Zobrazeno 1 - 10
of 30 269
pro vyhledávání: '"P. VIKAS"'
We explore the consequences of varying the pump beam waist that illuminates a nonlinear crystal, realizing spontaneous parametric down-conversion (SPDC). The coherence is transferred from the marginal one-photon wavefunction to the two-photon wavefun
Externí odkaz:
http://arxiv.org/abs/2411.03904
This paper introduces a novel model compression approach through dynamic layer-specific pruning in Large Language Models (LLMs), enhancing the traditional methodology established by SliceGPT. By transitioning from constant to dynamic slicing, our met
Externí odkaz:
http://arxiv.org/abs/2411.03513
Autor:
Nguyen, Hoang, Mahajan, Khyati, Yadav, Vikas, Yu, Philip S., Hashemi, Masoud, Maheshwary, Rishabh
Multilingual LLMs have achieved remarkable benchmark performance, but we find they continue to underperform on non-Latin script languages across contemporary LLM families. This discrepancy arises from the fact that LLMs are pretrained with orthograph
Externí odkaz:
http://arxiv.org/abs/2411.02398
We investigate what kind of images lie in the high-density regions of diffusion models. We introduce a theoretical mode-tracking process capable of pinpointing the exact mode of the denoising distribution, and we propose a practical high-probability
Externí odkaz:
http://arxiv.org/abs/2411.01293
Autor:
Konale, Aditya, Srivastava, Vikas
Soft polymers are ubiquitous materials found in nature and as engineering materials with properties varying from rate-independent to significantly rate-dependent depending on the crosslinking mechanisms. Current fracture toughness measures such as en
Externí odkaz:
http://arxiv.org/abs/2411.00231
We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or trunk diffusion process is associated with a primary variable (e.g.,
Externí odkaz:
http://arxiv.org/abs/2410.24012
Autor:
Nateghi, Ramin, Zhou, Ruoji, Saft, Madeline, Schnauss, Marina, Neill, Clayton, Alam, Ridwan, Handa, Nicole, Huang, Mitchell, Li, Eric V, Goldstein, Jeffery A, Schaeffer, Edward M, Nadim, Menatalla, Pourakpour, Fattaneh, Isaila, Bogdan, Felicelli, Christopher, Mehta, Vikas, Nezami, Behtash G, Ross, Ashley, Yang, Ximing, Cooper, Lee AD
Artificial intelligence may assist healthcare systems in meeting increasing demand for pathology services while maintaining diagnostic quality and reducing turnaround time and costs. We aimed to investigate the performance of an institutionally devel
Externí odkaz:
http://arxiv.org/abs/2410.23642
Optically trapped atoms in arrays of optical tweezers have emerged as a powerful platform for quantum information processing given the recent demonstrations of high-fidelity quantum logic gates and on-demand reconfigurable geometry. Both in gate oper
Externí odkaz:
http://arxiv.org/abs/2410.23430
Autor:
Shen, Xiaoqian, Xiong, Yunyang, Zhao, Changsheng, Wu, Lemeng, Chen, Jun, Zhu, Chenchen, Liu, Zechun, Xiao, Fanyi, Varadarajan, Balakrishnan, Bordes, Florian, Liu, Zhuang, Xu, Hu, Kim, Hyunwoo J., Soran, Bilge, Krishnamoorthi, Raghuraman, Elhoseiny, Mohamed, Chandra, Vikas
Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge constrained by LLM's context size. To address this limitation, we pro
Externí odkaz:
http://arxiv.org/abs/2410.17434
Autor:
Brokman, Jonathan, Hofman, Omer, Rachmil, Oren, Singh, Inderjeet, Priya, Rathina Sabapathy Aishvariya, Pahuja, Vikas, Giloni, Amit, Vainshtein, Roman, Kojima, Hisashi
This report presents a comparative analysis of open-source vulnerability scanners for conversational large language models (LLMs). As LLMs become integral to various applications, they also present potential attack surfaces, exposed to security risks
Externí odkaz:
http://arxiv.org/abs/2410.16527