Search Results for author: Yi-fan Chen

Found 13 papers, 3 papers with code

Policy Learning and Evaluation with Randomized Quasi-Monte Carlo

no code implementations16 Feb 2022 Sebastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen, Yi-fan Chen, Fei Sha

Reinforcement learning constantly deals with hard integrals, for example when computing expectations in policy evaluation and policy iteration.

Continuous Control Policy Gradient Methods +1

Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data

no code implementations4 Dec 2021 Fantine Huot, R. Lily Hu, Nita Goyal, Tharun Sankar, Matthias Ihme, Yi-fan Chen

To demonstrate the usefulness of this data set, we implement a neural network that takes advantage of the spatial information of this data to predict wildfire spread.

HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks

no code implementations NeurIPS Workshop DLDE 2021 Filipe de Avila Belbute-Peres, Yi-fan Chen, Fei Sha

Many types of physics-informed neural network models have been proposed in recent years as approaches for learning solutions to differential equations.

End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data

no code implementations19 Apr 2021 Michael Andrews, Bjorn Burkle, Yi-fan Chen, Davide DiCroce, Sergei Gleyzer, Ulrich Heintz, Meenakshi Narain, Manfred Paulini, Nikolas Pervan, Yusef Shafi, Wei Sun, Emanuele Usai, Kun Yang

We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon.

Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data

no code implementations15 Oct 2020 Fantine Huot, R. Lily Hu, Matthias Ihme, Qing Wang, John Burge, Tianjian Lu, Jason Hickey, Yi-fan Chen, John Anderson

Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness.

Towards Question-based Recommender Systems

1 code implementation28 May 2020 Jie Zou, Yi-fan Chen, Evangelos Kanoulas

Previous conversational recommender systems ask users to express their preferences over items or item facets.

Recommendation Systems

Simulation Pipeline for Traffic Evacuation in Urban Areas and Emergency Traffic Management Policy Improvements through Case Studies

1 code implementation14 Feb 2020 Yu Chen, S. Yusef Shafi, Yi-fan Chen

Traffic evacuation plays a critical role in saving lives in devastating disasters such as hurricanes, wildfires, floods, earthquakes, etc.

Prostate Segmentation using 2D Bridged U-net

no code implementations12 Jul 2018 Wanli Chen, Yue Zhang, Junjun He, Yu Qiao, Yi-fan Chen, Hongjian Shi, Xiaoying Tang

To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size.

Medical Image Segmentation Semantic Segmentation

Machine Learning in High Energy Physics Community White Paper

no code implementations8 Jul 2018 Kim Albertsson, Piero Altoe, Dustin Anderson, John Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Bjorn Burkle, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Yi-fan Chen, Taylor Childers, Yann Coadou, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Andrea De Simone, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ulrich Heintz, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Sydney Otten, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Wei Sun, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Justin Vasel, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Kun Yang, Omar Zapata

In this document we discuss promising future research and development areas for machine learning in particle physics.

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