Zobrazeno 1 - 10
of 21 683
pro vyhledávání: '"Klemm, A."'
Autor:
Bagajo, Joshua, Schwarke, Clemens, Klemm, Victor, Georgiev, Ignat, Sleiman, Jean-Pierre, Tordesillas, Jesus, Garg, Animesh, Hutter, Marco
Differentiable simulators provide analytic gradients, enabling more sample-efficient learning algorithms and paving the way for data intensive learning tasks such as learning from images. In this work, we demonstrate that locomotion policies trained
Externí odkaz:
http://arxiv.org/abs/2411.02189
Autor:
Klemm, Mason L., Siddique, Saif, Chang, Yuan-Chun, Xu, Sijie, Xie, Yaofeng, Legvold, Tanner, Kiani, Mehrdad T., Ye, Feng, Cao, Huibo, Hao, Yiqing, Tian, Wei, Luetkens, Hubertus, Matsuda, Masaaki, Natelson, Douglas, Guguchia, Zurab, Huang, Chien-Lung, Yi, Ming, Cha, Judy J., Dai, Pengcheng
Two-dimensional (2D) kagome lattice metals are interesting because they display flat electronic bands, Dirac points, Van Hove singularities, and can have interplay between charge density wave (CDW), magnetic order, and superconductivity. In kagome la
Externí odkaz:
http://arxiv.org/abs/2410.13994
First-order Policy Gradient (FoPG) algorithms such as Backpropagation through Time and Analytical Policy Gradients leverage local simulation physics to accelerate policy search, significantly improving sample efficiency in robot control compared to s
Externí odkaz:
http://arxiv.org/abs/2410.03076
Robot soccer, in its full complexity, poses an unsolved research challenge. Current solutions heavily rely on engineered heuristic strategies, which lack robustness and adaptability. Deep reinforcement learning has gained significant traction in vari
Externí odkaz:
http://arxiv.org/abs/2409.20326
Autor:
Gao, Bin, Chen, Tong, Liu, Chunxiao, Klemm, Mason L., Zhang, Shu, Ma, Zhen, Xu, Xianghan, Won, Choongjae, McCandless, Gregory T., Murai, Naoki, Ohira-Kawamura, Seiko, Moxim, Stephen J., Ryan, Jason T., Huang, Xiaozhou, Wang, Xiaoping, Chan, Julia Y., Cheong, Sang-Wook, Tchernyshyov, Oleg, Balents, Leon, Dai, Pengcheng
In magnetically ordered insulators, elementary quasiparticles manifest as spin waves - collective motions of localized magnetic moments propagating through the lattice - observed via inelastic neutron scattering. In effective spin-1/2 systems where g
Externí odkaz:
http://arxiv.org/abs/2408.15957
The model by Hu and Cai [Phys. Rev. Lett., Vol. 111(13) (2013)1 ] describes the self-organization of vascular networks for transport of fluids from source to sinks. Diameters, and thereby conductances, of vessel segments evolve so as to minimize a co
Externí odkaz:
http://arxiv.org/abs/2407.04120
Autor:
Debenedetti, Edoardo, Rando, Javier, Paleka, Daniel, Florin, Silaghi Fineas, Albastroiu, Dragos, Cohen, Niv, Lemberg, Yuval, Ghosh, Reshmi, Wen, Rui, Salem, Ahmed, Cherubin, Giovanni, Zanella-Beguelin, Santiago, Schmid, Robin, Klemm, Victor, Miki, Takahiro, Li, Chenhao, Kraft, Stefan, Fritz, Mario, Tramèr, Florian, Abdelnabi, Sahar, Schönherr, Lea
Large language model systems face important security risks from maliciously crafted messages that aim to overwrite the system's original instructions or leak private data. To study this problem, we organized a capture-the-flag competition at IEEE SaT
Externí odkaz:
http://arxiv.org/abs/2406.07954
The OpenMP API offers both task-based and data-parallel concepts to scientific computing. While it provides descriptive and prescriptive annotations, it is in many places deliberately unspecific how to implement its annotations. As the predominant Op
Externí odkaz:
http://arxiv.org/abs/2406.03077
Autor:
Klemm, Ryan, Chen, Bo
The use of steganography to transmit secret data is becoming increasingly common in security products and malware today. Despite being extremely popular, PDF files are not often the focus of steganography research, as most applications utilize digita
Externí odkaz:
http://arxiv.org/abs/2405.00865
The emergence of differentiable simulators enabling analytic gradient computation has motivated a new wave of learning algorithms that hold the potential to significantly increase sample efficiency over traditional Reinforcement Learning (RL) methods
Externí odkaz:
http://arxiv.org/abs/2404.02887