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Found 26 papers, 15 papers with code
3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization
The trained 3D CNNs are able to segment entire 852 x 852 x 250 voxel 3D volumes in only ~60 seconds, thus hastening the progress towards a deeper understanding of phase transformation phenomena such as dendritic solidification.
fairDMS: Rapid Model Training by Data and Model Reuse
Further, our model service achieves up to 200x improvement in training speed.
Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing
Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform.
Ultrafast Focus Detection for Automated Microscopy
Technological advancements in modern scientific instruments, such as scanning electron microscopes (SEMs), have significantly increased data acquisition rates and image resolutions enabling new questions to be explored; however, the resulting data volumes and velocities, combined with automated experiments, are quickly overwhelming scientists as there remain crucial steps that require human intervention, for example reviewing image focus.
KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural Networks
Kronecker-factored Approximate Curvature (K-FAC) has recently been shown to converge faster in deep neural network (DNN) training than stochastic gradient descent (SGD); however, K-FAC's larger memory footprint hinders its applicability to large models.
BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes
We describe how the task of rescaling suitable DNN training tasks to fit dynamically changing holes can be formulated as a deterministic mixed integer linear programming (MILP)-based resource allocation algorithm, and show that this MILP problem can be solved efficiently at run time.
Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval
Extremely high data rates at modern synchrotron and X-ray free-electron laser light source beamlines motivate the use of machine learning methods for data reduction, feature detection, and other purposes.
Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations
The use of ML methods to dynamically steer ensemble-based simulations promises significant improvements in the performance of scientific applications.
Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder
Advanced satellite-born remote sensing instruments produce high-resolution multi-spectral data for much of the globe at a daily cadence.
Fast and accurate learned multiresolution dynamical downscaling for precipitation
We compare the four new CNN-derived high-resolution precipitation results with precipitation generated from original high resolution simulations, a bilinear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique.
AI- and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes to Extract Druglike Molecules Contained in Natural Language Text
Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Infrastructure for Artificial Intelligence, Quantum and High Performance Computing
High Performance Computing (HPC), Artificial Intelligence (AI)/Machine Learning (ML), and Quantum Computing (QC) and communications offer immense opportunities for innovation and impact on society.
Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics.
The Rise of AI-Driven Simulators: Building a New Crystal Ball
These advances may lead to a new era in computational simulation, in which sensors of many kinds are used to produce vast quantities of data, AI methods identify patterns in those data, and new AI-driven simulators combine machine-learned and mathematical rules to make accurate and actionable predictions.
HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data
Intermolecular and long-range interactions are central to phenomena as diverse as gene regulation, topological states of quantum materials, electrolyte transport in batteries, and the universal solvation properties of water.
Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis
Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in catalysis, energy materials, composites, and more.
BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning
When applied to a real experimental dataset, a 3D reconstruction that used peak positions computed by BraggNN yields 15% better results on average as compared to a reconstruction obtained using peak positions determined using conventional 2D pseudo-Voigt fitting.
Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release
Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
funcX: A Federated Function Serving Fabric for Science
These new approaches must enable computation to be mobile, so that, for example, it can occur near data, be triggered by events (e. g., arrival of new data), be offloaded to specialized accelerators, or run remotely where resources are available.
Distributed, Parallel, and Cluster Computing
Scientific Image Restoration Anywhere
We explore this question by evaluating the performance and accuracy of a scientific image restoration model, for which both model input and output are images, on edge computing devices.
Deep Learning Accelerated Light Source Experiments
Experimental protocols at synchrotron light sources typically process and validate data only after an experiment has completed, which can lead to undetected errors and cannot enable online steering.
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
We use the problem of learning properties of inorganic materials from numerical attributes derived from material composition and/or crystal structure to compare IRNet's performance against that of other machine learning techniques.
Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations
Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the tradeoff between accuracy and computational cost.
TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks
We present \TOMOGAN{}, a denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions.
DLHub: Model and Data Serving for Science
Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications.
BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments
This framework automatically collects provenance data, including both performance data from workflow execution and data from the scientific domain of the workflow application.
Distributed, Parallel, and Cluster Computing Databases
