Zobrazeno 1 - 10
of 1 345
pro vyhledávání: '"A. Cubuk"'
Autor:
Gangan, Abhijeet S., Schoenholz, Samuel S., Cubuk, Ekin Dogus, Bauchy, Mathieu, Krishnan, N. M. Anoop
The accuracy of atomistic simulations depends on the precision of force fields. Traditional numerical methods often struggle to optimize the empirical force field parameters for reproducing target properties. Recent approaches rely on training these
Externí odkaz:
http://arxiv.org/abs/2409.13844
Autor:
Yang, Sherry, Batzner, Simon, Gao, Ruiqi, Aykol, Muratahan, Gaunt, Alexander L., McMorrow, Brendan, Rezende, Danilo J., Schuurmans, Dale, Mordatch, Igor, Cubuk, Ekin D.
Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal structures, the guidance from t
Externí odkaz:
http://arxiv.org/abs/2409.06762
This paper investigates the performance of the Contrastive Language-Image Pre-training (CLIP) when scaled down to limited computation budgets. We explore CLIP along three dimensions: data, architecture, and training strategies. With regards to data,
Externí odkaz:
http://arxiv.org/abs/2404.08197
Autor:
Real, Esteban, Chen, Yao, Rossini, Mirko, de Souza, Connal, Garg, Manav, Verghese, Akhil, Firsching, Moritz, Le, Quoc V., Cubuk, Ekin Dogus, Park, David H.
Computers calculate transcendental functions by approximating them through the composition of a few limited-precision instructions. For example, an exponential can be calculated with a Taylor series. These approximation methods were developed over th
Externí odkaz:
http://arxiv.org/abs/2312.08472
Autor:
Yang, Samuel J., Li, Shutong, Venugopalan, Subhashini, Tshitoyan, Vahe, Aykol, Muratahan, Merchant, Amil, Cubuk, Ekin Dogus, Cheon, Gowoon
Machine learning is transforming materials discovery by providing rapid predictions of material properties, which enables large-scale screening for target materials. However, such models require training data. While automated data extraction from sci
Externí odkaz:
http://arxiv.org/abs/2311.13778
Autor:
Yang, Sherry, Cho, KwangHwan, Merchant, Amil, Abbeel, Pieter, Schuurmans, Dale, Mordatch, Igor, Cubuk, Ekin Dogus
Generative models trained on internet-scale data are capable of generating novel and realistic texts, images, and videos. A natural next question is whether these models can advance science, for example by generating novel stable materials. Tradition
Externí odkaz:
http://arxiv.org/abs/2311.09235
Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synthesis and development of new materials in the laboratory. Predicting th
Externí odkaz:
http://arxiv.org/abs/2310.01117
Data augmentation methods have played an important role in the recent advance of deep learning models, and have become an indispensable component of state-of-the-art models in semi-supervised, self-supervised, and supervised training for vision. Desp
Externí odkaz:
http://arxiv.org/abs/2305.13520
Autor:
Phuthi, Mgcini Keith, Yao, Archie Mingze, Batzner, Simon, Musaelian, Albert, Kozinsky, Boris, Cubuk, Ekin Dogus, Viswanathan, Venkatasubramanian
The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity and low melting point of lithium as well as the microscopic scales at w
Externí odkaz:
http://arxiv.org/abs/2305.06925
Autor:
Leng, Zhaoqi, Li, Guowang, Liu, Chenxi, Cubuk, Ekin Dogus, Sun, Pei, He, Tong, Anguelov, Dragomir, Tan, Mingxing
Data augmentations are important in training high-performance 3D object detectors for point clouds. Despite recent efforts on designing new data augmentations, perhaps surprisingly, most state-of-the-art 3D detectors only use a few simple data augmen
Externí odkaz:
http://arxiv.org/abs/2210.13488