Zobrazeno 1 - 10
of 25 292
pro vyhledávání: '"Or Milo"'
Autor:
Zimmerman, Julia Witte, Hudon, Denis, Cramer, Kathryn, Ruiz, Alejandro J., Beauregard, Calla, Fehr, Ashley, Fudolig, Mikaela Irene, Demarest, Bradford, Bird, Yoshi Meke, Trujillo, Milo Z., Danforth, Christopher M., Dodds, Peter Sheridan
Tokenization is a necessary component within the current architecture of many language models, including the transformer-based large language models (LLMs) of Generative AI, yet its impact on the model's cognition is often overlooked. We argue that L
Externí odkaz:
http://arxiv.org/abs/2412.10924
Autor:
Park, Pyeongjae, Ortiz, Brenden R., Sprague, Milo, Sakhya, Anup Pradhan, Chen, Si Athena, Frontzek, Matthias. D., Tian, Wei, Sibille, Romain, Mazzone, Daniel G., Tabata, Chihiro, Kaneko, Koji, DeBeer-Schmitt, Lisa M., Stone, Matthew B., Parker, David S., Samolyuk, German D., Miao, Hu, Neupane, Madhab, Christianson, Andrew D.
Kagome metals with van Hove singularities (VHSs) near the Fermi level can host intriguing quantum phenomena, including chiral loop currents, electronic nematicity, and unconventional superconductivity. However, unconventional magnetic states driven b
Externí odkaz:
http://arxiv.org/abs/2412.10286
Autor:
Havrilla, Alex, Dai, Andrew, O'Mahony, Laura, Oostermeijer, Koen, Zisler, Vera, Albalak, Alon, Milo, Fabrizio, Raparthy, Sharath Chandra, Gandhi, Kanishk, Abbasi, Baber, Phung, Duy, Iyer, Maia, Mahan, Dakota, Blagden, Chase, Gureja, Srishti, Hamdy, Mohammed, Li, Wen-Ding, Paolini, Giovanni, Ammanamanchi, Pawan Sasanka, Meyerson, Elliot
Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct comparisons among synthetic data generation algorithms are scarce, making it di
Externí odkaz:
http://arxiv.org/abs/2412.02980
Autor:
Stern, Ari, Viviani, Milo
Runge-Kutta methods are affine equivariant: applying a method before or after an affine change of variables yields the same numerical trajectory. However, for some applications, one would like to perform numerical integration after a quadratic change
Externí odkaz:
http://arxiv.org/abs/2411.12634
Autor:
Liu, Yahua, Hosseini, Seyed Ali, Liu, Cong, Feinberg, Milo, Dorschner, Benedikt, Wang, Zuankai, Karlin, Ilya
Contact time of bouncing drops is one of the most essential parameters to quantify the water-repellency of surfaces. Generally, the contact time on superhydrophobic surfaces is known to be Weber number-independent. Here, we probe an additional charac
Externí odkaz:
http://arxiv.org/abs/2410.20821
Autor:
Florentin, Dan I., Milo, Tomer
Assume that $rB_{2}^{n} \subset P$ for some polytope $P \subset \mathbb{R}^n$, where $r \in (\frac{1}{2},1]$. Denote by $\mathcal{F}$ the set of facets of $P$, and by $N=N(P,B_2^n)$ the covering number of $P$ by the Euclidean unit ball $B_2^n$. We pr
Externí odkaz:
http://arxiv.org/abs/2410.17811
Autor:
Cavagna, Antoine, Eder, Milo, Chowdhury, Enam, Kalouguine, André, Kaur, Jaismeen, Mourou, Gérard, Haessler, Stefan, Martens, Rodrigo Lopez
We report on continuous high-harmonic generation at 1 kHz repetition rate from a liquid-sheet plasma mirror driven by relativistic-intensity near-single-cycle light transients. Through precise control of both the surface plasma density gradient and t
Externí odkaz:
http://arxiv.org/abs/2410.15404
Autor:
Ran-Milo, Yuval, Lumbroso, Eden, Cohen-Karlik, Edo, Giryes, Raja, Globerson, Amir, Cohen, Nadav
Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most notably diagonal. In contrast to typical neural network modules, whose p
Externí odkaz:
http://arxiv.org/abs/2410.14067
Autor:
Orr, Daniel, Weising, Milo Bechtloff
The modified Macdonald functions $\widetilde{H}_{\mu}$ are fundamental objects in modern algebraic combinatorics. Haiman showed that there is a correspondence between the $(\mathbb{C}^{*})^2$-fixed points $I_{\mu}$ of the Hilbert schemes $\mathrm{Hil
Externí odkaz:
http://arxiv.org/abs/2410.13642
Autor:
Roucairol, Milo, Cazenave, Tristan
We are interested in the automatic refutation of spectral graph theory conjectures. Most existing works address this problem either with the exhaustive generation of graphs with a limited size or with deep reinforcement learning. Exhaustive generatio
Externí odkaz:
http://arxiv.org/abs/2409.18626