Zobrazeno 1 - 10
of 27 175
pro vyhledávání: '"Madhavan, A. A."'
We introduce Simultaneous Weighted Preference Optimization (SWEPO), a novel extension of Direct Preference Optimization (DPO) designed to accommodate multiple dynamically chosen positive and negative responses for each query. SWEPO employs a weighted
Externí odkaz:
http://arxiv.org/abs/2412.04628
Autor:
Varun, Yerram, Madhavan, Rahul, Addepalli, Sravanti, Suggala, Arun, Shanmugam, Karthikeyan, Jain, Prateek
Publikováno v:
Varun, Y., Madhavan, R., Addepalli, S., Suggala, A., Shanmugam, K., & Jain, P. Time-Reversal Provides Unsupervised Feedback to LLMs. In The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
Large Language Models (LLMs) are typically trained to predict in the forward direction of time. However, recent works have shown that prompting these models to look back and critique their own generations can produce useful feedback. Motivated by thi
Externí odkaz:
http://arxiv.org/abs/2412.02626
We show that ultrafast optical pump pulses provide effective control over charge orders in the kagome metals $A$V$_3$Sb$_5$ with $A=$ K, Rb, and Cs. Starting from the real charge density waves (rCDWs) at the $p$-type Van Hove singularity, we conduct
Externí odkaz:
http://arxiv.org/abs/2411.10447
Autor:
Chai, Wenhao, Song, Enxin, Du, Yilun, Meng, Chenlin, Madhavan, Vashisht, Bar-Tal, Omer, Hwang, Jeng-Neng, Xie, Saining, Manning, Christopher D.
Video detailed captioning is a key task which aims to generate comprehensive and coherent textual descriptions of video content, benefiting both video understanding and generation. In this paper, we propose AuroraCap, a video captioner based on a lar
Externí odkaz:
http://arxiv.org/abs/2410.03051
Tactile sensing is a powerful means of implicit communication between a human and a robot assistant. In this paper, we investigate how tactile sensing can transcend cross-embodiment differences across robotic systems in the context of collaborative m
Externí odkaz:
http://arxiv.org/abs/2409.14896
Autor:
Verma, Apurv, Krishna, Satyapriya, Gehrmann, Sebastian, Seshadri, Madhavan, Pradhan, Anu, Ault, Tom, Barrett, Leslie, Rabinowitz, David, Doucette, John, Phan, NhatHai
Creating secure and resilient applications with large language models (LLM) requires anticipating, adjusting to, and countering unforeseen threats. Red-teaming has emerged as a critical technique for identifying vulnerabilities in real-world LLM impl
Externí odkaz:
http://arxiv.org/abs/2407.14937
In this work, we describe a novel approach to building a neural PDE solver leveraging recent advances in transformer based neural network architectures. Our model can provide solutions for different values of PDE parameters without any need for retra
Externí odkaz:
http://arxiv.org/abs/2407.06209
Autor:
Kengle, Caitlin S., Chaudhuri, Dipanjan, Guo, Xuefei, Johnson, Thomas A., Bettler, Simon, Simeth, Wolfgang, Krogstad, Matthew J., Islam, Zahir, Ran, Sheng, Saha, Shanta R., Paglione, Johnpierre, Butch, Nicholas P., Fradkin, Eduardo, Madhavan, Vidya, Abbamonte, Peter
Publikováno v:
Phys. Rev. B 110, 145101 (2024)
The long-sought pair density wave (PDW) is an exotic phase of matter in which charge density wave (CDW) order is intertwined with the amplitude or phase of coexisting, superconducting order \cite{Berg2009,Berg2009b}. Originally predicted to exist in
Externí odkaz:
http://arxiv.org/abs/2406.14688
Autor:
Varadarajan, Madhavan
We consider spherically symmetric gravity coupled to a spherically symmetric scalar field with a specific coupling which depends on the Areal Radius. Classical collapse is described by the Vaidya solution. The semiclassical Einstein equations are wel
Externí odkaz:
http://arxiv.org/abs/2406.09176
Autor:
Mukherjee, Mandovi, Mao, Xiangyu, Rahman, Nael, DeLude, Coleman, Driscoll, Joe, Sharma, Sudarshan, Behnam, Payman, Kamal, Uday, Woo, Jongseok, Kim, Daehyun, Khan, Sharjeel, Tong, Jianming, Seo, Jamin, Sinha, Prachi, Swaminathan, Madhavan, Krishna, Tushar, Pande, Santosh, Romberg, Justin, Mukhopadhyay, Saibal
A near memory hardware accelerator, based on a novel direct path computational model, for real-time emulation of radio frequency systems is demonstrated. Our evaluation of hardware performance uses both application-specific integrated circuits (ASIC)
Externí odkaz:
http://arxiv.org/abs/2406.08714