WV-Net: A foundation model for SAR WV-mode satellite imagery trained using contrastive self-supervised learning on 10 million images. CoRR abs/2406.18765 (2024) - [i16]Arianna Bunnell, Yannik Glaser, Dustin Valdez, Thomas K. Wolfgruber, Aleen Altamirano, Carol Zamora González, Brenda Y. Hernandez, Peter Sadowski, John A. Shepherd:
Learning a Clinically-Relevant Concept Bottleneck for Lesion Detection in Breast Ultrasound. CoRR abs/2407.00267 (2024) - [i15]Arianna Bunnell, Kailee Hung, John A. Shepherd, Peter Sadowski:
BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI. CoRR abs/2407.11316 (2024) - [i14]Linnea M. Wolniewicz, Peter Sadowski, Claudio Corti:
Neural Surrogate HMC: Accelerated Hamiltonian Monte Carlo with a Neural Network Surrogate Likelihood. CoRR abs/2407.20432 (2024) - 2023
- [i13]Yusuke Hatanaka, Yannik Glaser, Geoff Galgon, Giuseppe Torri, Peter Sadowski:
Diffusion Models for High-Resolution Solar Forecasts. CoRR abs/2302.00170 (2023) - 2021
- [j10]Brandon Quach, Yannik Glaser, Justin Edward Stopa, Alexis Aurelien Mouche, Peter J. Sadowski:
Deep Learning for Predicting Significant Wave Height From Synthetic Aperture Radar. IEEE Trans. Geosci. Remote. Sens. 59(3): 1859-1867 (2021) - [c13]Michael Ito, Yannik Glaser, Peter J. Sadowski:
Evolution-Informed Neural Networks for Microbiome Data Analysis. BIBM 2021: 3386-3391 - [i12]Mohammadamin Tavakoli, Peter J. Sadowski, Pierre Baldi:
Tourbillon: a Physically Plausible Neural Architecture. CoRR abs/2107.06424 (2021) - 2020
- [j9]Lars Hertel, Julian Collado, Peter J. Sadowski, Jordan Ott, Pierre Baldi:
Sherpa: Robust hyperparameter optimization for machine learning. SoftwareX 12: 100591 (2020) - [c12]Anton Nikolaev, Ingo Richter, Peter Sadowski:
Deep Learning for Climate Models of the Atlantic Ocean. AAAI Spring Symposium: MLPS 2020 - [c11]Brandon Quach, Yannik Glaser, Justin Stopa, Peter J. Sadowski:
Deep Sensing of Ocean Wave Heights with Synthetic Aperture Radar. AAAI Spring Symposium: MLPS 2020 - [i11]Lars Hertel, Julian Collado, Peter J. Sadowski, Jordan Ott, Pierre Baldi:
Sherpa: Robust Hyperparameter Optimization for Machine Learning. CoRR abs/2005.04048 (2020)
2010 – 2019
- 2019
- [c10]Pierre Baldi, Peter J. Sadowski, Zhiqin Lu:
Learning in the Machine: Random Backpropagation and the Deep Learning Channel (Extended Abstract). IJCAI 2019: 6348-6352 - [i10]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:
Thermodynamic Computing. CoRR abs/1911.01968 (2019) - 2018
- [j8]Pierre Baldi, Peter J. Sadowski, Zhiqin Lu:
Learning in the machine: Random backpropagation and the deep learning channel. Artif. Intell. 260: 1-35 (2018) - [j7]Pierre Baldi, Peter J. Sadowski:
Learning in the machine: Recirculation is random backpropagation. Neural Networks 108: 479-494 (2018) - 2017
- [j6]Pierre Baldi, Peter J. Sadowski, Zhiqin Lu:
Learning in the machine: The symmetries of the deep learning channel. Neural Networks 95: 110-133 (2017) - [c9]Peter J. Sadowski, Pierre Baldi:
Deep Learning in the Natural Sciences: Applications to Physics. Braverman Readings in Machine Learning 2017: 269-297 - [i9]Peter J. Sadowski, Balint Radics, Ananya, Yasunori Yamazaki, Pierre Baldi:
Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning. CoRR abs/1706.01826 (2017) - [i8]Pierre Baldi, Peter J. Sadowski, Zhiqin Lu:
