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Found 10 papers, 5 papers with code
Recombination of Artificial Neural Networks
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., weights and biases) from hyperparameters (e. g., learning rate, weight decay, and dropout) during sexual reproduction.
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.
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.
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.
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.
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.
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
