Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Gudur, Gautham Krishna"'
Autor:
Lingam, Vijay, Tejaswi, Atula, Vavre, Aditya, Shetty, Aneesh, Gudur, Gautham Krishna, Ghosh, Joydeep, Dimakis, Alex, Choi, Eunsol, Bojchevski, Aleksandar, Sanghavi, Sujay
Popular parameter-efficient fine-tuning (PEFT) methods, such as LoRA and its variants, freeze pre-trained model weights \(W\) and inject learnable matrices \(\Delta W\). These \(\Delta W\) matrices are structured for efficient parameterization, often
Externí odkaz:
http://arxiv.org/abs/2405.19597
Calibration can reduce overconfident predictions of deep neural networks, but can calibration also accelerate training? In this paper, we show that it can when used to prioritize some examples for performing subset selection. We study the effect of p
Externí odkaz:
http://arxiv.org/abs/2210.06592
Federated learning is an effective way of extracting insights from different user devices while preserving the privacy of users. However, new classes with completely unseen data distributions can stream across any device in a federated learning setti
Externí odkaz:
http://arxiv.org/abs/2106.10019
In the recent past, psychological stress has been increasingly observed in humans, and early detection is crucial to prevent health risks. Stress detection using on-device deep learning algorithms has been on the rise owing to advancements in pervasi
Externí odkaz:
http://arxiv.org/abs/2012.02702
Various health-care applications such as assisted living, fall detection, etc., require modeling of user behavior through Human Activity Recognition (HAR). Such applications demand characterization of insights from multiple resource-constrained user
Externí odkaz:
http://arxiv.org/abs/2012.02539
Various IoT applications demand resource-constrained machine learning mechanisms for different applications such as pervasive healthcare, activity monitoring, speech recognition, real-time computer vision, etc. This necessitates us to leverage inform
Externí odkaz:
http://arxiv.org/abs/2011.03206
Various health-care applications such as assisted living, fall detection etc., require modeling of user behavior through Human Activity Recognition (HAR). HAR using mobile- and wearable-based deep learning algorithms have been on the rise owing to th
Externí odkaz:
http://arxiv.org/abs/1906.00108