Bayesian and pairwise local similarity discriminant analysis. - Peter J. Sadowski; Luca Cazzanti; Maya R. Gupta (CIP, 2010)

Understanding Dropout. - Pierre Baldi; Peter J. Sadowski (NIPS, 2013)

Small-Molecule 3D Structure Prediction Using Open Crystallography Data. - Peter J. Sadowski; Pierre Baldi (J. Chem. Inf. Model., 2013)

Deep autoencoder neural networks for gene ontology annotation predictions. - Davide Chicco; Peter J. Sadowski; Pierre Baldi (BCB, 2014)

Deep Learning, Dark Knowledge, and Dark Matter. - Peter J. Sadowski; Julian Collado; Daniel Whiteson; Pierre Baldi (HEPML@NIPS, 2014)

Searching for Higgs Boson Decay Modes with Deep Learning. - Peter J. Sadowski; Daniel Whiteson; Pierre Baldi (NIPS, 2014)

The dropout learning algorithm. - Pierre Baldi; Peter J. Sadowski (Artif. Intell., 2014)

Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning. - Pierre Baldi; Peter J. Sadowski; Daniel Whiteson (CoRR, 2014)

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 001; Raquel Norel; Kahn Rhrissorrakrai; Michael D. Zeller; Sahand Hormoz (Bioinform., 2015)

Learning Activation Functions to Improve Deep Neural Networks. - Forest Agostinelli; Matthew D. Hoffman; Peter J. Sadowski; Pierre Baldi (ICLR (Workshop), 2015)

The Ebb and Flow of Deep Learning: a Theory of Local Learning. - Pierre Baldi; Peter J. Sadowski (CoRR, 2015)

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 (ICMLA, 2016)

PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. - Md. Mostofa Ali Patwary; Nadathur Rajagopalan Satish; Narayanan Sundaram; Jialin Liu 002; Peter J. Sadowski; Evan Racah; Surendra Byna; Craig Tull; Wahid Bhimji; Prabhat; Pradeep Dubey (IPDPS, 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; et al. (CoRR, 2016)

Parameterized Machine Learning for High-Energy Physics. - Pierre Baldi; Kyle Cranmer; Taylor Faucett; Peter J. Sadowski; Daniel Whiteson (CoRR, 2016)

Learning in the Machine: Random Backpropagation and the Learning Channel. - Pierre Baldi; Peter J. Sadowski; Zhiqin Lu (CoRR, 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)

A theory of local learning, the learning channel, and the optimality of backpropagation. - Pierre Baldi; Peter J. Sadowski (Neural Networks, 2016)

Deep Learning for Experimental Physics. - Peter J. Sadowski (2016)

Deep Learning in the Natural Sciences: Applications to Physics. - Peter J. Sadowski; Pierre Baldi (Braverman Readings in Machine Learning, 2017)

Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning. - Peter J. Sadowski; Balint Radics; Ananya; Yasunori Yamazaki; Pierre Baldi (CoRR, 2017)

Learning in the machine: The symmetries of the deep learning channel. - Pierre Baldi; Peter J. Sadowski; Zhiqin Lu (Neural Networks, 2017)

Learning in the machine: Random backpropagation and the deep learning channel. - Pierre Baldi; Peter J. Sadowski; Zhiqin Lu (Artif. Intell., 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 (Extended Abstract). - Pierre Baldi; Peter J. Sadowski; Zhiqin Lu (IJCAI, 2019)

Thermodynamic Computing. - Tom Conte 001; 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; et al. (CoRR, 2019)

Deep Learning for Climate Models of the Atlantic Ocean. - Anton Nikolaev; Ingo Richter; Peter Sadowski (AAAI Spring Symposium: MLPS, 2020)

Deep Sensing of Ocean Wave Heights with Synthetic Aperture Radar. - Brandon Quach; Yannik Glaser; Justin Stopa; Peter J. Sadowski (AAAI Spring Symposium: MLPS, 2020)

Sherpa: Robust hyperparameter optimization for machine learning. - Lars Hertel; Julian Collado; Peter J. Sadowski; Jordan Ott; Pierre Baldi (SoftwareX, 2020)

Evolution-Informed Neural Networks for Microbiome Data Analysis. - Michael Ito; Yannik Glaser; Peter J. Sadowski (BIBM, 2021)

Tourbillon: a Physically Plausible Neural Architecture. - Mohammadamin Tavakoli; Peter J. Sadowski; Pierre Baldi (CoRR, 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)

Diffusion Models for High-Resolution Solar Forecasts. - Yusuke Hatanaka; Yannik Glaser; Geoff Galgon; Giuseppe Torri; Peter Sadowski (CoRR, 2023)

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 (COINS, 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 002 (MICCAI (3), 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)

BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI. - Arianna Bunnell; Kailee Hung; John A. Shepherd 002; Peter Sadowski (CoRR, 2024)

Neural Surrogate HMC: Accelerated Hamiltonian Monte Carlo with a Neural Network Surrogate Likelihood. - Linnea M. Wolniewicz; Peter Sadowski; Claudio Corti (CoRR, 2024)

Deep Learning Predicts Mammographic Breast Density in Clinical Breast Ultrasound Images. - Arianna Bunnell; Dustin Valdez; Thomas K. Wolfgruber; Brandon Quon; Kailee Hung; Brenda Y. Hernandez; Todd B. Seto; Jeffrey Killeen; Marshall Miyoshi; Peter Sadowski; John A. Shepherd 002 (CoRR, 2024)

Artificial Intelligence-Informed Handheld Breast Ultrasound for Screening: A Systematic Review of Diagnostic Test Accuracy. - Arianna Bunnell; Dustin Valdez; Fredrik Strand; Yannik Glaser; Peter Sadowski; John A. Shepherd 002 (CoRR, 2024)

Deep probabilistic direction prediction in 3D with applications to directional dark matter detectors. - Majd Ghrear; Peter Sadowski; Sven E. Vahsen (Mach. Learn. Sci. Technol., 2024)

Deep Learning Enables Large-Scale Shape and Appearance Modeling in Total-Body DXA Imaging. - Arianna Bunnell; Devon Cataldi; Yannik Glaser; Thomas K. Wolfgruber; Steven B. Heymsfield; Alan B. Zonderman; Thomas L. Kelly; Peter Sadowski; John A. Shepherd 002 (CoRR, 2025)