Learning in the Machine: the Symmetries of the Deep Learning Channel. CoRR abs/1712.08608 (2017) - 2016
- [b1]Peter J. Sadowski:
Deep Learning for Experimental Physics. University of California, Irvine, USA, 2016 - [j5]Peter J. Sadowski, David Fooshee, Niranjan Subrahmanya, Pierre Baldi:
Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction. J. Chem. Inf. Model. 56(11): 2125-2128 (2016) - [j4]Pierre Baldi, Peter J. Sadowski:
A theory of local learning, the learning channel, and the optimality of backpropagation. Neural Networks 83: 51-74 (2016) - [c8]Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat:
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. ICMLA 2016: 892-897 - [c7]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:
PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. IPDPS 2016: 494-503 - [i7]Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat:
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks. CoRR abs/1601.07621 (2016) - [i6]Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter J. Sadowski, Daniel Whiteson:
Parameterized Machine Learning for High-Energy Physics. CoRR abs/1601.07913 (2016) - [i5]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:
Theano: A Python framework for fast computation of mathematical expressions. CoRR abs/1605.02688 (2016) - [i4]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:
PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. CoRR abs/1607.08220 (2016) - [i3]Pierre Baldi, Peter J. Sadowski, Zhiqin Lu:
Learning in the Machine: Random Backpropagation and the Learning Channel. CoRR abs/1612.02734 (2016) - 2015
- [j3]Michael Biehl, Peter J. Sadowski, Gyan Bhanot, Erhan Bilal, Adel Dayarian, Pablo Meyer, Raquel Norel, Kahn Rhrissorrakrai, Michael D. Zeller, Sahand Hormoz:
Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge. Bioinform. 31(4): 453-461 (2015) - [c6]Forest Agostinelli, Matthew D. Hoffman, Peter J. Sadowski, Pierre Baldi:
Learning Activation Functions to Improve Deep Neural Networks. ICLR (Workshop) 2015 - [i2]Pierre Baldi, Peter J. Sadowski:
The Ebb and Flow of Deep Learning: a Theory of Local Learning. CoRR abs/1506.06472 (2015) - 2014
- [j2]Pierre Baldi, Peter J. Sadowski:
The dropout learning algorithm. Artif. Intell. 210: 78-122 (2014) - [c5]Davide Chicco, Peter J. Sadowski, Pierre Baldi:
Deep autoencoder neural networks for gene ontology annotation predictions. BCB 2014: 533-540 - [c4]Peter J. Sadowski, Julian Collado, Daniel Whiteson, Pierre Baldi:
Deep Learning, Dark Knowledge, and Dark Matter. HEPML@NIPS 2014: 81-87 - [c3]Peter J. Sadowski, Daniel Whiteson, Pierre Baldi:
Searching for Higgs Boson Decay Modes with Deep Learning. NIPS 2014: 2393-2401 - [i1]Pierre Baldi, Peter J. Sadowski, Daniel Whiteson:
Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning. CoRR abs/1410.3469 (2014) - 2013
- [j1]Peter J. Sadowski, Pierre Baldi:
Small-Molecule 3D Structure Prediction Using Open Crystallography Data. J. Chem. Inf. Model. 53(12): 3127-3130 (2013) - [c2]Pierre Baldi, Peter J. Sadowski:
Understanding Dropout. NIPS 2013: 2814-2822 - 2010
- [c1]Peter J. Sadowski, Luca Cazzanti, Maya R. Gupta:
Bayesian and pairwise local similarity discriminant analysis. CIP 2010: 287-292
Coauthor Index
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last updated on 2024-10-23 21:24 CEST by the dblp team
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