no code implementations • 26 Mar 2022 • Octavi Obiols-Sales, Abhinav Vishnu, Nicholas Malaya, Aparna Chandramowlishwaran
Due to NUNet's ability to super-resolve only regions of interest, it predicts the same target 1024x1024 spatial resolution 7-28. 5x faster than state-of-the-art DL methods and reduces the memory usage by 4. 4-7. 65x, showcasing improved scalability.
no code implementations • 17 Aug 2021 • Octavi Obiols-Sales, Abhinav Vishnu, Nicholas Malaya, Aparna Chandramowlishwaran
SURFNet primarily trains the DL model on low-resolution datasets and transfer learns the model on a handful of high-resolution flow problems - accelerating the traditional numerical solver independent of the input size.
no code implementations • 9 May 2020 • Octavi Obiols-Sales, Abhinav Vishnu, Nicholas Malaya, Aparna Chandramowlishwaran
CFD is widely used in physical system design and optimization, where it is used to predict engineering quantities of interest, such as the lift on a plane wing or the drag on a motor vehicle.
no code implementations • 13 Aug 2018 • Garrett B. Goh, Khushmeen Sakloth, Charles Siegel, Abhinav Vishnu, Jim Pfaendtner
Deep learning algorithms excel at extracting patterns from raw data, and with large datasets, they have been very successful in computer vision and natural language applications.
1 code implementation • 2 Jul 2018 • Jiankai Sun, Abhinav Vishnu, Aniket Chakrabarti, Charles Siegel, Srinivasan Parthasarathy
Using data from eight stack exchange sites, we are able to improve upon the routing metrics (Precision$@1$, Accuracy, MRR) over the state-of-the-art models such as semantic matching by $159. 5\%$,$31. 84\%$, and $40. 36\%$ for cold questions posted by existing askers, and $123. 1\%$, $27. 03\%$, and $34. 81\%$ for cold questions posted by new askers respectively.
no code implementations • 15 Mar 2018 • Jeff Daily, Abhinav Vishnu, Charles Siegel, Thomas Warfel, Vinay Amatya
In this paper, we present GossipGraD - a gossip communication protocol based Stochastic Gradient Descent (SGD) algorithm for scaling Deep Learning (DL) algorithms on large-scale systems.
1 code implementation • 7 Dec 2017 • Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks.
4 code implementations • 6 Dec 2017 • Garrett B. Goh, Nathan O. Hodas, Charles Siegel, Abhinav Vishnu
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software.
2 code implementations • 5 Oct 2017 • Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas, Nathan Baker
The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representation learning of deep neural networks.
2 code implementations • 20 Jun 2017 • Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas, Nathan Baker
We then show how Chemception can serve as a general-purpose neural network architecture for predicting toxicity, activity, and solvation properties when trained on a modest database of 600 to 40, 000 compounds.
no code implementations • 17 Jan 2017 • Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry.
no code implementations • 3 Oct 2016 • Charles Siegel, Jeff Daily, Abhinav Vishnu
We present novel techniques to accelerate the convergence of Deep Learning algorithms by conducting low overhead removal of redundant neurons -- apoptosis of neurons -- which do not contribute to model learning, during the training phase itself.
no code implementations • 20 Jun 2016 • Vivek Datla, David Lin, Max Louwerse, Abhinav Vishnu
Specifically we develop a modified-ADIOS algorithm based on ADIOS Solan et al. (2005) to learn grammar structures, and use these grammar structures to learn the rules for identifying the semantic roles based on the context in which the grammar structures appeared.
no code implementations • 4 May 2016 • Shuai Zheng, Abhinav Vishnu, Chris Ding
Deep Learning is a very powerful machine learning model.
no code implementations • 7 Mar 2016 • Abhinav Vishnu, Charles Siegel, Jeffrey Daily
Machine Learning and Data Mining (MLDM) algorithms are becoming increasingly important in analyzing large volume of data generated by simulations, experiments and mobile devices.
Distributed, Parallel, and Cluster Computing
no code implementations • 3 Dec 2015 • Vivek Datla, Abhinav Vishnu
The music industry is a $130 billion industry.
no code implementations • 19 Jun 2014 • Jeyanthi Narasimhan, Abhinav Vishnu, Lawrence Holder, Adolfy Hoisie
Under sample elimination, several heuristics for {\em earliest possible} to {\em lazy} elimination of non-contributing samples are proposed.