Zobrazeno 1 - 10
of 11 113
pro vyhledávání: '"Sandler P"'
Autor:
Glazer, Elliot, Erdil, Ege, Besiroglu, Tamay, Chicharro, Diego, Chen, Evan, Gunning, Alex, Olsson, Caroline Falkman, Denain, Jean-Stanislas, Ho, Anson, Santos, Emily de Oliveira, Järviniemi, Olli, Barnett, Matthew, Sandler, Robert, Vrzala, Matej, Sevilla, Jaime, Ren, Qiuyu, Pratt, Elizabeth, Levine, Lionel, Barkley, Grant, Stewart, Natalie, Grechuk, Bogdan, Grechuk, Tetiana, Enugandla, Shreepranav Varma, Wildon, Mark
We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most major branches of modern mathematics -- from computationally intensiv
Externí odkaz:
http://arxiv.org/abs/2411.04872
Publikováno v:
ICML 2024 Workshop on Mechanistic Interpretability
What happens when a new piece of knowledge is introduced into the training data and how long does it last while a large language model (LM) continues to train? We investigate this question by injecting facts into LMs from a new probing dataset, "Outl
Externí odkaz:
http://arxiv.org/abs/2410.21750
Fine-tuning modern computer vision models requires accurately labeled data for which the ground truth may not exist, but a set of multiple labels can be obtained from labelers of variable accuracy. We tie the notion of label quality to confidence in
Externí odkaz:
http://arxiv.org/abs/2410.00085
This paper presents an examination of State Space Models (SSM) and Koopman-based deep learning methods for modelling the dynamics of both linear and non-linear stiff strings. Through experiments with datasets generated under different initial conditi
Externí odkaz:
http://arxiv.org/abs/2408.16650
Autor:
Kristiansen, Gus, Sandler, Mark, Zhmoginov, Andrey, Miller, Nolan, Goyal, Anirudh, Lee, Jihwan, Vladymyrov, Max
In modern deep learning, the models are learned by applying gradient updates using an optimizer, which transforms the updates based on various statistics. Optimizers are often hand-designed and tuning their hyperparameters is a big part of the traini
Externí odkaz:
http://arxiv.org/abs/2408.09310
Autor:
Judge, Arnaud, Judge, Thierry, Duchateau, Nicolas, Sandler, Roman A., Sokol, Joseph Z., Bernard, Olivier, Jodoin, Pierre-Marc
Performance of deep learning segmentation models is significantly challenged in its transferability across different medical imaging domains, particularly when aiming to adapt these models to a target domain with insufficient annotated data for effec
Externí odkaz:
http://arxiv.org/abs/2406.17902
Publikováno v:
Phys. Rev. B 109, 245403 (2024)
We study a system composed of graphene decorated with an array of islands with C_3v symmetry that induce quantum dot (IQD) regions via proximity effects and give rise to several spin-orbit couplings (SOCs). We evaluate transport properties for an arr
Externí odkaz:
http://arxiv.org/abs/2406.02393
Autor:
Li, Thomas Z., Xu, Kaiwen, Krishnan, Aravind, Gao, Riqiang, Kammer, Michael N., Antic, Sanja, Xiao, David, Knight, Michael, Martinez, Yency, Paez, Rafael, Lentz, Robert J., Deppen, Stephen, Grogan, Eric L., Lasko, Thomas A., Sandler, Kim L., Maldonado, Fabien, Landman, Bennett A.
Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinica
Externí odkaz:
http://arxiv.org/abs/2405.10993
Subjecting a massive two-dimensional Dirac material to a vortex light beam provides a mechanism for the photo-induction of multiply quantized vortices. Using Floquet theory, we show that electronic vortices, characterized by their total angular momen
Externí odkaz:
http://arxiv.org/abs/2404.09086
Recent research has demonstrated that transformers, particularly linear attention models, implicitly execute gradient-descent-like algorithms on data provided in-context during their forward inference step. However, their capability in handling more
Externí odkaz:
http://arxiv.org/abs/2402.14180