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Found 17 papers, 5 papers with code
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software.
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
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.
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
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.
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks.
ColdRoute: Effective Routing of Cold Questions in Stack Exchange Sites
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.
GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent
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.
Deep Learning for Computational Chemistry
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry.
Adaptive Neuron Apoptosis for Accelerating Deep Learning on Large Scale Systems
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.
Accelerating Deep Learning with Shrinkage and Recall
Deep Learning is a very powerful machine learning model.
A Data-Driven Approach for Semantic Role Labeling from Induced Grammar Structures in Language
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.
Predicting the top and bottom ranks of billboard songs using Machine Learning
The music industry is a $130 billion industry.
Fast Support Vector Machines Using Parallel Adaptive Shrinking on Distributed Systems
Under sample elimination, several heuristics for {\em earliest possible} to {\em lazy} elimination of non-contributing samples are proposed.
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction
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.
Distributed TensorFlow with MPI
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
CFDNet: a deep learning-based accelerator for fluid simulations
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.
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets
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.
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows
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.
