Zobrazeno 1 - 10
of 12 296
pro vyhledávání: '"Heckel BE"'
Autor:
Heckel, Kade M., Weller, Adrian
With the capability to write convincing and fluent natural language and generate code, Foundation Models present dual-use concerns broadly and within the cyber domain specifically. Generative AI has already begun to impact cyberspace through a broad
Externí odkaz:
http://arxiv.org/abs/2410.18312
Autor:
Heckel, Annika
The cochromatic number $\zeta(G)$ of a graph $G$ is the minimum number of colours needed for a vertex colouring where every colour class is either an independent set or a clique. Let $\chi(G)$ denote the usual chromatic number. Around 1991 Erd\H{o}s
Externí odkaz:
http://arxiv.org/abs/2409.17614
A major challenge of the long measurement times in magnetic resonance imaging (MRI), an important medical imaging technology, is that patients may move during data acquisition. This leads to severe motion artifacts in the reconstructed images and vol
Externí odkaz:
http://arxiv.org/abs/2409.09370
Autor:
Heckel, Annika
In this note, we show that the difference between the chromatic and the cochromatic number of the random graph $G_{n,1/2}$ is not whp bounded by $n^{1/2-o(1)}$, addressing a question of Erd\H{o}s and Gimbel.
Comment: 4 pages
Comment: 4 pages
Externí odkaz:
http://arxiv.org/abs/2408.13839
Autor:
Li, Jeffrey, Fang, Alex, Smyrnis, Georgios, Ivgi, Maor, Jordan, Matt, Gadre, Samir, Bansal, Hritik, Guha, Etash, Keh, Sedrick, Arora, Kushal, Garg, Saurabh, Xin, Rui, Muennighoff, Niklas, Heckel, Reinhard, Mercat, Jean, Chen, Mayee, Gururangan, Suchin, Wortsman, Mitchell, Albalak, Alon, Bitton, Yonatan, Nezhurina, Marianna, Abbas, Amro, Hsieh, Cheng-Yu, Ghosh, Dhruba, Gardner, Josh, Kilian, Maciej, Zhang, Hanlin, Shao, Rulin, Pratt, Sarah, Sanyal, Sunny, Ilharco, Gabriel, Daras, Giannis, Marathe, Kalyani, Gokaslan, Aaron, Zhang, Jieyu, Chandu, Khyathi, Nguyen, Thao, Vasiljevic, Igor, Kakade, Sham, Song, Shuran, Sanghavi, Sujay, Faghri, Fartash, Oh, Sewoong, Zettlemoyer, Luke, Lo, Kyle, El-Nouby, Alaaeldin, Pouransari, Hadi, Toshev, Alexander, Wang, Stephanie, Groeneveld, Dirk, Soldaini, Luca, Koh, Pang Wei, Jitsev, Jenia, Kollar, Thomas, Dimakis, Alexandros G., Carmon, Yair, Dave, Achal, Schmidt, Ludwig, Shankar, Vaishaal
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardized corpus of 240T tokens extracted from Common Crawl, effective pretrai
Externí odkaz:
http://arxiv.org/abs/2406.11794
In the quest for next-generation sequence modeling architectures, State Space Models (SSMs) have emerged as a potent alternative to transformers, particularly for their computational efficiency and suitability for dynamical systems. This paper invest
Externí odkaz:
http://arxiv.org/abs/2406.09477
Autor:
Smith, G. L., Hoyle, C. D., Gundlach, J. H., Adelberger, E. G., Heckel, B. R., Swanson, H. E.
Publikováno v:
Physical Review D 61, 022001, 1999
We tested the equivalence principle at short length scales by rotating a 3-ton $^{238}$U attractor around a compact torsion balance containing Cu and Pb test bodies. The observed differential acceleration of the test bodies toward the attractor, $a_{
Externí odkaz:
http://arxiv.org/abs/2405.10982
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstructi
Externí odkaz:
http://arxiv.org/abs/2404.15692
Autor:
Müller, Patric, Sack, Achim, Dümler, Jens, Heckel, Michael, Wenzel, Tim, Siegert, Teresa, Schuldt-Lieb, Sonja, Gieseler, Henning, Pöschel, Thorsten
The topology and surface characteristics of lyophilisates significantly impact the stability and reconstitutability of freeze-dried pharmaceuticals. Consequently, visual quality control of the product is imperative. However, this procedure is not onl
Externí odkaz:
http://arxiv.org/abs/2404.11867
Autor:
Mansour, Youssef, Heckel, Reinhard
Deep learning-based methods have shown remarkable success for various image restoration tasks such as denoising and deblurring. The current state-of-the-art networks are relatively deep and utilize (variants of) self attention mechanisms. Those netwo
Externí odkaz:
http://arxiv.org/abs/2404.00807