Search Results for author: Ricardo Vilalta

Found 9 papers, 1 papers with code

Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators

no code implementations22 Nov 2023 Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta

To avoid these faults, we apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability of particle accelerators.

Anomaly Detection

Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter

no code implementations24 Mar 2023 zhenyu Dai, Ben Moews, Ricardo Vilalta, Romeel Dave

Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge.

Applications and Techniques for Fast Machine Learning in Science

no code implementations25 Oct 2021 Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bahr, Jurgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomas E. Muller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan Rankin, Simon Rothman, ASHISH SHARMA, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.

BIG-bench Machine Learning

Learning Abstract Task Representations

no code implementations19 Jan 2021 Mikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani, Ricardo Vilalta

A proper form of data characterization can guide the process of learning-algorithm selection and model-performance estimation.


Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients

1 code implementation12 Oct 2020 Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan, Rafael S. de Souza, Alexander Ihler, Kara Ponder, Ricardo Vilalta, Anais Moller, David O. Jones, Mi Dai, Alberto Krone-Martins, Bruno Quint, Sreevarsha Sreejith, Alex I. Malz, Lluis Galbany

The Recommendation System for Spectroscopic follow-up (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment.

Active Learning Astronomy +1

Transfer Learning in Astronomy: A New Machine-Learning Paradigm

no code implementations20 Dec 2018 Ricardo Vilalta

In contrast, a new generation of techniques is emerging where predictive models can take advantage of previous experience to leverage information from similar tasks.

Astronomy BIG-bench Machine Learning +1

A General Approach to Domain Adaptation with Applications in Astronomy

no code implementations20 Dec 2018 Ricardo Vilalta, Kinjal Dhar Gupta, Dainis Boumber, Mikhail M. Meskhi

The ability to build a model on a source task and subsequently adapt such model on a new target task is a pervasive need in many astronomical applications.

Active Learning Astronomy +2

Conceptual Domain Adaptation Using Deep Learning

no code implementations16 Aug 2018 Behrang Mehrparvar, Ricardo Vilalta

We introduce a search framework to correctly align high-level representations when training deep networks; such framework leads to the notion of conceptual --as opposed to representational-- domain adaptation.

Domain Adaptation

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