Zobrazeno 1 - 10
of 140 509
pro vyhledávání: '"PRADEEP, A"'
Autor:
Jain, Gauri, Varakantham, Pradeep, Xu, Haifeng, Taneja, Aparna, Doshi, Prashant, Tambe, Milind
Publikováno v:
PRICAI 2024: Trends in Artificial Intelligence. PRICAI 2024. Lecture Notes in Computer Science(), vol 15285
Public health practitioners often have the goal of monitoring patients and maximizing patients' time spent in "favorable" or healthy states while being constrained to using limited resources. Restless multi-armed bandits (RMAB) are an effective model
Externí odkaz:
http://arxiv.org/abs/2412.08463
Large Language Models (LLMs) generating unsafe responses to toxic prompts is a significant issue in their applications. While various efforts aim to address this safety concern, previous approaches often demand substantial human data collection or re
Externí odkaz:
http://arxiv.org/abs/2412.06843
The promise of unsupervised multi-view-stereo (MVS) is to leverage large unlabeled datasets, yet current methods underperform when training on difficult data, such as handheld smartphone videos of indoor scenes. Meanwhile, high-quality synthetic data
Externí odkaz:
http://arxiv.org/abs/2412.05771
Cryogenic fluids have extensive applications as fuel for launch vehicles in space applications and research. The physics of cryogenic flows are highly complex due to the sensitive nature of phase transformation from liquid to bubbly liquid and vapor,
Externí odkaz:
http://arxiv.org/abs/2412.05471
Autor:
Misra, Shobhna, Pradeep, Reshma Peremadathil, Feng, Yaoxuan, Grob, Urs, Mandru, Andrada Oana, Degen, Christian L., Hug, Hans J., Eichler, Alexander
The separation of physical forces acting on the tip of a magnetic force microscope (MFM) is essential for correct magnetic imaging. Electrostatic forces can be modulated by varying the tip-sample potential and minimized to map the local Kelvin potent
Externí odkaz:
http://arxiv.org/abs/2412.04165
Autor:
Medi, Tejaswini, Rampini, Arianna, Reddy, Pradyumna, Jayaraman, Pradeep Kumar, Keuper, Margret
Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike diffusion models, AR models enable more efficient and controllable gen
Externí odkaz:
http://arxiv.org/abs/2411.19037
Autor:
Lambert, Nathan, Morrison, Jacob, Pyatkin, Valentina, Huang, Shengyi, Ivison, Hamish, Brahman, Faeze, Miranda, Lester James V., Liu, Alisa, Dziri, Nouha, Lyu, Shane, Gu, Yuling, Malik, Saumya, Graf, Victoria, Hwang, Jena D., Yang, Jiangjiang, Bras, Ronan Le, Tafjord, Oyvind, Wilhelm, Chris, Soldaini, Luca, Smith, Noah A., Wang, Yizhong, Dasigi, Pradeep, Hajishirzi, Hannaneh
Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques lag behind proprietary ones. The underlying training data and recipes for
Externí odkaz:
http://arxiv.org/abs/2411.15124
Trilayer nickelates are a rich class of materials exhibiting diverse correlated phenomena, including superconductivity, density wave transitions, non-Fermi liquid behavior along with an unusual metal-to-metal transition around T* ~ 150 K. Understandi
Externí odkaz:
http://arxiv.org/abs/2411.13933
Calculation of the coherent nonlinear response of a system is essential to correctly interpret results from advanced techniques such as two-dimensional coherent spectroscopy (2DCS). Usually, even for the simplest systems, such calculations are either
Externí odkaz:
http://arxiv.org/abs/2411.13290
Autor:
Pradeep, Ronak, Thakur, Nandan, Upadhyay, Shivani, Campos, Daniel, Craswell, Nick, Lin, Jimmy
This report provides an initial look at partial results from the TREC 2024 Retrieval-Augmented Generation (RAG) Track. We have identified RAG evaluation as a barrier to continued progress in information access (and more broadly, natural language proc
Externí odkaz:
http://arxiv.org/abs/2411.09607