Abhinav Vishnu
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View article: Process Gas Effects on Deposition Characteristics and Properties of L-Ded In718: Argon, Nitrogen, and Mixed Gas Environments
Process Gas Effects on Deposition Characteristics and Properties of L-Ded In718: Argon, Nitrogen, and Mixed Gas Environments Open
View article: REV-ERBβ Binding Pocket Dynamics with Implications for Rational Design of Small Molecule Modulators
REV-ERBβ Binding Pocket Dynamics with Implications for Rational Design of Small Molecule Modulators Open
REV-ERBβ is a nuclear receptor (NR) with heme as an endogenous ligand that regulates its transcriptional activity. With key role in cellular functions such as glucose metabolism, immune response, and dysregulation in pathologies such as Ty…
View article: Runtime extension for neural network training with heterogeneous memory
Runtime extension for neural network training with heterogeneous memory Open
Systems, apparatuses, and methods for managing buffers in a neural network implementation with heterogeneous memory are disclosed. A system includes a neural network coupled to a first memory and a second memory. The first memory is a rela…
View article: ADARNet: Deep Learning Predicts Adaptive Mesh Refinement
ADARNet: Deep Learning Predicts Adaptive Mesh Refinement Open
View article: NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows Open
Deep Learning (DL) algorithms are becoming increasingly popular for the reconstruction of high-resolution turbulent flows (aka super-resolution). However, current DL approaches perform spatially uniform super-resolution - a key performance…
View article: NWChem: Past, present, and future
NWChem: Past, present, and future Open
© 2020 U.S. Government. Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic…
View article: SURFNet: Super-Resolution of Turbulent Flows with Transfer Learning using Small Datasets
SURFNet: Super-Resolution of Turbulent Flows with Transfer Learning using Small Datasets Open
Deep Learning (DL) algorithms are emerging as a key alternative to computationally expensive CFD simulations. However, state-of-the-art DL approaches require large and high-resolution training data to learn accurate models. The size and av…
View article: Kleio
Kleio Open
The increasing demand of big data analytics for more main memory capacity in datacenters and exascale computing environments is driving the integration of heterogeneous memory technologies. The new technologies exhibit vastly greater diffe…
View article: Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction Open
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. However, in other domains, large datasets on which to learn …
View article: Effective Machine Learning Based Format Selection and Performance Modeling for SpMV on GPUs
Effective Machine Learning Based Format Selection and Performance Modeling for SpMV on GPUs Open
Sparse Matrix-Vector multiplication (SpMV) is a key kernel for many applications in computational science and data analytics. Several efforts have addressed the optimization of SpMV on GPUs, and a number of compact sparse-matrix representa…
View article: Using Rule-Based Labels for Weak Supervised Learning
Using Rule-Based Labels for Weak Supervised Learning Open
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry data is inherently small and fragmented. In this work, we develop an approach of usin…
View article: Improving underwater localization accuracy with machine learning
Improving underwater localization accuracy with machine learning Open
Machine learning classification and regression algorithms were applied to calibrate the localization errors of a time-difference-of-arrival (TDOA)-based acoustic sensor array used for tracking salmon passage through a hydroelectric dam on …
View article: Desh
Desh Open
Today's large-scale supercomputers encounter faults on a daily basis. Exascale systems are likely to experience even higher fault rates due to increased component count and density. Triggering resilience-mitigating techniques remains a cha…
View article: NUMA-Caffe
NUMA-Caffe Open
Convolution Neural Networks (CNNs), a special subcategory of Deep Learning Neural Networks (DNNs), have become increasingly popular in industry and academia for their powerful capability in pattern classification, image processing, and spe…
View article: Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing
Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing Open
View article: Introduction to GraML 2018
Introduction to GraML 2018 Open
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
View article: GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent
GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent Open
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. The salient features of GossipGraD are: 1) reduction…
View article: How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions? Open
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. In the chemistry doma…
View article: Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for\n Transferable Chemical Property Prediction
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for\n Transferable Chemical Property Prediction Open
With access to large datasets, deep neural networks (DNN) have achieved\nhuman-level accuracy in image and speech recognition tasks. However, in\nchemistry, data is inherently small and fragmented. In this work, we develop an\napproach of …
View article: Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction Open
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop an approach of usi…
View article: ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction
ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction Open
View article: SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties Open
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is structural information that can be used to predict complex …
View article: SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for\n Predicting Chemical Properties
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for\n Predicting Chemical Properties Open
Chemical databases store information in text representations, and the SMILES\nformat is a universal standard used in many cheminformatics software. Encoded\nin each SMILES string is structural information that can be used to predict\ncompl…
View article: How Much Chemistry Does a Deep Neural Network Need to Know to Make\n Accurate Predictions?
How Much Chemistry Does a Deep Neural Network Need to Know to Make\n Accurate Predictions? Open
The meteoric rise of deep learning models in computer vision research, having\nachieved human-level accuracy in image recognition tasks is firm evidence of\nthe impact of representation learning of deep neural networks. In the chemistry\nd…
View article: What does fault tolerant Deep Learning need from MPI?
What does fault tolerant Deep Learning need from MPI? Open
Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed DL implementations which use MPI require days of training …
View article: Chemception: A Deep Neural Network with Minimal Chemistry Knowledge\n Matches the Performance of Expert-developed QSAR/QSPR Models
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge\n Matches the Performance of Expert-developed QSAR/QSPR Models Open
In the last few years, we have seen the transformative impact of deep\nlearning in many applications, particularly in speech recognition and computer\nvision. Inspired by Google's Inception-ResNet deep convolutional neural network\n(CNN) f…
View article: Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models Open
In the last few years, we have seen the transformative impact of deep learning in many applications, particularly in speech recognition and computer vision. Inspired by Google's Inception-ResNet deep convolutional neural network (CNN) for …
View article: Enabling scalability-sensitive speculative parallelization for FSM computations
Enabling scalability-sensitive speculative parallelization for FSM computations Open
Finite state machines (FSMs) are the backbone of many applications, but are difficult to parallelize due to their inherent dependencies. Speculative FSM parallelization has shown promise on multicore machines with up to eight cores. Howeve…
View article: User-transparent Distributed TensorFlow
User-transparent Distributed TensorFlow Open
Deep Learning (DL) algorithms have become the {\em de facto} choice for data analysis. Several DL implementations -- primarily limited to a single compute node -- such as Caffe, TensorFlow, Theano and Torch have become readily available. D…
View article: Deep Learning for Computational Chemistry
Deep Learning for Computational Chemistry Open
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning…