Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Cranmer Miles"'
Autor:
Tsoi Ho Fung, Pol Adrian Alan, Loncar Vladimir, Govorkova Ekaterina, Cranmer Miles, Dasu Sridhara, Elmer Peter, Harris Philip, Ojalvo Isobel, Pierini Maurizio
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09036 (2024)
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints. In this cont
Externí odkaz:
https://doaj.org/article/817d76a9edcc44f6a31f1c9c7616cb98
We present a novel method for symbolic regression (SR), the task of searching for compact programmatic hypotheses that best explain a dataset. The problem is commonly solved using genetic algorithms; we show that we can enhance such methods by induci
Externí odkaz:
http://arxiv.org/abs/2409.09359
Autor:
Kane, Sarah G., Belokurov, Vasily, Cranmer, Miles, Monty, Stephanie, Zhang, Hanyuan, Ardern-Arentsen, Anke, Kane, Elana
Globular clusters (GCs) are sites of extremely efficient star formation, and recent studies suggest they significantly contributed to the early Milky Way's stellar mass build-up. Although their role has since diminished, GCs' impact on the Galaxy's i
Externí odkaz:
http://arxiv.org/abs/2409.00197
Autor:
Watson, Joe, Song, Chen, Weeger, Oliver, Gruner, Theo, Le, An T., Hansel, Kay, Hendawy, Ahmed, Arenz, Oleg, Trojak, Will, Cranmer, Miles, D'Eramo, Carlo, Bülow, Fabian, Goyal, Tanmay, Peters, Jan, Hoffman, Martin W.
This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to the
Externí odkaz:
http://arxiv.org/abs/2408.09840
Constraining planet formation models based on the observed exoplanet population requires generating large samples of synthetic planetary systems, which can be computationally prohibitive. A significant bottleneck is simulating the giant impact phase,
Externí odkaz:
http://arxiv.org/abs/2408.08873
Autor:
Golkar, Siavash, Bietti, Alberto, Pettee, Mariel, Eickenberg, Michael, Cranmer, Miles, Hirashima, Keiya, Krawezik, Geraud, Lourie, Nicholas, McCabe, Michael, Morel, Rudy, Ohana, Ruben, Parker, Liam Holden, Blancard, Bruno Régaldo-Saint, Cho, Kyunghyun, Ho, Shirley
Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications. This paper introduces the contextual counting task, a novel toy problem aimed at enh
Externí odkaz:
http://arxiv.org/abs/2406.02585
The standard cosmological model, with its six independent parameters, successfully describes our observable Universe. One of these parameters, the optical depth to reionization $\tau_\mathrm{reio}$, represents the scatterings that Cosmic Microwave Ba
Externí odkaz:
http://arxiv.org/abs/2405.13680
Autor:
Parker, Liam, Lanusse, Francois, Golkar, Siavash, Sarra, Leopoldo, Cranmer, Miles, Bietti, Alberto, Eickenberg, Michael, Krawezik, Geraud, McCabe, Michael, Ohana, Ruben, Pettee, Mariel, Blancard, Bruno Regaldo-Saint, Tesileanu, Tiberiu, Cho, Kyunghyun, Ho, Shirley
We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used - without any model fine-tuning - for a variety of downstream tasks inc
Externí odkaz:
http://arxiv.org/abs/2310.03024
Autor:
McCabe, Michael, Blancard, Bruno Régaldo-Saint, Parker, Liam Holden, Ohana, Ruben, Cranmer, Miles, Bietti, Alberto, Eickenberg, Michael, Golkar, Siavash, Krawezik, Geraud, Lanusse, Francois, Pettee, Mariel, Tesileanu, Tiberiu, Cho, Kyunghyun, Ho, Shirley
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling. MPP involves training large surrogate models to predict the dynamics of multiple heterogeneous physical systems sim
Externí odkaz:
http://arxiv.org/abs/2310.02994
Autor:
Golkar, Siavash, Pettee, Mariel, Eickenberg, Michael, Bietti, Alberto, Cranmer, Miles, Krawezik, Geraud, Lanusse, Francois, McCabe, Michael, Ohana, Ruben, Parker, Liam, Blancard, Bruno Régaldo-Saint, Tesileanu, Tiberiu, Cho, Kyunghyun, Ho, Shirley
Large Language Models have not yet been broadly adapted for the analysis of scientific datasets due in part to the unique difficulties of tokenizing numbers. We propose xVal, a numerical encoding scheme that represents any real number using just a si
Externí odkaz:
http://arxiv.org/abs/2310.02989