no code implementations • 16 Mar 2024 • Moseli Mots'oehli, Anton Nikolaev, Wawan B. IGede, John Lynham, Peter J. Mous, Peter Sadowski
These models are trained on a dataset of 50, 000 hand-annotated images containing 163 different fish species, ranging in length from 10cm to 250cm.
no code implementations • 1 Feb 2023 • Yusuke Hatanaka, Yannik Glaser, Geoff Galgon, Giuseppe Torri, Peter Sadowski
Forecasting future weather and climate is inherently difficult.
1 code implementation • 14 Jun 2022 • Lambert T. Leong, Michael C. Wong, Yannik Glaser, Thomas Wolfgruber, Steven B. Heymsfield, Peter Sadowski, John A. Shepherd
Differences between image types are primarily due to the imaging modality and medical images utilize a wide range of physics based techniques while natural images are captured using only visible light.
no code implementations • 13 Jul 2021 • Mohammadamin Tavakoli, Peter Sadowski, Pierre Baldi
The circular autoencoders are trained in self-supervised mode by recirculation algorithms and the top layer in supervised mode by stochastic gradient descent, with the option of propagating error information through the entire stack using non-symmetric connections.
1 code implementation • 8 May 2020 • Lars Hertel, Julian Collado, Peter Sadowski, Jordan Ott, Pierre Baldi
Sherpa is a hyperparameter optimization library for machine learning models.
no code implementations • ICLR 2019 • Peter Sadowski, Pierre Baldi
We show that each target can be modeled as a sample from a Dirichlet distribution, where the parameters of the Dirichlet are provided by the output of a neural network, and that the combined model can be trained using the gradient of the data likelihood.
no code implementations • 22 Dec 2017 • Pierre Baldi, Peter Sadowski, Zhiqin Lu
Specifically, random backpropagation and its variations can be performed with the same non-linear neurons used in the main input-output forward channel, and the connections in the learning channel can be adapted using the same algorithm used in the forward channel, removing the need for any specialized hardware in the learning channel.
1 code implementation • 6 Jun 2017 • Peter Sadowski, Balint Radics, Ananya, Yasunori Yamazaki, Pierre Baldi
Antihydrogen is at the forefront of antimatter research at the CERN Antiproton Decelerator.
no code implementations • 10 Mar 2017 • Chase Shimmin, Peter Sadowski, Pierre Baldi, Edison Weik, Daniel Whiteson, Edward Goul, Andreas Søgaard
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass.
no code implementations • 8 Dec 2016 • Pierre Baldi, Peter Sadowski, Zhiqin Lu
It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system.
1 code implementation • 9 May 2016 • The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, 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 Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, 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 Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher 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 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
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
no code implementations • 28 Jan 2016 • Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists.
2 code implementations • 28 Jan 2016 • Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, Daniel Whiteson
We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters.
BIG-bench Machine Learning Vocal Bursts Intensity Prediction
no code implementations • 22 Jun 2015 • Pierre Baldi, Peter Sadowski
The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms.
3 code implementations • 21 Dec 2014 • Forest Agostinelli, Matthew Hoffman, Peter Sadowski, Pierre Baldi
Artificial neural networks typically have a fixed, non-linear activation function at each neuron.
Ranked #162 on Image Classification on CIFAR-10
no code implementations • 13 Oct 2014 • Pierre Baldi, Peter Sadowski, Daniel Whiteson
The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5$\sigma$ significance barrier without more data.
2 code implementations • 19 Feb 2014 • Pierre Baldi, Peter Sadowski, Daniel Whiteson
Standard approaches have relied on `shallow' machine learning models that have a limited capacity to learn complex non-linear functions of the inputs, and rely on a pain-staking search through manually constructed non-linear features.
High Energy Physics - Phenomenology High Energy Physics - Experiment