
Imagedriven discriminative and generative machine learning algorithms for establishing microstructureprocessing relationships
We investigate methods of microstructure representation for the purpose ...
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PhysicsInformed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
Data assimilation for parameter and state estimation in subsurface trans...
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Robustness Metrics for RealWorld Adversarial Examples
We explore metrics to evaluate the robustness of realworld adversarial ...
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A Scalable Framework for Acceleration of CNN Training on DeeplyPipelined FPGA Clusters with Weight and Workload Balancing
Deep Neural Networks (DNNs) have revolutionized numerous applications, b...
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Streetlevel Traveltime Estimation via Aggregated Uber Data
Estimating temporal patterns in travel times along road segments in urba...
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PhysicsInformed Kriging: A PhysicsInformed Gaussian Process Regression Method for DataModel Convergence
In this work, we propose a new Gaussian process regression (GPR) method:...
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Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder
Recent studies have shown that the aggregated dynamic flexibility of an ...
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An imagedriven machine learning approach to kinetic modeling of a discontinuous precipitation reaction
Micrograph quantification is an essential component of several materials...
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Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training
Training neural network often uses a machine learning framework such as ...
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A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs
Tensor computations present significant performance challenges that impa...
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A Graphbased Model for GPU Caching Problems
Modeling data sharing in GPU programs is a challenging task because of t...
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A Class of Logistic Functions for Approximating StateInclusive Koopman Operators
An outstanding challenge in nonlinear systems theory is identification o...
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ChemNet: A Transferable and Generalizable Deep Neural Network for SmallMolecule Property Prediction
With access to large datasets, deep neural networks (DNN) have achieved ...
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SMILES2Vec: An Interpretable GeneralPurpose Deep Neural Network for Predicting Chemical Properties
Chemical databases store information in text representations, and the SM...
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Faster Fuzzing: Reinitialization with Deep Neural Models
We improve the performance of the American Fuzzy Lop (AFL) fuzz testing ...
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How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
In the last few years, we have seen the rise of deep learning applicatio...
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Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems
The Koopman operator has recently garnered much attention for its value ...
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Adaptive Neuron Apoptosis for Accelerating Deep Learning on Large Scale Systems
We present novel techniques to accelerate the convergence of Deep Learni...
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Dynamic Input Structure and Network Assembly for FewShot Learning
The ability to learn from a small number of examples has been a difficul...
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Accelerating Deep Learning with Shrinkage and Recall
Deep Learning is a very powerful machine learning model. Deep Learning t...
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A Workflow for Visual Diagnostics of Binary Classifiers using InstanceLevel Explanations
Humanintheloop data analysis applications necessitate greater transpa...
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Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs
Link prediction, or predicting the likelihood of a link in a knowledge g...
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Multicentrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection
Many modern datasets can be represented as graphs and hence spectral dec...
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Feature Clustering for Accelerating Parallel Coordinate Descent
Largescale L1regularized loss minimization problems arise in highdime...
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Fishing for Clickbaits in Social Images and Texts with LinguisticallyInfused Neural Network Models
This paper presents the results and conclusions of our participation in ...
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SVEN: Informative Visual Representation of Complex Dynamic Structure
Graphs change over time, and typically variations on the small multiples...
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Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks
Increasingly, cognitive scientists have demonstrated interest in applyin...
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Beyond Fine Tuning: A Modular Approach to Learning on Small Data
In this paper we present a technique to train neural network models on s...
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A DataDriven Approach for Semantic Role Labeling from Induced Grammar Structures in Language
Semantic roles play an important role in extracting knowledge from text....
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Predicting the top and bottom ranks of billboard songs using Machine Learning
The music industry is a 130 billion industry. Predicting whether a song ...
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Multimodal Geolocation Estimation Using Deep Neural Networks
Estimating the location where an image was taken based solely on the con...
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Bounded Rationality in Scholarly Knowledge Discovery
In an informationrich world, people's time and attention must be divide...
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VietorisRips and Cech Complexes of Metric Gluings
We study VietorisRips and Cech complexes of metric wedge sums and metri...
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A General Framework of Enhancing Sparsity of Generalized Polynomial Chaos Expansions
Compressive sensing has become a powerful addition to uncertainty quanti...
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A Chronological EdgeDriven Approach to Temporal Subgraph Isomorphism
Many real world networks are considered temporal networks, in which the ...
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FewShot Learning with MetricAgnostic Conditional Embeddings
Learning high quality class representations from few examples is a key p...
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Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
Deep learning has recently demonstrated stateofthe art performance on ...
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GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent
In this paper, we present GossipGraD  a gossip communication protocol b...
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Multivariate Gaussian Process Regression for Multiscale Data Assimilation and Uncertainty Reduction
We present a multivariate Gaussian process regression approach for param...
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A 2/3Approximation Algorithm for Vertexweighted Matching in Bipartite Graphs
We consider the maximum vertexweighted matching problem (MVM), in which...
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Lidar Cloud Detection with Fully Convolutional Networks
In this contribution, we present a novel approach for segmenting laser r...
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Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction
Deep learning algorithms excel at extracting patterns from raw data. Thr...
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Using RuleBased Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction
With access to large datasets, deep neural networks (DNN) have achieved ...
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Trimmed Ensemble Kalman Filter for Nonlinear and NonGaussian Data Assimilation Problems
We study the ensemble Kalman filter (EnKF) algorithm for sequential data...
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Model of Cognitive Dynamics Predicts Performance on Standardized Tests
In the modern knowledge economy, success demands sustained focus and hig...
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ILNet: Using Expert Knowledge to Guide the Design of Furcated Neural Networks
Deep neural networks (DNN) excel at extracting patterns. Through represe...
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Scaling Submodular Optimization Approaches for Control Applications in Networked Systems
Often times, in many design problems, there is a need to select a small ...
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Projecting Trouble: Light Based Adversarial Attacks on Deep Learning Classifiers
This work demonstrates a physical attack on a deep learning image classi...
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PhysicsInformed CoKriging: A GaussianProcessRegressionBased Multifidelity Method for DataModel Convergence
In this work, we propose a new Gaussian process regression (GPR)based m...
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When Bifidelity Meets CoKriging: An Efficient PhysicsInformed Multifidelity Method
In this work, we propose a framework that combines the approximationthe...
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PNNL
Pacific Northwest National Laboratory is one of the United States Department of Energy national laboratories, managed by the Department of Energy's Office of Science.