Zobrazeno 1 - 10
of 55
pro vyhledávání: '"K. Srijith"'
We present a continual learning approach for generative adversarial networks (GANs), by designing and leveraging parameter-efficient feature map transformations. Our approach is based on learning a set of global and task-specific parameters. The glob
Externí odkaz:
http://arxiv.org/abs/2103.04032
The location check-ins of users through various location-based services such as Foursquare, Twitter, and Facebook Places, etc., generate large traces of geo-tagged events. These event-traces often manifest in hidden (possibly overlapping) communities
Externí odkaz:
http://arxiv.org/abs/2006.07580
Publikováno v:
Astrophysics & Space Science; Aug2024, Vol. 369 Issue 8, p1-20, 20p
Publikováno v:
Astrophysics and Space Science. 367
Gamma-Ray Bursts (GRBs) have been traditionally divided into two categories: "short" and "long" with durations less than and greater than two seconds, respectively. However, there is a lot of literature (with conflicting results) regarding the existe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf08c19a1ff04fb2628ffae6b643389c
https://doi.org/10.1007/s10509-022-04068-z
https://doi.org/10.1007/s10509-022-04068-z
Autor:
P. K. Srijith, Srinivas Anumasa
Publikováno v:
WACV
Neural ordinary differential equations (NODE) have been proposed as a continuous depth generalization to popular deep learning models such as Residual networks (ResNets). They provide parameter efficiency and automate the model selection process in d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dcb7637ca939e87cb691420e799b02bf
http://arxiv.org/abs/2112.12707
http://arxiv.org/abs/2112.12707
Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification
Classification of posts in social media such as Twitter is difficult due to the noisy and short nature of texts. Sequence classification models based on recurrent neural networks (RNN) are popular for classifying posts that are sequential in nature.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4762d5bf4c346ed8780a8e06bbc44dfe
http://arxiv.org/abs/2112.12809
http://arxiv.org/abs/2112.12809
Publikováno v:
Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d43f80ba7b655887fd24c1db33607815
https://doi.org/10.1145/3487351.3488341
https://doi.org/10.1145/3487351.3488341
Autor:
P. K. Srijith, Ragja Palakkadavath
Publikováno v:
COMAD/CODS
Generative adversarial networks are one of the most popular approaches to generate new data from complex high-dimensional data distributions. They have revolutionized the area of generative models by creating quality samples that highly resemble the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a09dd65d3ff22ab0b080a44e3399c838
https://doi.org/10.1145/3430984.3431016
https://doi.org/10.1145/3430984.3431016
Publikováno v:
Optical Fiber Sensors Conference 2020 Special Edition.
We experimentally demonstrate the visualization of ultrasonic wave propagation in a metallic plate using a surface-bonded fiber Bragg grating sensor and non-contact excitation of the desired guided mode.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d7ea5c0b8827610b66a1e50919745eb
https://doi.org/10.1364/ofs.2020.w4.32
https://doi.org/10.1364/ofs.2020.w4.32
Autor:
M. Bilal Mohammed, A. Sailesh, K. Srijith, G. Rajeshkumar, S. Arvindh Seshadri, B. Brahatheesh Vikram
Publikováno v:
Eco-Friendly Adhesives for Wood and Natural Fiber Composites ISBN: 9789813347489
In recent years wood has received an ample attention as the reinforcing filler for producing polymeric composites because these wood fillers have various advantages like biodegradable, renewable, less cost, light weight, less abrasive to equipment an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1cbcaab0c0e7b0cad7cf4be1b33cdcb6
https://doi.org/10.1007/978-981-33-4749-6_5
https://doi.org/10.1007/978-981-33-4749-6_5