no code implementations • 25 Sep 2024 • Akash Agrawal, Mayesh Mohapatra, Abhinav Raja, Paritosh Tiwari, Vishwajeet Pattanaik, Neeru Jaiswal, Arpit Agarwal, Punit Rathore
The current process for cyclone detection and intensity estimation involves physics-based simulation studies which are time-consuming, only using image features will automate the process for significantly faster and more accurate predictions.
1 code implementation • 24 Oct 2023 • Alokendu Mazumder, Tirthajit Baruah, Bhartendu Kumar, Rishab Sharma, Vishwajeet Pattanaik, Punit Rathore
In LoRAE, we incorporated a low-rank regularizer to adaptively reconstruct a low-dimensional latent space while preserving the basic objective of an autoencoder.
no code implementations • 15 Sep 2023 • Alokendu Mazumder, Rishabh Sabharwal, Manan Tayal, Bhartendu Kumar, Punit Rathore
Lastly, (iii) we also demonstrate that our derived constant step size has better abilities in reducing the gradient norms, and empirically, we show that despite the accumulation of a few past gradients, the key driver for convergence in Adam is the non-increasing step sizes.
no code implementations • 29 May 2023 • Alokendu Mazumder, Tirthajit Baruah, Akash Kumar Singh, Pagadla Krishna Murthy, Vishwajeet Pattanaik, Punit Rathore
Estimating the number of clusters and cluster structures in unlabeled, complex, and high-dimensional datasets (like images) is challenging for traditional clustering algorithms.
no code implementations • 9 Sep 2020 • Punit Rathore, Ali Zonoozi, Omid Geramifard, Tan Kian Lee
In this paper, we analyze drivers' and passengers' locations at the time of booking request in the context of drivers' pick-up performances.
no code implementations • 21 Aug 2020 • Punit Rathore, James C. Bezdek, Paolo Santi, Carlo Ratti
We demonstrate ConiVAT approach to visual assessment and single linkage clustering on nine datasets to show that, it improves the quality of iVAT images for complex datasets, and it also overcomes the limitation of SL clustering with VAT/iVAT due to "noisy" bridges between clusters.
no code implementations • 10 Jun 2018 • Punit Rathore, Dheeraj Kumar, Sutharshan Rajasegarar, Marimuthu Palaniswami, James C. Bezdek
To address these limitations, we propose a scalable clustering and Markov chain based hybrid framework, called Traj-clusiVAT-based TP, for both short-term and long-term trajectory prediction, which can handle a large number of overlapping trajectories in a dense road network.