no code implementations • 1 Feb 2023 • Abhishek Sharma, Arpit Jain, Shubhangi Sharma, Ashutosh Gupta, Prateek Jain, Saraju P. Mohanty
In this work, multiclass classification is performed on phenotypic data using an SVM model.
no code implementations • 27 Apr 2022 • Zhaoyuan Yang, Arpit Jain
Dropout as regularization has been used extensively to prevent overfitting for training neural networks.
no code implementations • 21 Dec 2020 • Luis Francisco, Tanmay Lagare, Arpit Jain, Somal Chaudhary, Madhura Kulkarni, Divya Sardana, W. Rhett Davis, Paul Franzon
Using this solution, we can detect multiple DRC violations 32x faster than Boolean checkers with an accuracy of up to 92.
2 code implementations • 24 Nov 2019 • Bharathan Balaji, Jordan Bell-Masterson, Enes Bilgin, Andreas Damianou, Pablo Moreno Garcia, Arpit Jain, Runfei Luo, Alvaro Maggiar, Balakrishnan Narayanaswamy, Chun Ye
Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games.
no code implementations • CVPR 2018 • Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser Nam Lim, Rama Chellappa
In this work, we focus on adapting the representations learned by segmentation networks across synthetic and real domains.
no code implementations • ICCV 2017 • Swami Sankaranarayanan, Arpit Jain, Ser Nam Lim
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems.
no code implementations • 22 May 2017 • Swami Sankaranarayanan, Arpit Jain, Rama Chellappa, Ser Nam Lim
In this paper, we present an efficient approach to perform adversarial training by perturbing intermediate layer activations and study the use of such perturbations as a regularizer during training.
no code implementations • 23 Mar 2017 • Swami Sankaranarayanan, Arpit Jain, Ser Nam Lim
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems.
no code implementations • CVPR 2013 • Srikumar Ramalingam, Jaishanker K. Pillai, Arpit Jain, Yuichi Taguchi
In this paper, we consider the problem of detecting junctions and using them for recovering the spatial layout of an indoor scene.
no code implementations • CVPR 2013 • Arpit Jain, Abhinav Gupta, Mikel Rodriguez, Larry S. Davis
representation for videos based on mid-level discriminative spatio-temporal patches.