Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Cranmer, Miles"'
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
Autor:
Pedersen, Christian, Tesileanu, Tiberiu, Wu, Tinghui, Golkar, Siavash, Cranmer, Miles, Zhang, Zijun, Ho, Shirley
In Elmarakeby et al., "Biologically informed deep neural network for prostate cancer discovery", a feedforward neural network with biologically informed, sparse connections (P-NET) was presented to model the state of prostate cancer. We verified the
Externí odkaz:
http://arxiv.org/abs/2309.16645
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 295, 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:
http://arxiv.org/abs/2305.04099
Autor:
Cranmer, Miles
PySR is an open-source library for practical symbolic regression, a type of machine learning which aims to discover human-interpretable symbolic models. PySR was developed to democratize and popularize symbolic regression for the sciences, and is bui
Externí odkaz:
http://arxiv.org/abs/2305.01582
Machine learning has become increasingly popular for efficiently modelling the dynamics of complex physical systems, demonstrating a capability to learn effective models for dynamics which ignore redundant degrees of freedom. Learned simulators typic
Externí odkaz:
http://arxiv.org/abs/2211.15338
We propose parameterizing the population distribution of the gravitational wave population modeling framework (Hierarchical Bayesian Analysis) with a normalizing flow. We first demonstrate the merit of this method on illustrative experiments and then
Externí odkaz:
http://arxiv.org/abs/2211.09008