Zobrazeno 1 - 10
of 86 663
pro vyhledávání: '"KRISHNA A P"'
Autor:
Yang, Haizhou, Zhang, Jiyang, Assi, Ismael, Nallamothu, Brahmajee K, Garikipati, Krishna, Figueroa, C. Alberto
Coronary Artery Disease (CAD) and Coronary Microvascular Disease (CMD) can lead to insufficient blood flow to the myocardium, affecting millions of people globally. Coronary angiography, one of the most commonly used imaging modalities, offers valuab
Externí odkaz:
http://arxiv.org/abs/2412.04798
Autor:
Agarwal, Kunika, Naik, Sahil Gopalkrishna, Chakraborty, Ananya, Sen, Samrat, Ghosal, Pratik, Paul, Biswajit, Banik, Manik, Patra, Ram Krishna
The zero-error capacity of a noisy classical channel quantifies its ability to transmit information with absolute certainty, i.e., without any error. Unlike Shannon's standard channel capacity, which remains unaffected by pre-shared correlations, zer
Externí odkaz:
http://arxiv.org/abs/2412.04779
Autor:
Liu, Zhijian, Zhu, Ligeng, Shi, Baifeng, Zhang, Zhuoyang, Lou, Yuming, Yang, Shang, Xi, Haocheng, Cao, Shiyi, Gu, Yuxian, Li, Dacheng, Li, Xiuyu, Fang, Yunhao, Chen, Yukang, Hsieh, Cheng-Yu, Huang, De-An, Cheng, An-Chieh, Nath, Vishwesh, Hu, Jinyi, Liu, Sifei, Krishna, Ranjay, Xu, Daguang, Wang, Xiaolong, Molchanov, Pavlo, Kautz, Jan, Yin, Hongxu, Han, Song, Lu, Yao
Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a family of open VLMs designed to optimize both efficiency and accuracy
Externí odkaz:
http://arxiv.org/abs/2412.04468
Autor:
Mattu, Sandesh Rao, Khan, Imran Ali, Khammammetti, Venkatesh, Dabak, Beyza, Mohammed, Saif Khan, Narayanan, Krishna, Calderbank, Robert
Much of the engineering behind current wireless systems has focused on designing an efficient and high-throughput downlink to support human-centric communication such as video streaming and internet browsing. This paper looks ahead to design of the u
Externí odkaz:
http://arxiv.org/abs/2412.04295
Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding
This paper presents a comprehensive chatbot system designed to handle a wide range of audio-related queries by integrating multiple specialized audio processing models. The proposed system uses an intent classifier, trained on a diverse audio query d
Externí odkaz:
http://arxiv.org/abs/2412.03980
Autor:
Gadhia, Nandini, Smyrnakis, Michalis, Liu, Po-Yu, Blake, Damer, Hay, Melanie, Nguyen, Anh, Richards, Dominic, Xia, Dong, Krishna, Ritesh
Graph-based machine learning methods are useful tools in the identification and prediction of variation in genetic data. In particular, the comprehension of phenotypic effects at the cellular level is an accelerating research area in pharmacogenomics
Externí odkaz:
http://arxiv.org/abs/2412.03744
Autor:
Bigverdi, Mahtab, Luo, Zelun, Hsieh, Cheng-Yu, Shen, Ethan, Chen, Dongping, Shapiro, Linda G., Krishna, Ranjay
Multimodal language models (MLMs) still face challenges in fundamental visual perception tasks where specialized models excel. Tasks requiring reasoning about 3D structures benefit from depth estimation, and reasoning about 2D object instances benefi
Externí odkaz:
http://arxiv.org/abs/2412.03548
Autor:
Mackraz, Natalie, Sivakumar, Nivedha, Khorshidi, Samira, Patel, Krishna, Theobald, Barry-John, Zappella, Luca, Apostoloff, Nicholas
Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated the bias transfer hypothesis (BTH) by studying the effect of the fine-tuni
Externí odkaz:
http://arxiv.org/abs/2412.03537
Autor:
Wang, Hao, Zhu, Wenhui, Dong, Xuanzhao, Chen, Yanxi, Li, Xin, Qiu, Peijie, Chen, Xiwen, Vasa, Vamsi Krishna, Xiong, Yujian, Dumitrascu, Oana M., Razi, Abolfazl, Wang, Yalin
In this work, we propose Many-MobileNet, an efficient model fusion strategy for retinal disease classification using lightweight CNN architecture. Our method addresses key challenges such as overfitting and limited dataset variability by training mul
Externí odkaz:
http://arxiv.org/abs/2412.02825
Autor:
Sivaramakrishnan, Vignesh, Kalagarla, Krishna C., Devonport, Rosalyn, Pilipovsky, Joshua, Tsiotras, Panagiotis, Oishi, Meeko
We present a neural network verification toolbox to 1) assess the probability of satisfaction of a constraint, and 2) synthesize a set expansion factor to achieve the probability of satisfaction. Specifically, the tool box establishes with a user-spe
Externí odkaz:
http://arxiv.org/abs/2412.02940