𝐏𝐚𝐩𝐞𝐫 𝐀𝐜𝐜𝐞𝐩𝐭𝐞𝐝 𝐚𝐭 International Conference on Learning Representations (𝐈𝐂𝐋𝐑) 𝟐𝟎𝟐𝟔!
𝐀𝐛𝐨𝐮𝐭 ICLR
The International Conference on Learning Representations (ICLR) is a top-tier venue in Artificial Intelligence and learning systems. According to Google Scholar Metrics, ICLR ranks 8th overall across all disciplines and publication types (2nd in the AI category), placing it among the most prestigious and highest-impact publications, including Nature, The Lancet, The New England Journal of Medicine, Science, and CVPR. ICLR records a h5-index of 362 and h5-median of 652, reflecting very strong citation impact and wide research influence.
𝐀𝐛𝐨𝐮𝐭 the paper
This work introduces 𝐂𝐎𝐒𝐌𝐎-𝐈𝐍𝐑, a novel complex modulation-based framework to mitigate spectral attenuation, a common phenomenon that degrades INR performance. The work is grounded on a strong theoretical foundation based on Chebyshev polynomial approximations and harmonic distortion analysis.
This includes a thorough experimental analysis showing that COSMO-INR significantly outperforms the state of the art across numerous tasks, including image reconstruction, denoising, inpainting, neural radiance fields (NeRFs), and 3D object reconstruction.
Full paper can be accessed at: https://lnkd.in/gezcqYcw
Why it is so important? 𝐄𝐧𝐭𝐢𝐫𝐞𝐥𝐲 𝐇𝐨𝐦𝐞-𝐆𝐫𝐨𝐰𝐧 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐭 𝐔𝐨𝐏
The achievement is especially meaningful because the research was led entirely by recent graduates and faculty from the Department of Electrical & Electronic Engineering – UOP, through the MARC (Multidisciplinary AI Research Centre – University of Peradeniya) INR team. From idea to implementation and writing, the work was developed locally, showcasing the strength of home-grown talent and mentorship at UoP in producing globally competitive AI research.
Team Members
Pandula Thennakoon, Avishka Ranasinghe, Mario De Silva, Buwaneka Epakanda
Supervisors
Roshan Godaliyadda, Mervyn Parakrama Ekanayake, Vijitha R. Herath
