S. A. Jacobs

发表

AN ENERGY-EFFICIENT MULTI-MODAL ADAPTIVE CONTROL SCHEME AND ITS PROPOSED IMPLEMENTATION ON A PROTOTYPE CLIMBING ROBOT

S. A. Jacobs, A. A. Dehghani-Sanij, 2009 .

DEVELOPMENT OF PREDICTIVE MODELS OF CLIMBING ROBOT ATTACHMENT MECHANISMS FOR AN ENERGY-EFFICIENT ADAPTIVE CONTROL SCHEME

S. A. Jacobs, A. A. Dehghani-Sanu, 2008 .

Communication Quantization for Data-Parallel Training of Deep Neural Networks

Sam Ade Jacobs, Brian Van Essen, S. A. Jacobs, 2016, 2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC).

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets

Sam Ade Jacobs, Peer-Timo Bremer, Tom Benson, 2019, 2019 IEEE International Conference on Cluster Computing (CLUSTER).

Load Balancing Techniques for Scalable Parallelization of Sampling-Based Motion Planning Algorithms

S. A. Jacobs, L. Rauchwerger, Adam Fidel, 2013 .

Input Parameters X Y Predicted Output Forward Model Discriminator Model Physical Consistency Loss Inverse Model Cycle Consistency Loss X Predicted Parameters Surrogate Fidelity Loss ScalarImage

S. A. Jacobs, Tim Moon, B. V. Essen, 2019 .

Livermore Big Artificial Neural Network Toolkit

Sam Ade Jacobs, Hyojin Kim, Brian Van Essen, 2016 .

Learning Interpretable Models Through Multi-Objective Neural Architecture Search

S. A. Jacobs, Tim Moon, Zachariah Carmichael, 2021, ArXiv.

Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models

Sam Ade Jacobs, Felice C Lightstone, S. A. Jacobs, 2021, Int. J. High Perform. Comput. Appl..

Scalable Composition and Analysis Techniques for Massive Scientific Workflows

S. A. Jacobs, Jonathan E. Allen, B. V. Essen, 2022, 2022 IEEE 18th International Conference on e-Science (e-Science).

Parallelizing Graph Neural Networks via Matrix Compaction for Edge-Conditioned Networks

S. A. Jacobs, Tim Moon, B. V. Essen, 2022, 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid).

Adaptive neighbor connection for PRMs: A natural fit for heterogeneous environments and parallelism

Sam Ade Jacobs, Nancy M. Amato, Chinwe Ekenna, 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Local randomization in neighbor selection improves PRM roadmap quality

S. A. Jacobs, N. Amato, Lydia Tapia, 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Graph-based clustering for detecting frequent patterns in event log data

Sam Ade Jacobs, Yu Ding, Aldo Dagnino, 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE).

Enabling machine learning-ready HPC ensembles with Merlin

S. A. Jacobs, B. V. Essen, P. Bremer, 2019, Future Gener. Comput. Syst..

A scalable distributed RRT for motion planning

Sam Ade Jacobs, Nancy M. Amato, S. A. Jacobs, 2013, 2013 IEEE International Conference on Robotics and Automation.

Merlin: Enabling Machine Learning-Ready HPC Ensembles pdf

Sam Ade Jacobs, Rushil Anirudh, Peer-Timo Bremer, 2019, ArXiv.

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications

Sam Ade Jacobs, Valerio Pascucci, Rushil Anirudh, 2019, IEEE Transactions on Visualization and Computer Graphics.

Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms

Sam Ade Jacobs, Nancy M. Amato, Lawrence Rauchwerger, 2014, 2014 IEEE 28th International Parallel and Distributed Processing Symposium.

SUPER: SUb-Graph Parallelism for TransformERs

Dhabaleswar K. Panda, Hari Subramoni, Brian Van Essen, 2021, 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency pdf

Sam Ade Jacobs, S. A. Jacobs, Jonathan E. Allen, 2019, ArXiv.

DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

S. A. Jacobs, Samyam Rajbhandari, Yuxiong He, 2023, ArXiv.

ZeRO++: Extremely Efficient Collective Communication for Giant Model Training

S. A. Jacobs, Samyam Rajbhandari, Olatunji Ruwase, 2023, ArXiv.

Learning Interpretable Models Through Multi-Objective Neural Architecture Search

S. A. Jacobs, Tim Moon, Zachariah Carmichael, 2021, 2112.08645.

Enabling machine learning-ready HPC ensembles with Merlin

S. A. Jacobs, B. V. Essen, P. Bremer, 2019, Future Gener. Comput. Syst..