Zobrazeno 1 - 10
of 1 958
pro vyhledávání: '"A Kadambi"'
Autor:
Solomon, Jim, Jalilian, Laleh, Vilesov, Alexander, Mathew, Meryl, Grogan, Tristan, Bedayat, Arash, Kadambi, Achuta
Human-machine teaming in medical AI requires us to understand to what degree a trained clinician should weigh AI predictions. While previous work has shown the potential of AI assistance at improving clinical predictions, existing clinical decision s
Externí odkaz:
http://arxiv.org/abs/2412.00372
Autor:
Fan, Zhiwen, Zhang, Jian, Cong, Wenyan, Wang, Peihao, Li, Renjie, Wen, Kairun, Zhou, Shijie, Kadambi, Achuta, Wang, Zhangyang, Xu, Danfei, Ivanovic, Boris, Pavone, Marco, Wang, Yue
Reconstructing and understanding 3D structures from a limited number of images is a well-established problem in computer vision. Traditional methods usually break this task into multiple subtasks, each requiring complex transformations between differ
Externí odkaz:
http://arxiv.org/abs/2410.18956
Autor:
Bhardwaj, Kartikeya, Pandey, Nilesh Prasad, Priyadarshi, Sweta, Ganapathy, Viswanath, Esteves, Rafael, Kadambi, Shreya, Borse, Shubhankar, Whatmough, Paul, Garrepalli, Risheek, Van Baalen, Mart, Teague, Harris, Nagel, Markus
In this paper, we propose Sparse High Rank Adapters (SHiRA) that directly finetune 1-2% of the base model weights while leaving others unchanged, thus, resulting in a highly sparse adapter. This high sparsity incurs no inference overhead, enables rap
Externí odkaz:
http://arxiv.org/abs/2407.16712
Autor:
Del Regno, Kai, Vilesov, Alexander, Armouti, Adnan, Harish, Anirudh Bindiganavale, Can, Selim Emir, Kita, Ashley, Kadambi, Achuta
Polysomnography (PSG), the current gold standard method for monitoring and detecting sleep disorders, is cumbersome and costly. At-home testing solutions, known as home sleep apnea testing (HSAT), exist. However, they are contact-based, a feature whi
Externí odkaz:
http://arxiv.org/abs/2407.11936
The exponential progress in generative AI poses serious implications for the credibility of all real images and videos. There will exist a point in the future where 1) digital content produced by generative AI will be indistinguishable from those cre
Externí odkaz:
http://arxiv.org/abs/2407.04169
Generative artificial intelligence (GenAI) has the potential to improve healthcare through automation that enhances the quality and safety of patient care. Powered by foundation models that have been pretrained and can generate complex content, GenAI
Externí odkaz:
http://arxiv.org/abs/2407.16902
Autor:
Li, Renjie, Pan, Panwang, Yang, Bangbang, Xu, Dejia, Zhou, Shijie, Zhang, Xuanyang, Li, Zeming, Kadambi, Achuta, Wang, Zhangyang, Tu, Zhengzhong, Fan, Zhiwen
The blooming of virtual reality and augmented reality (VR/AR) technologies has driven an increasing demand for the creation of high-quality, immersive, and dynamic environments. However, existing generative techniques either focus solely on dynamic o
Externí odkaz:
http://arxiv.org/abs/2406.13527
Autor:
Bhardwaj, Kartikeya, Pandey, Nilesh Prasad, Priyadarshi, Sweta, Ganapathy, Viswanath, Esteves, Rafael, Kadambi, Shreya, Borse, Shubhankar, Whatmough, Paul, Garrepalli, Risheek, Van Baalen, Mart, Teague, Harris, Nagel, Markus
Low Rank Adaptation (LoRA) has gained massive attention in the recent generative AI research. One of the main advantages of LoRA is its ability to be fused with pretrained models adding no overhead during inference. However, from a mobile deployment
Externí odkaz:
http://arxiv.org/abs/2406.13175
Autor:
Borse, Shubhankar, Kadambi, Shreya, Pandey, Nilesh Prasad, Bhardwaj, Kartikeya, Ganapathy, Viswanath, Priyadarshi, Sweta, Garrepalli, Risheek, Esteves, Rafael, Hayat, Munawar, Porikli, Fatih
While Low-Rank Adaptation (LoRA) has proven beneficial for efficiently fine-tuning large models, LoRA fine-tuned text-to-image diffusion models lack diversity in the generated images, as the model tends to copy data from the observed training samples
Externí odkaz:
http://arxiv.org/abs/2406.08798
Autor:
Ezhov, Vadim, Park, Hyoungseob, Zhang, Zhaoyang, Upadhyay, Rishi, Zhang, Howard, Chandrappa, Chethan Chinder, Kadambi, Achuta, Ba, Yunhao, Dorsey, Julie, Wong, Alex
We propose a method for depth estimation under different illumination conditions, i.e., day and night time. As photometry is uninformative in regions under low-illumination, we tackle the problem through a multi-sensor fusion approach, where we take
Externí odkaz:
http://arxiv.org/abs/2405.17315