Zobrazeno 1 - 10
of 13 279
pro vyhledávání: '"Denny, P"'
Autor:
Sidiropoulos, Themistoklis, Singh, Puloma, Noll, Tino, Schneider, Michael, Engel, Dieter, Sommer, Denny, Steinbach, Felix, Will, Ingo, Pfau, Bastian, Schmising, Clemens von Korff, Eisebitt, Stefan
All-optical helicity-independent switching (AO-HIS) is of interest for ultrafast and energy efficient magnetic switching in future magnetic data storage approaches. Yet, to achieve high bit density magnetic recording it is necessary to reduce the siz
Externí odkaz:
http://arxiv.org/abs/2408.13079
Autor:
Jarmusch, Aaron, Cabarcas, Felipe, Pophale, Swaroop, Kallai, Andrew, Doerfert, Johannes, Peyralans, Luke, Lee, Seyong, Denny, Joel, Chandrasekaran, Sunita
Software developers must adapt to keep up with the changing capabilities of platforms so that they can utilize the power of High- Performance Computers (HPC), including exascale systems. OpenMP, a directive-based parallel programming model, allows de
Externí odkaz:
http://arxiv.org/abs/2408.11777
We study the problem of learning multi-index models in high-dimensions using a two-layer neural network trained with the mean-field Langevin algorithm. Under mild distributional assumptions on the data, we characterize the effective dimension $d_{\ma
Externí odkaz:
http://arxiv.org/abs/2408.07254
Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning. In this work, we propose a deep latent state-space generative model t
Externí odkaz:
http://arxiv.org/abs/2407.19371
In this study, we present the Graph Sub-Graph Network (GSN), a novel hybrid image classification model merging the strengths of Convolutional Neural Networks (CNNs) for feature extraction and Graph Neural Networks (GNNs) for structural modeling. GSN
Externí odkaz:
http://arxiv.org/abs/2407.14772
Autor:
Reeves, Brent N., Prather, James, Denny, Paul, Leinonen, Juho, MacNeil, Stephen, Becker, Brett A., Luxton-Reilly, Andrew
Generative AI (GenAI) and large language models in particular, are disrupting Computer Science Education. They are proving increasingly capable at more and more challenges. Some educators argue that they pose a serious threat to computing education,
Externí odkaz:
http://arxiv.org/abs/2407.09231
Autor:
Hayakawa, Hisashi, Ebihara, Yusuke, Mishev, Alexander, Koldobskiy, Sergey, Kusano, Kanya, Bechet, Sabrina, Yashiro, Seiji, Iwai, Kazumasa, Shinbori, Atsuki, Mursula, Kalevi, Miyake, Fusa, Shiota, Daikou, Silveira, Marcos V. D., Stuart, Robert, Oliveira, Denny M., Akiyama, Sachiko, Ohnishi, Kouji, Miyoshi, Yoshizumi
In May 2024, the scientific community observed intense solar eruptions that resulted in an extreme geomagnetic storm and auroral extension, highlighting the need to document and quantify these events. This study mainly focuses on their quantification
Externí odkaz:
http://arxiv.org/abs/2407.07665
Autor:
Koutcheme, Charles, Dainese, Nicola, Hellas, Arto, Sarsa, Sami, Leinonen, Juho, Ashraf, Syed, Denny, Paul
The emergence of large language models (LLMs) has transformed research and practice in a wide range of domains. Within the computing education research (CER) domain, LLMs have received plenty of attention especially in the context of learning program
Externí odkaz:
http://arxiv.org/abs/2407.04873
We study the computational and sample complexity of learning a target function $f_*:\mathbb{R}^d\to\mathbb{R}$ with additive structure, that is, $f_*(x) = \frac{1}{\sqrt{M}}\sum_{m=1}^M f_m(\langle x, v_m\rangle)$, where $f_1,f_2,...,f_M:\mathbb{R}\t
Externí odkaz:
http://arxiv.org/abs/2406.11828
Autor:
Zheng, Huaixiu Steven, Mishra, Swaroop, Zhang, Hugh, Chen, Xinyun, Chen, Minmin, Nova, Azade, Hou, Le, Cheng, Heng-Tze, Le, Quoc V., Chi, Ed H., Zhou, Denny
We introduce NATURAL PLAN, a realistic planning benchmark in natural language containing 3 key tasks: Trip Planning, Meeting Planning, and Calendar Scheduling. We focus our evaluation on the planning capabilities of LLMs with full information on the
Externí odkaz:
http://arxiv.org/abs/2406.04520