Zobrazeno 1 - 10
of 15 107
pro vyhledávání: '"WILSON, ANDREW"'
Autor:
Amin, Alan Nawzad, Gruver, Nate, Kuang, Yilun, Li, Lily, Elliott, Hunter, McCarter, Calvin, Raghu, Aniruddh, Greenside, Peyton, Wilson, Andrew Gordon
To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or by learning from large antibody databases to predict only typical antib
Externí odkaz:
http://arxiv.org/abs/2412.07763
Autor:
Masserano, Luca, Ansari, Abdul Fatir, Han, Boran, Zhang, Xiyuan, Faloutsos, Christos, Mahoney, Michael W., Wilson, Andrew Gordon, Park, Youngsuk, Rangapuram, Syama, Maddix, Danielle C., Wang, Yuyang
How to best develop foundational models for time series forecasting remains an important open question. Tokenization is a crucial consideration in this effort: what is an effective discrete vocabulary for a real-valued sequential input? To address th
Externí odkaz:
http://arxiv.org/abs/2412.05244
Autor:
Zhang, Hanyu, Arvin, Chuck, Efimov, Dmitry, Mahoney, Michael W., Perrault-Joncas, Dominique, Ramasubramanian, Shankar, Wilson, Andrew Gordon, Wolff, Malcolm
Modern time-series forecasting models often fail to make full use of rich unstructured information about the time series themselves. This lack of proper conditioning can lead to obvious model failures; for example, models may be unaware of the detail
Externí odkaz:
http://arxiv.org/abs/2412.02525
Autor:
Bhattacharjee, Ananya, Suh, Jina, Ershadi, Mahsa, Iqbal, Shamsi T., Wilson, Andrew D., Hernandez, Javier
Communication traits in text-based human-AI conversations play pivotal roles in shaping user experiences and perceptions of systems. With the advancement of large language models (LLMs), it is now feasible to analyze these traits at a more granular l
Externí odkaz:
http://arxiv.org/abs/2410.20468
Autor:
Potapczynski, Andres, Qiu, Shikai, Finzi, Marc, Ferri, Christopher, Chen, Zixi, Goldblum, Micah, Bruss, Bayan, De Sa, Christopher, Wilson, Andrew Gordon
Dense linear layers are the dominant computational bottleneck in large neural networks, presenting a critical need for more efficient alternatives. Previous efforts focused on a small number of hand-crafted structured matrices and neglected to invest
Externí odkaz:
http://arxiv.org/abs/2410.02117
Autor:
Numan, Nels, Rajaram, Shwetha, Kumaravel, Balasaravanan Thoravi, Marquardt, Nicolai, Wilson, Andrew D.
There is increased interest in using generative AI to create 3D spaces for Virtual Reality (VR) applications. However, today's models produce artificial environments, falling short of supporting collaborative tasks that benefit from incorporating the
Externí odkaz:
http://arxiv.org/abs/2409.13926
Autor:
Lotfi, Sanae, Kuang, Yilun, Amos, Brandon, Goldblum, Micah, Finzi, Marc, Wilson, Andrew Gordon
Large language models (LLMs) with billions of parameters excel at predicting the next token in a sequence. Recent work computes non-vacuous compression-based generalization bounds for LLMs, but these bounds are vacuous for large models at the billion
Externí odkaz:
http://arxiv.org/abs/2407.18158
Autor:
Shwartz-Ziv, Ravid, Goldblum, Micah, Bansal, Arpit, Bruss, C. Bayan, LeCun, Yann, Wilson, Andrew Gordon
It is widely believed that a neural network can fit a training set containing at least as many samples as it has parameters, underpinning notions of overparameterized and underparameterized models. In practice, however, we only find solutions accessi
Externí odkaz:
http://arxiv.org/abs/2406.11463
To make accurate predictions, understand mechanisms, and design interventions in systems of many variables, we wish to learn causal graphs from large scale data. Unfortunately the space of all possible causal graphs is enormous so scalably and accura
Externí odkaz:
http://arxiv.org/abs/2406.09177
Autor:
Kapoor, Sanyam, Gruver, Nate, Roberts, Manley, Collins, Katherine, Pal, Arka, Bhatt, Umang, Weller, Adrian, Dooley, Samuel, Goldblum, Micah, Wilson, Andrew Gordon
When using large language models (LLMs) in high-stakes applications, we need to know when we can trust their predictions. Some works argue that prompting high-performance LLMs is sufficient to produce calibrated uncertainties, while others introduce
Externí odkaz:
http://arxiv.org/abs/2406.08391