Zobrazeno 1 - 10
of 11 499
pro vyhledávání: '"A A Bau"'
Publikováno v:
J. Phys. Soc. Jpn. 92, 064401 (2023)
Based on the quantum kinetic equation for electrons, we theoretically study the quantum multi-photon non-linear absorption of a strong electromagnetic wave (EMW) in two-dimensional graphene. Two cases of the electron scattering mechanism are consider
Externí odkaz:
http://arxiv.org/abs/2412.15638
Autor:
Huong, Nguyen Thu, Bau, Nguyen Quang, Ba, Cao Thi Vi, Dung, Bui Thi, Toan, Nguyen Cong, Tran, Anh-Tuan
Magnetoresistance oscillations in semiconductor quantum wells, with the semi-parabolic plus semi-inverse squared potential, under the influence of intense electromagnetic waves (IEMW), is studied theoretically. Analytical expression for the longitudi
Externí odkaz:
http://arxiv.org/abs/2412.15630
Autor:
Cooper, A. Feder, Choquette-Choo, Christopher A., Bogen, Miranda, Jagielski, Matthew, Filippova, Katja, Liu, Ken Ziyu, Chouldechova, Alexandra, Hayes, Jamie, Huang, Yangsibo, Mireshghallah, Niloofar, Shumailov, Ilia, Triantafillou, Eleni, Kairouz, Peter, Mitchell, Nicole, Liang, Percy, Ho, Daniel E., Choi, Yejin, Koyejo, Sanmi, Delgado, Fernando, Grimmelmann, James, Shmatikov, Vitaly, De Sa, Christopher, Barocas, Solon, Cyphert, Amy, Lemley, Mark, boyd, danah, Vaughan, Jennifer Wortman, Brundage, Miles, Bau, David, Neel, Seth, Jacobs, Abigail Z., Terzis, Andreas, Wallach, Hanna, Papernot, Nicolas, Lee, Katherine
We articulate fundamental mismatches between technical methods for machine unlearning in Generative AI, and documented aspirations for broader impact that these methods could have for law and policy. These aspirations are both numerous and varied, mo
Externí odkaz:
http://arxiv.org/abs/2412.06966
We explore the question: "How much prior art knowledge is needed to create art?" To investigate this, we propose a text-to-image generation model trained without access to art-related content. We then introduce a simple yet effective method to learn
Externí odkaz:
http://arxiv.org/abs/2412.00176
Autor:
Tognazzi, Andrea, Franceschini, Paolo, Biechteler, Jonas, Baù, Enrico, Cino, Alfonso Carmelo, Tittl, Andreas, De Angelis, Costantino, Sortino, Luca
Layered van der Waals (vdW) materials have emerged as a promising platform for nanophotonics due to large refractive indexes and giant optical anisotropy. Unlike conventional dielectrics and semiconductors, the absence of covalent bonds between layer
Externí odkaz:
http://arxiv.org/abs/2411.06156
The increasing congestion in the near-Earth space environment has amplified the need for robust and efficient conjunction analysis techniques including the computation of the minimum distance between orbital paths in the presence of perturbations. Af
Externí odkaz:
http://arxiv.org/abs/2410.20928
Concept erasure in language models has traditionally lacked a comprehensive evaluation framework, leading to incomplete assessments of effectiveness of erasure methods. We propose an evaluation paradigm centered on three critical criteria: innocence
Externí odkaz:
http://arxiv.org/abs/2410.02760
Autor:
Mueller, Aaron, Brinkmann, Jannik, Li, Millicent, Marks, Samuel, Pal, Koyena, Prakash, Nikhil, Rager, Can, Sankaranarayanan, Aruna, Sharma, Arnab Sen, Sun, Jiuding, Todd, Eric, Bau, David, Belinkov, Yonatan
Interpretability provides a toolset for understanding how and why neural networks behave in certain ways. However, there is little unity in the field: most studies employ ad-hoc evaluations and do not share theoretical foundations, making it difficul
Externí odkaz:
http://arxiv.org/abs/2408.01416
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
Autor:
Karvonen, Adam, Wright, Benjamin, Rager, Can, Angell, Rico, Brinkmann, Jannik, Smith, Logan, Verdun, Claudio Mayrink, Bau, David, Marks, Samuel
What latent features are encoded in language model (LM) representations? Recent work on training sparse autoencoders (SAEs) to disentangle interpretable features in LM representations has shown significant promise. However, evaluating the quality of
Externí odkaz:
http://arxiv.org/abs/2408.00113
Autor:
Fiotto-Kaufman, Jaden, Loftus, Alexander R., Todd, Eric, Brinkmann, Jannik, Pal, Koyena, Troitskii, Dmitrii, Ripa, Michael, Belfki, Adam, Rager, Can, Juang, Caden, Mueller, Aaron, Marks, Samuel, Sharma, Arnab Sen, Lucchetti, Francesca, Prakash, Nikhil, Brodley, Carla, Guha, Arjun, Bell, Jonathan, Wallace, Byron C., Bau, David
We introduce NNsight and NDIF, technologies that work in tandem to enable scientific study of very large neural networks. NNsight is an open-source system that extends PyTorch to introduce deferred remote execution. NDIF is a scalable inference servi
Externí odkaz:
http://arxiv.org/abs/2407.14561