Zobrazeno 1 - 10
of 17 773
pro vyhledávání: '"A A, Alexandrov"'
Autor:
Weber, Maurice, Fu, Daniel, Anthony, Quentin, Oren, Yonatan, Adams, Shane, Alexandrov, Anton, Lyu, Xiaozhong, Nguyen, Huu, Yao, Xiaozhe, Adams, Virginia, Athiwaratkun, Ben, Chalamala, Rahul, Chen, Kezhen, Ryabinin, Max, Dao, Tri, Liang, Percy, Ré, Christopher, Rish, Irina, Zhang, Ce
Large language models are increasingly becoming a cornerstone technology in artificial intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset composition and filtering remain largely elusive. Many of the top-perfor
Externí odkaz:
http://arxiv.org/abs/2411.12372
Autor:
Adak, Dibyendu, Truong, Duc P., Vuchkov, Radoslav, De, Saibal, DeSantis, Derek, Roberts, Nathan V., Rasmussen, Kim Ø., Alexandrov, Boian S.
In this paper, we present a new space-time Petrov-Galerkin-like method. This method utilizes a mixed formulation of Tensor Train (TT) and Quantized Tensor Train (QTT), designed for the spectral element discretization (Q1-SEM) of the time-dependent co
Externí odkaz:
http://arxiv.org/abs/2411.04026
Autor:
Barron, Ryan C., Grantcharov, Ves, Wanna, Selma, Eren, Maksim E., Bhattarai, Manish, Solovyev, Nicholas, Tompkins, George, Nicholas, Charles, Rasmussen, Kim Ø., Matuszek, Cynthia, Alexandrov, Boian S.
Large Language Models (LLMs) are pre-trained on large-scale corpora and excel in numerous general natural language processing (NLP) tasks, such as question answering (QA). Despite their advanced language capabilities, when it comes to domain-specific
Externí odkaz:
http://arxiv.org/abs/2410.02721
Autor:
Zakharova, Anastasiia, Alexandrov, Dmitriy, Khodorchenko, Maria, Butakov, Nikolay, Vasilev, Alexey, Savchenko, Maxim, Grigorievskiy, Alexander
Machine learning (ML) models trained on datasets owned by different organizations and physically located in remote databases offer benefits in many real-world use cases. State regulations or business requirements often prevent data transfer to a cent
Externí odkaz:
http://arxiv.org/abs/2409.15558
Autor:
Bunne, Charlotte, Roohani, Yusuf, Rosen, Yanay, Gupta, Ankit, Zhang, Xikun, Roed, Marcel, Alexandrov, Theo, AlQuraishi, Mohammed, Brennan, Patricia, Burkhardt, Daniel B., Califano, Andrea, Cool, Jonah, Dernburg, Abby F., Ewing, Kirsty, Fox, Emily B., Haury, Matthias, Herr, Amy E., Horvitz, Eric, Hsu, Patrick D., Jain, Viren, Johnson, Gregory R., Kalil, Thomas, Kelley, David R., Kelley, Shana O., Kreshuk, Anna, Mitchison, Tim, Otte, Stephani, Shendure, Jay, Sofroniew, Nicholas J., Theis, Fabian, Theodoris, Christina V., Upadhyayula, Srigokul, Valer, Marc, Wang, Bo, Xing, Eric, Yeung-Levy, Serena, Zitnik, Marinka, Karaletsos, Theofanis, Regev, Aviv, Lundberg, Emma, Leskovec, Jure, Quake, Stephen R.
The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intell
Externí odkaz:
http://arxiv.org/abs/2409.11654
Autor:
Zollicoffer, Geigh, Vu, Minh, Nebgen, Ben, Castorena, Juan, Alexandrov, Boian, Bhattarai, Manish
This work presents an information-theoretic examination of diffusion-based purification methods, the state-of-the-art adversarial defenses that utilize diffusion models to remove malicious perturbations in adversarial examples. By theoretically chara
Externí odkaz:
http://arxiv.org/abs/2409.08255
Autor:
Lebedev, Anton, Alexandrov, Vassil
Publikováno v:
2018 IEEE/ACM 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (scalA), Dallas, TX, USA, 2018, pp. 81-90
In this paper we present computational experiments with the Markov Chain Monte Carlo Matrix Inversion ($(\text{MC})^2\text{MI}$) on several accelerator architectures and investigate their impact on performance and scalability of the method. The metho
Externí odkaz:
http://arxiv.org/abs/2409.03095
Autor:
Vu, Minh, Nebgen, Ben, Skau, Erik, Zollicoffer, Geigh, Castorena, Juan, Rasmussen, Kim, Alexandrov, Boian, Bhattarai, Manish
As Machine Learning (ML) applications rapidly grow, concerns about adversarial attacks compromising their reliability have gained significant attention. One unsupervised ML method known for its resilience to such attacks is Non-negative Matrix Factor
Externí odkaz:
http://arxiv.org/abs/2408.03909
Autor:
Danis, Mustafa Engin, Truong, Duc P., DeSantis, Derek, Petersen, Mark, Rasmussen, Kim O., Alexandrov, Boian S.
In this paper, we introduce a high-order tensor-train (TT) finite volume method for the Shallow Water Equations (SWEs). We present the implementation of the $3^{rd}$ order Upwind and the $5^{th}$ order Upwind and WENO reconstruction schemes in the TT
Externí odkaz:
http://arxiv.org/abs/2408.03483
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing (NLP) tasks, such as question-answering, sentiment analysis, text summarization, and machine translation. Howev
Externí odkaz:
http://arxiv.org/abs/2408.01008