2 code implementations • 10 Apr 2024 • Neel Mishra, Bamdev Mishra, Pratik Jawanpuria, Pawan Kumar
It modifies the Gauss-Newton method to approximate the min-max Hessian and uses the Sherman-Morrison inversion formula to calculate the inverse.
no code implementations • 15 Oct 2023 • Prasha Srivastava, Pawan Kumar, Zia Abbas
Generative AI has seen remarkable growth over the past few years, with diffusion models being state-of-the-art for image generation.
no code implementations • 15 Oct 2023 • Arpan Dasgupta, Pawan Kumar
A common problem faced during this decomposition is that even though the given matrix may be very sparse, the decomposition may lead to a denser triangular factors due to fill-in.
no code implementations • 21 Aug 2023 • Sajal Khandelwal, Pawan Kumar, Syed Azeemuddin
Radio frequency (RF) biosensors, in particular those based on inter-digitated capacitors (IDCs), are pivotal in areas like biomedical diagnosis, remote sensing, and wireless communication.
no code implementations • 8 Aug 2023 • Ji Wei Yoon, Adithya Kumar, Pawan Kumar, Kedar Hippalgaonkar, J Senthilnath, Vijila Chellappan
For the subset of highly conductive samples, we employed a regression model to predict their conductivities, yielding an impressive test R2 value of 0. 984.
no code implementations • 13 May 2023 • Jayadev Naram, Tanmay Kumar Sinha, Pawan Kumar
We consider the problem of learning low-rank tensors from partial observations with structural constraints, and propose a novel factorization of such tensors, which leads to a simpler optimization problem.
no code implementations • 13 May 2023 • Tanmay Kumar Sinha, Jayadev Naram, Pawan Kumar
Recent approaches to the tensor completion problem have often overlooked the nonnegative structure of the data.
1 code implementation • 20 Apr 2023 • Istasis Mishra, Arpan Dasgupta, Pratik Jawanpuria, Bamdev Mishra, Pawan Kumar
Extreme multi-label (XML) classification refers to the task of supervised multi-label learning that involves a large number of labels.
1 code implementation • 20 Apr 2023 • Neel Mishra, Pawan Kumar
In our work, we propose a novel yet simple approach to obtain an adaptive learning rate for gradient-based descent methods on classification tasks.
no code implementations • 20 Apr 2023 • Sachin Kumar Danisetty, Santhosh Reddy Mylaram, Pawan Kumar
The proposed method is fastest to obtain similar accuracy when compared to prominent second order methods.
1 code implementation • 24 Mar 2023 • Kinal Mehta, Anuj Mahajan, Pawan Kumar
We present marl-jax, a multi-agent reinforcement learning software package for training and evaluating social generalization of the agents.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 15 Feb 2023 • Jérôme Kunegis, Pawan Kumar, Jun Sun, Anna Samoilenko, Giuseppe Pirró
In this paper we take the problem of visualising large graphs from a novel perspective: we leave the original graph's nodes and edges behind, and instead summarise its properties such as the clustering coefficient and bipartivity by generating a completely new graph whose structural properties match that of the original graph.
no code implementations • 15 Feb 2023 • Prasha Srivastava, Pawan Kumar, Zia Abbas
Various studies have shown the advantages of using Machine Learning (ML) techniques for analog and digital IC design automation and optimization.
no code implementations • 12 Feb 2023 • Arpan Dasgupta, Siddhant Katyan, Shrutimoy Das, Pawan Kumar
Compared to traditional multilabel classification, here the number of labels is extremely large, hence, the name extreme multilabel classification.
1 code implementation • 10 Dec 2022 • Kinal Mehta, Anuj Mahajan, Pawan Kumar
A reliable critic is central to on-policy actor-critic learning.
no code implementations • 25 Apr 2022 • Andi Han, Bamdev Mishra, Pratik Jawanpuria, Pawan Kumar, Junbin Gao
In this paper, we study min-max optimization problems on Riemannian manifolds.
1 code implementation • 30 Sep 2021 • Neeraj Kollepara, Snehith Kumar Chatakonda, Pawan Kumar
In this work, we announce a comprehensive well curated and opensource dataset with millions of samples for pre-college and college level problems in mathematicsand science.
no code implementations • 30 Sep 2021 • Jayadev Naram, Tanmay Kumar Sinha, Pawan Kumar
We propose a novel Riemannian method for solving the Extreme multi-label classification problem that exploits the geometric structure of the sparse low-dimensional local embedding models.
no code implementations • 29 Jun 2021 • Pawan Kumar, Christina Surulescu, Anna Zhigun
We propose a multiphase modeling approach to describe glioma pseudopalisade patterning under the influence of acidosis.
no code implementations • 28 Jan 2021 • Kiyoung Jo, Pawan Kumar, Joseph Orr, Surendra B. Anantharaman, Jinshui Miao, Michael Motala, Arkamita Bandyopadhyay, Kim Kisslinger, Christopher Muratore, Vivek B. Shenoy, Eric Stach, Nicholas Glavin, Deep Jariwala
To be specific, potential, conductance and photoluminescence at the buried metal/MoS2 interface are correlated as a function of a variety of metal deposition conditions as well as the type of metal contacts.
Mesoscale and Nanoscale Physics Materials Science Applied Physics Optics
no code implementations • 13 Jan 2021 • Pawan Kumar, Melinda Nagy, Alexandre Lemerle, Bidya Binay Karak, Kristof Petrovay
The polar precursor method is widely considered to be the most robust physically motivated method to predict the amplitude of an upcoming solar cycle. It uses indicators of the magnetic field concentrated near the poles around sunspot minimum.
Solar and Stellar Astrophysics Space Physics
no code implementations • 29 Sep 2020 • Pawan Kumar, Srikanta Bedathur
In section 3 we will consider systems that uses formal languages e. g. $\lambda$-calculus (Steedman, 1996), $\lambda$-DCS (Liang, 2013).
no code implementations • 21 Sep 2020 • Pawan Kumar, Christina Surulescu
We propose a model for glioma patterns in a microlocal tumor environment under the influence of acidity, angiogenesis, and tissue anisotropy.
no code implementations • 10 Jul 2020 • Pawan Kumar, Jing Li, Christina Surulescu
Moreover, we study two different types of scaling and compare the behavior of the obtained macroscopic PDEs by way of simulations.
no code implementations • AAAI-2020 2019 • Pawan Kumar, Dhanajit Brahma, Harish Karnick, Piyush Rai
We apply our framework on two tasks: Sentence Ordering and Order Discrimination.
no code implementations • 31 Jan 2018 • Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani
We present a class of algorithms capable of directly training deep neural networks with respect to large families of task-specific performance measures such as the F-measure and the Kullback-Leibler divergence that are structured and non-decomposable.