Zobrazeno 1 - 10
of 3 896
pro vyhledávání: '"A. Smola"'
Autor:
Pinc, Jan, Vlcak, Petr, Lebeda, Miroslav, Bartunek, Vilem, Smola, Vojtech, Vronka, Marek, Drahokoupil, Jan, Weiss, Zdenek, Svora, Petr, Lesakova, Hana, Sindelarova, Katerina, Molnarova, Orsolya, Horazdovsky, Tomas, Studecky, Tomas, Salvetr, Pavel, Kubasek, Jiri, Capek, Jaroslav, Skolakova, Andrea
In this research, the influence of the N+ ion implantation process on the microstructure of a biodegradable Zn-0.8Mg-0.2Sr alloy was investigated using various experimental techniques. Microscopic analysis revealed that a fluence of 17x10^17 ions/cm^
Externí odkaz:
http://arxiv.org/abs/2407.06616
Autor:
Tennyson, Jonathan, Yurchenko, Sergei N., Zhang, Jingxin, Bowesman, Charles A., Brady, Ryan P., Buldyreva, Jeanna, Chubb, Katy L., Gamache, Robert R., Gorman, Maire N., Guest, Elizabeth R., Hill, Christian, Kefala, Kyriaki, Lynas-Gray, A. E., Mellor, Thomas M., McKemmish, Laura K., Mitev, Georgi B., Mizus, Irina I., Owens, Alec, Peng, Zhijian, Perri, Armando N., Pezzella, Marco, Polyansky, Oleg L., Qu, Qianwei, Semenov, Mikhail, Smola, Oleksiy, Solokov, Andrei, Somogyi, Wilfrid, Upadhyay, Apoorva, Wright, Samuel O. M., Zobov, Nikolai F.
The ExoMol database (www.exomol.com) provides molecular data for spectroscopic studies of hot atmospheres. These data are widely used to model atmospheres of exoplanets, cool stars and other astronomical objects, as well as a variety of terrestrial a
Externí odkaz:
http://arxiv.org/abs/2406.06347
Diffusion models that are based on iterative denoising have been recently proposed and leveraged in various generation tasks like image generation. Whereas, as a way inherently built for continuous data, existing diffusion models still have some limi
Externí odkaz:
http://arxiv.org/abs/2304.04746
Autor:
Ren, Shuhuai, Zhang, Aston, Zhu, Yi, Zhang, Shuai, Zheng, Shuai, Li, Mu, Smola, Alex, Sun, Xu
This work proposes POMP, a prompt pre-training method for vision-language models. Being memory and computation efficient, POMP enables the learned prompt to condense semantic information for a rich set of visual concepts with over twenty-thousand cla
Externí odkaz:
http://arxiv.org/abs/2304.04704
Autor:
Garg, Saurabh, Erickson, Nick, Sharpnack, James, Smola, Alex, Balakrishnan, Sivaraman, Lipton, Zachary C.
Despite the emergence of principled methods for domain adaptation under label shift, their sensitivity to shifts in class conditional distributions is precariously under explored. Meanwhile, popular deep domain adaptation heuristics tend to falter wh
Externí odkaz:
http://arxiv.org/abs/2302.03020
Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer. However, existing CoT studies have prim
Externí odkaz:
http://arxiv.org/abs/2302.00923
Parameter-efficient fine-tuning aims to achieve performance comparable to fine-tuning, using fewer trainable parameters. Several strategies (e.g., Adapters, prefix tuning, BitFit, and LoRA) have been proposed. However, their designs are hand-crafted
Externí odkaz:
http://arxiv.org/abs/2301.01821
Large language models (LLMs) can perform complex reasoning by generating intermediate reasoning steps. Providing these steps for prompting demonstrations is called chain-of-thought (CoT) prompting. CoT prompting has two major paradigms. One leverages
Externí odkaz:
http://arxiv.org/abs/2210.03493
Real-world deployment of machine learning models is challenging because data evolves over time. While no model can work when data evolves in an arbitrary fashion, if there is some pattern to these changes, we might be able to design methods to addres
Externí odkaz:
http://arxiv.org/abs/2210.01422
Existing out-of-distribution (OOD) detection methods are typically benchmarked on training sets with balanced class distributions. However, in real-world applications, it is common for the training sets to have long-tailed distributions. In this work
Externí odkaz:
http://arxiv.org/abs/2207.01160