115 citations · 331 across the 10 of their papers we have counts for
11 papers
Recombination of Artificial Neural Networks
Aaron Vose, Jacob Balma, Alex Heye +5
We propose a genetic algorithm (GA) for hyperparameter optimization of artificial neural networks which includes chromosomal crossover as well as a decoupling of parameters (i.e.,…
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction
Garrett B. Goh, Khushmeen Sakloth, Charles Siegel +2
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. H…
ColdRoute: Effective Routing of Cold Questions in Stack Exchange Sites
Jiankai Sun, Abhinav Vishnu, Aniket Chakrabarti +2
Routing questions in Community Question Answer services (CQAs) such as Stack Exchange sites is a well-studied problem. Yet, cold-start -- a phenomena observed when a new question i…
GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent
Jeff Daily, Abhinav Vishnu, Charles Siegel +2
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 sy…
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction
Garrett B. Goh, Charles Siegel, Abhinav Vishnu +1
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…
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
Garrett B. Goh, Nathan O. Hodas, Charles Siegel +1
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…
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
Garrett B. Goh, Charles Siegel, Abhinav Vishnu +2
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 representat…
What does fault tolerant Deep Learning need from MPI?
Vinay Amatya, Abhinav Vishnu, Charles Siegel +1
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…
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
Garrett B. Goh, Charles Siegel, Abhinav Vishnu +2
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 In…
User-transparent Distributed TensorFlow
Abhinav Vishnu, Joseph Manzano, Charles Siegel +1
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, Te…
Distributed TensorFlow with MPI
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. W…