Pierre Baldi
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Articles by Pierre Baldi
Adapting vision-language models for neutrino event classification in high-energy physics
Abstract Recent advances in machine learning, particularly in multimodal models, have created new opportunities for analyzing complex data in high-energy physics, where accurate identification of particle interactions is critical for scientific discovery. However, existing approaches rely heavily on convolutional neural networks, which lack interpretability and do not fully leverage multimodal reasoning capabilities.
[2510.09659] Heterogeneous Point Set Transformers for Segmentation of Multiple View Particle Detectors
arXiv:2510.09659 (cs) View PDF HTML (experimental) Comments: Subjects: Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex) Report number: FERMILAB-CONF-25-0720-PPD Cite as: arXiv:2510.09659 [cs.LG] (or arXiv:2510.09659v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2510.09659 Submission history From: Alejandro Yankelevich [ view email] [v1] Tue, 7 Oct 2025 16:00:57 UTC (3,864 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected...
Evaluating the Performance of Large Language Models for Spanish Language in Undergraduate Admissions Exams
Ayuda Buscar en la ayuda Buscar en la ayuda Evaluating the Performance of Large Language Models for Spanish Language in Undergraduate Admissions Exams Autores: Sabino Miranda, Obdulia Pichardo Lagunas, Bella Martínez-Seis, Pierre Baldi Localización: Computación y Sistemas (CyS), ISSN 1405-5546, ISSN-e 2007-9737, Vol. 27, Nº. 4, 2023, págs.
[2505.02937] Fast and Precise Track Fitting with Machine Learning
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[2504.17073] Sparse Phased Array Optimization Using Deep Learning
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Maternal circadian rhythms during pregnancy dictate metabolic plasticity in offspring
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Keywords circadian rhythm metabolism circadian clock pregnancy developmental origins of health and disease obesity leptin time-restricted feeding caloric restriction eating behavior Introduction The circadian rhythm is a nearly 24-h period of self-sustainable variation under constant conditions, which helps maintain homeostasis by predicting changes in the environmental light-dark (LD) cycle.
By Na Yao, Kenichiro Kinouchi, Manami Katoh, Kousha Changizi Ashtiani, Sherif Abdelkarim, Hiroyuki Morimoto, Takuto Torimitsu, Takahide Kozuma, Akihide Iwahara, Shotaro Kosugi, Jin Komuro, Kyosuke Kato, Shun Tonomura, Toshifumi Nakamura, Arata Itoh, Shintaro Yamaguchi, Jun Yoshino, Junichiro Irie, Hisayuki Hashimoto, Shinsuke Yuasa, Akiko Satoh, Yohei Mikami, Shusaku Uchida, Takatoshi Ueki, Seitaro Nomura, Pierre Baldi, Kaori Hayashi, Hiroshi Itoh
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Cell Metabolism
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Chemically Informed Deep Learning for Interpretable Radical Reaction Prediction
Journal of Chemical Information and Modeling Cite this: J. Chem. Inf. Model. 2025, XXXX, XXX Click to copy citationCitation copied! . This publication is licensed under CC-BY-NC-ND 4.0 . You are free to share (copy and redistribute) this article in any medium or format within the parameters below: Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted.
Chemically Informed Deep Learning for Interpretable Radical Reaction Prediction
Introduction Click to copy section linkSection link copied! Attaining chemist-level performance in predicting the outcomes of chemical reactions remains an unresolved challenge in computational chemistry. A solution to this challenge would have applications in many areas of chemistry both in the laboratory, for instance in synthesis, retrosynthesis, and drug design; and in nature, for instance: in biology and atmospheric chemistry.
Specific exercise patterns generate an epigenetic molecular memory window that drives long-term memory formation and identifies ACVR1C as a bidirectional regulator of memory in mice | Nature Communications
Abstract Exercise has beneficial effects on cognition throughout the lifespan. Here, we demonstrate that specific exercise patterns transform insufficient, subthreshold training into long-term memory in mice. Our findings reveal a potential molecular memory window such that subthreshold training within this window enables long-term memory formation.
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
arXiv:2307.11922 (cs) Download a PDF of the paper titled Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors, by Kolby Nottingham and 6 other authors Download PDF Submission history From: Kolby Nottingham [ view email] [v1] Fri, 21 Jul 2023 22:02:50 UTC (10,071 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart...
Clinical Knowledge and Reasoning Abilities of AI Large Language Models in Pharmacy: A Comparative Study on the NAPLEX Exam
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[2305.10399] End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
arXiv:2305.10399 (hep-ex) Download a PDF of the paper titled End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics, by Alexander Shmakov and 5 other authors Download PDF Submission history From: Michael Fenton [ view email] [v1] Wed, 17 May 2023 17:43:10 UTC (5,040 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code,...
State of AI
Dear reader, Welcome to the inaugural edition of our AI Research Insights newsletter! We're thrilled to have you on board as we embark on this exciting journey together. Our mission is to keep you updated on the latest and most groundbreaking research in the world of artificial intelligence and machine learning, making it accessible, engaging, and insightful.
[2303.17491] Language Models can Solve Computer Tasks
arXiv Forum: How do we make accessible research papers a reality? If research isn't accessible, can we really call it "Open" Science? In response to the high interest in this event we have expanded our online hosting capacity and re-opened registration. Grab your seat.
Deep learning models of the discrete component of the Galactic interstellar $\ensuremath{\gamma}$-ray emission
Abstract A significant pointlike component from the small-scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data, but modeling this emission relies on observations of rare gas tracers only available in limited regions of the sky. Identifying this contribution is important to discriminate γ-ray point sources from interstellar gas, and to better characterize extended γ-ray sources.
A Fortran-Keras Deep Learning Bridge for Scientific Computing
Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available.
Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set
Abstract During the perioperative period patients often suffer complications, including acute kidney injury (AKI), reintubation, and mortality. In order to effectively prevent these complications, high-risk patients must be readily identified. However, most current risk scores are designed to predict a single postoperative complication and often lack specificity on the patient level.
Solving the Rubik’s cube with deep reinforcement learning and search
Abstract The Rubik’s cube is a prototypical combinatorial puzzle that has a large state space with a single goal state. The goal state is unlikely to be accessed using sequences of randomly generated moves, posing unique challenges for machine learning. We solve the Rubik’s cube with DeepCubeA, a deep reinforcement learning approach that learns how to solve increasingly difficult states in reverse from the goal state without any specific domain knowledge.
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