Zobrazeno 1 - 10
of 157 388
pro vyhledávání: '"Colin, P."'
This paper is motivated by recent works on inverse problems for acoustic wave propagation in the interior of gas giant planets. In such planets, the speed of sound is isotropic and tends to zero at the surface. Geometrically, this corresponds to a Ri
Externí odkaz:
http://arxiv.org/abs/2406.19734
Autor:
White, Colin, Dooley, Samuel, Roberts, Manley, Pal, Arka, Feuer, Ben, Jain, Siddhartha, Shwartz-Ziv, Ravid, Jain, Neel, Saifullah, Khalid, Naidu, Siddartha, Hegde, Chinmay, LeCun, Yann, Goldstein, Tom, Neiswanger, Willie, Goldblum, Micah
Test set contamination, wherein test data from a benchmark ends up in a newer model's training set, is a well-documented obstacle for fair LLM evaluation and can quickly render benchmarks obsolete. To mitigate this, many recent benchmarks crowdsource
Externí odkaz:
http://arxiv.org/abs/2406.19314
Autor:
Moreira, Ana, Leifler, Ola, Betz, Stefanie, Brooks, Ian, Capilla, Rafael, Coroama, Vlad Constantin, Duboc, Leticia, Fernandes, Joao Paulo, Heldal, Rogardt, Lago, Patricia, Nguyen, Ngoc-Thanh, Oyedeji, Shola, Penzenstadler, Birgit, Peters, Anne Kathrin, Porras, Jari, Venters, Colin C.
Education for sustainable development has evolved to include more constructive approaches and a better understanding of what is needed to align education with the cultural, societal, and pedagogical changes required to avoid the risks posed by an uns
Externí odkaz:
http://arxiv.org/abs/2406.18945
Autor:
Wei, Hui, Xu, Maxwell A., Samplawski, Colin, Rehg, James M., Kumar, Santosh, Marlin, Benjamin M.
Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subject to extensive missingness due to a complex combination of factors. I
Externí odkaz:
http://arxiv.org/abs/2406.18848
Autor:
Ren, Wenke, Guo, Hengxiao, Shen, Yue, Silverman, John D., Burke, Colin J., Wang, Shu, Wang, Junxian
We introduce an improved method for decomposing the emission of active galactic nuclei (AGN) and their host galaxies using templates from principal component analysis (PCA). This approach integrates prior information from PCA with a penalized pixel f
Externí odkaz:
http://arxiv.org/abs/2406.17598
Autor:
Penedo, Guilherme, Kydlíček, Hynek, allal, Loubna Ben, Lozhkov, Anton, Mitchell, Margaret, Raffel, Colin, Von Werra, Leandro, Wolf, Thomas
The performance of a large language model (LLM) depends heavily on the quality and size of its pretraining dataset. However, the pretraining datasets for state-of-the-art open LLMs like Llama 3 and Mixtral are not publicly available and very little i
Externí odkaz:
http://arxiv.org/abs/2406.17557
Nobel laureate Philip Anderson and Elihu Abrahams once stated that, "even if machines did contribute to normal science, we see no mechanism by which they could create a Kuhnian revolution and thereby establish a new physical law." In this Perspective
Externí odkaz:
http://arxiv.org/abs/2406.17836
Generally, discretization of partial differential equations (PDEs) creates a sequence of linear systems $A_k x_k = b_k, k = 0, 1, 2, ..., N$ with well-known and structured sparsity patterns. Preconditioners are often necessary to achieve fast converg
Externí odkaz:
http://arxiv.org/abs/2406.17656
Autor:
Gleason, Samuel P., Rakowski, Alexander, Ribet, Stephanie M., Zeltmann, Steven E., Savitzky, Benjamin H., Henderson, Matthew, Ciston, Jim, Ophus, Colin
Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here we train an a
Externí odkaz:
http://arxiv.org/abs/2406.16310
Autor:
Jeon, Byungsoo, Wu, Mengdi, Cao, Shiyi, Kim, Sunghyun, Park, Sunghyun, Aggarwal, Neeraj, Unger, Colin, Arfeen, Daiyaan, Liao, Peiyuan, Miao, Xupeng, Alizadeh, Mohammad, Ganger, Gregory R., Chen, Tianqi, Jia, Zhihao
Deep neural networks (DNNs) continue to grow rapidly in size, making them infeasible to train on a single device. Pipeline parallelism is commonly used in existing DNN systems to support large-scale DNN training by partitioning a DNN into multiple st
Externí odkaz:
http://arxiv.org/abs/2406.17145