Zobrazeno 1 - 10
of 4 324
pro vyhledávání: '"Cresswell, P"'
Autor:
Vouitsis, Noël, Hosseinzadeh, Rasa, Ross, Brendan Leigh, Villecroze, Valentin, Gorti, Satya Krishna, Cresswell, Jesse C., Loaiza-Ganem, Gabriel
Although diffusion models can generate remarkably high-quality samples, they are intrinsically bottlenecked by their expensive iterative sampling procedure. Consistency models (CMs) have recently emerged as a promising diffusion model distillation me
Externí odkaz:
http://arxiv.org/abs/2411.08954
Autor:
Yang, Yifei, Lee, Seungjun, Chen, Yu-Chia, Jia, Qi, Sousa, Duarte, Odlyzko, Michael, Garcia-Barriocanal, Javier, Yu, Guichuan, Haugstad, Greg, Fan, Yihong, Huang, Yu-Han, Lyu, Deyuan, Cresswell, Zach, Low, Tony, Wang, Jian-Ping
Spin-orbit torque (SOT) can be used to efficiently manipulate the magnetic state of magnetic materials, which is an essential element for memory and logic applications. Due to symmetry constraints, only in-plane spins can be injected into the ferroma
Externí odkaz:
http://arxiv.org/abs/2411.05682
Autor:
Ross, Brendan Leigh, Kamkari, Hamidreza, Wu, Tongzi, Hosseinzadeh, Rasa, Liu, Zhaoyan, Stein, George, Cresswell, Jesse C., Loaiza-Ganem, Gabriel
As deep generative models have progressed, recent work has shown them to be capable of memorizing and reproducing training datapoints when deployed. These findings call into question the usability of generative models, especially in light of the lega
Externí odkaz:
http://arxiv.org/abs/2411.00113
Autor:
Krause, Claudius, Giannelli, Michele Faucci, Kasieczka, Gregor, Nachman, Benjamin, Salamani, Dalila, Shih, David, Zaborowska, Anna, Amram, Oz, Borras, Kerstin, Buckley, Matthew R., Buhmann, Erik, Buss, Thorsten, Cardoso, Renato Paulo Da Costa, Caterini, Anthony L., Chernyavskaya, Nadezda, Corchia, Federico A. G., Cresswell, Jesse C., Diefenbacher, Sascha, Dreyer, Etienne, Ekambaram, Vijay, Eren, Engin, Ernst, Florian, Favaro, Luigi, Franchini, Matteo, Gaede, Frank, Gross, Eilam, Hsu, Shih-Chieh, Jaruskova, Kristina, Käch, Benno, Kalagnanam, Jayant, Kansal, Raghav, Kim, Taewoo, Kobylianskii, Dmitrii, Korol, Anatolii, Korcari, William, Krücker, Dirk, Krüger, Katja, Letizia, Marco, Li, Shu, Liu, Qibin, Liu, Xiulong, Loaiza-Ganem, Gabriel, Madula, Thandikire, McKeown, Peter, Melzer-Pellmann, Isabell-A., Mikuni, Vinicius, Nguyen, Nam, Ore, Ayodele, Schweitzer, Sofia Palacios, Pang, Ian, Pedro, Kevin, Plehn, Tilman, Pokorski, Witold, Qu, Huilin, Raikwar, Piyush, Raine, John A., Reyes-Gonzalez, Humberto, Rinaldi, Lorenzo, Ross, Brendan Leigh, Scham, Moritz A. W., Schnake, Simon, Shimmin, Chase, Shlizerman, Eli, Soybelman, Nathalie, Srivatsa, Mudhakar, Tsolaki, Kalliopi, Vallecorsa, Sofia, Yeo, Kyongmin, Zhang, Rui
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few t
Externí odkaz:
http://arxiv.org/abs/2410.21611
Autor:
Ma, Junwei, Thomas, Valentin, Hosseinzadeh, Rasa, Kamkari, Hamidreza, Labach, Alex, Cresswell, Jesse C., Golestan, Keyvan, Yu, Guangwei, Volkovs, Maksims, Caterini, Anthony L.
The challenges faced by neural networks on tabular data are well-documented and have hampered the progress of tabular foundation models. Techniques leveraging in-context learning (ICL) have shown promise here, allowing for dynamic adaptation to unsee
Externí odkaz:
http://arxiv.org/abs/2410.18164
Autor:
Gorti, Satya Krishna, Gofman, Ilan, Liu, Zhaoyan, Wu, Jiapeng, Vouitsis, Noël, Yu, Guangwei, Cresswell, Jesse C., Hosseinzadeh, Rasa
Text-to-SQL generation enables non-experts to interact with databases via natural language. Recent advances rely on large closed-source models like GPT-4 that present challenges in accessibility, privacy, and latency. To address these issues, we focu
Externí odkaz:
http://arxiv.org/abs/2410.12916
Although conformal prediction is a promising method for quantifying the uncertainty of machine learning models, the prediction sets it outputs are not inherently actionable. Many applications require a single output to act on, not several. To overcom
Externí odkaz:
http://arxiv.org/abs/2410.01888
Autor:
Cresswell-Clay, Nathaniel, Liu, Bowen, Durran, Dale, Liu, Andy, Espinosa, Zachary I., Moreno, Raul, Karlbauer, Matthias
A key challenge for computationally intensive state-of-the-art Earth-system models is to distinguish global warming signals from interannual variability. Recently machine learning models have performed better than state-of-the-art numerical weather p
Externí odkaz:
http://arxiv.org/abs/2409.16247
Autor:
Cresswell, Jesse C., Kim, Taewoo
Novel machine learning methods for tabular data generation are often developed on small datasets which do not match the scale required for scientific applications. We investigate a recent proposal to use XGBoost as the function approximator in diffus
Externí odkaz:
http://arxiv.org/abs/2408.16046
Autor:
Kowalczuk, Antoni, Dubiński, Jan, Ghomi, Atiyeh Ashari, Sui, Yi, Stein, George, Wu, Jiapeng, Cresswell, Jesse C., Boenisch, Franziska, Dziedzic, Adam
Large-scale vision models have become integral in many applications due to their unprecedented performance and versatility across downstream tasks. However, the robustness of these foundation models has primarily been explored for a single task, name
Externí odkaz:
http://arxiv.org/abs/2407.12588