Zobrazeno 1 - 10
of 254
pro vyhledávání: '"Ranganath, Rajesh"'
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
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
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
Autor:
Manduchi, Laura, Pandey, Kushagra, Bamler, Robert, Cotterell, Ryan, Däubener, Sina, Fellenz, Sophie, Fischer, Asja, Gärtner, Thomas, Kirchler, Matthias, Kloft, Marius, Li, Yingzhen, Lippert, Christoph, de Melo, Gerard, Nalisnick, Eric, Ommer, Björn, Ranganath, Rajesh, Rudolph, Maja, Ullrich, Karen, Broeck, Guy Van den, Vogt, Julia E, Wang, Yixin, Wenzel, Florian, Wood, Frank, Mandt, Stephan, Fortuin, Vincent
The field of deep generative modeling has grown rapidly and consistently over the years. With the availability of massive amounts of training data coupled with advances in scalable unsupervised learning paradigms, recent large-scale generative models
Externí odkaz:
http://arxiv.org/abs/2403.00025
Autor:
van Amsterdam, Wouter A. C., van Geloven, Nan, Krijthe, Jesse H., Ranganath, Rajesh, Ciná, Giovanni
Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisions, and are often hailed as the poster children for personalized, data
Externí odkaz:
http://arxiv.org/abs/2312.01210
Autor:
Yu, Boyang, Kaku, Aakash, Liu, Kangning, Parnandi, Avinash, Fokas, Emily, Venkatesan, Anita, Pandit, Natasha, Ranganath, Rajesh, Schambra, Heidi, Fernandez-Granda, Carlos
Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a novel framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based cha
Externí odkaz:
http://arxiv.org/abs/2311.12781
Autor:
Albergo, Michael S., Goldstein, Mark, Boffi, Nicholas M., Ranganath, Rajesh, Vanden-Eijnden, Eric
Generative models inspired by dynamical transport of measure -- such as flows and diffusions -- construct a continuous-time map between two probability densities. Conventionally, one of these is the target density, only accessible through samples, wh
Externí odkaz:
http://arxiv.org/abs/2310.03725