Zobrazeno 1 - 10
of 3 365
pro vyhledávání: '"P, Ranganath"'
Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering capabilities. However, these models are sometimes prone to generating credible-sounding but incorrect inf
Externí odkaz:
http://arxiv.org/abs/2412.02904
The oxidation of human sebum, a lipid mixture covering our skin, generates a range of volatile and semi-volatile carbonyl compounds that contribute largely to indoor air pollution in crowded environments. Kinetic models have been developed to gain a
Externí odkaz:
http://arxiv.org/abs/2412.00436
Publikováno v:
38th Conference on Neural Information Processing Systems (NeurIPS 2024)
Feature attributions attempt to highlight what inputs drive predictive power. Good attributions or explanations are thus those that produce inputs that retain this predictive power; accordingly, evaluations of explanations score their quality of pred
Externí odkaz:
http://arxiv.org/abs/2411.02664
Contrastive learning methods, such as CLIP, leverage naturally paired data-for example, images and their corresponding text captions-to learn general representations that transfer efficiently to downstream tasks. While such approaches are generally a
Externí odkaz:
http://arxiv.org/abs/2411.01053
Autor:
Barty, Christopher P. J., Algots, J. Martin, Amador, Alexander J., Barty, James C. R., Betts, Shawn M., Casteñada, Marcelo A., Chu, Matthew M., Daley, Michael E., Lopez, Ricardo A. De Luna, Diviak, Derek A., Effarah, Haytham H., Feliciano, Roberto, Garcia, Adan, Grabiel, Keith J., Griffin, Alex S., Hartemann, Frederic V., Heid, Leslie, Hwang, Yoonwoo, Imeshev, Gennady, Jentschel, Michael, Johnson, Christopher A., Kinosian, Kenneth W., Lagzda, Agnese, Lochrie, Russell J., May, Michael W., Molina, Everardo, Nagel, Christopher L., Nagel, Henry J., Peirce, Kyle R., Peirce, Zachary R., Quiñonez, Mauricio E., Raksi, Ferenc, Ranganath, Kelanu, Reutershan, Trevor, Salazar, Jimmie, Schneider, Mitchell E., Seggebruch, Michael W. L., Yang, Joy Y., Yeung, Nathan H., Zapata, Collette B., Zapata, Luis E., Zepeda, Eric J., Zhang, Jingyuan
The design and optimization of laser-Compton x-ray systems based on compact distributed charge accelerator structures can enable micron-scale imaging of disease and the concomitant production of beams of Very High Energy Electrons (VHEEs) capable of
Externí odkaz:
http://arxiv.org/abs/2408.04082
Reversing a diffusion process by learning its score forms the heart of diffusion-based generative modeling and for estimating properties of scientific systems. The diffusion processes that are tractable center on linear processes with a Gaussian stat
Externí odkaz:
http://arxiv.org/abs/2407.07998
Generative models of language exhibit impressive capabilities but still place non-negligible probability mass over undesirable outputs. In this work, we address the task of updating a model to avoid unwanted outputs while minimally changing model beh
Externí odkaz:
http://arxiv.org/abs/2406.13660
Foundational vision transformer models have shown impressive few shot performance on many vision tasks. This research presents a novel investigation into the application of parameter efficient fine-tuning methods within an active learning (AL) framew
Externí odkaz:
http://arxiv.org/abs/2406.09296
Autor:
Yen, Chen-Yu, Singhal, Raghav, Sharma, Umang, Ranganath, Rajesh, Chopra, Sumit, Pinto, Lerrel
Magnetic Resonance (MR) imaging, despite its proven diagnostic utility, remains an inaccessible imaging modality for disease surveillance at the population level. A major factor rendering MR inaccessible is lengthy scan times. An MR scanner collects
Externí odkaz:
http://arxiv.org/abs/2406.04318
Autor:
Chen, Angelica, Malladi, Sadhika, Zhang, Lily H., Chen, Xinyi, Zhang, Qiuyi, Ranganath, Rajesh, Cho, Kyunghyun
Preference learning algorithms (e.g., RLHF and DPO) are frequently used to steer LLMs to produce generations that are more preferred by humans, but our understanding of their inner workings is still limited. In this work, we study the conventional wi
Externí odkaz:
http://arxiv.org/abs/2405.19534