Zobrazeno 1 - 10
of 556
pro vyhledávání: '"Pettee , Mariel"'
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
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 of spatiotemporal systems with transformers. In MPP, rather than training one model on a specific physical system, w
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
Due in part to their discontinuous and discrete default encodings for numbers, Large Language Models (LLMs) have not yet been commonly used to process numerically-dense scientific datasets. Rendering datasets as text, however, could help aggregate di
Externí odkaz:
http://arxiv.org/abs/2310.02989
The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions, and more. Many modern scientific applications, however, make use of d
Externí odkaz:
http://arxiv.org/abs/2305.10500
Autor:
Pettee, Mariel, Thanvantri, Sowmya, Nachman, Benjamin, Shih, David, Buckley, Matthew R., Collins, Jack H.
Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we use Classification Without Labels (CWoLa), a weak
Externí odkaz:
http://arxiv.org/abs/2305.03761
Many particle physics datasets like those generated at colliders are described by continuous coordinates (in contrast to grid points like in an image), respect a number of symmetries (like permutation invariance), and have a stochastic dimensionality
Externí odkaz:
http://arxiv.org/abs/2304.01266
We present a new algorithm that identifies reconstructed jets originating from hadronic decays of tau leptons against those from quarks or gluons. No tau lepton reconstruction algorithm is used. Instead, the algorithm represents jets as heterogeneous
Externí odkaz:
http://arxiv.org/abs/2301.00501
We summarize the model and results of PirouNet, a semi-supervised recurrent variational autoencoder. Given a small amount of dance sequences labeled with qualitative choreographic annotations, PirouNet conditionally generates dance sequences in the s
Externí odkaz:
http://arxiv.org/abs/2209.10010
Autor:
Kasieczka, Gregor, Mastandrea, Radha, Mikuni, Vinicius, Nachman, Benjamin, Pettee, Mariel, Shih, David
There is a growing need for machine learning-based anomaly detection strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at the Large Hadron Collider (LHC) and elsewhere. The first step of any anomaly detection approach is to
Externí odkaz:
http://arxiv.org/abs/2209.06225