264 citations · 403 across the 9 of their papers we have counts for
9 papers
Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational -body Problem
Maxwell X. Cai, Simon Portegies Zwart, Damian Podareanu
The gravitational -body problem, which is fundamentally important in astrophysics to predict the motion of celestial bodies under the mutual gravity of each other, is usuall…
A Holistic Analysis of Datacenter Operations: Resource Usage, Energy, and Workload Characterization -- Extended Technical Report
Laurens Versluis, Mehmet Cetin, Caspar Greeven +5
Improving datacenter operations is vital for the digital society. We posit that doing so requires our community to shift, from operational aspects taken in isolation to holistic an…
DeepGalaxy: Deducing the Properties of Galaxy Mergers from Images Using Deep Neural Networks
Maxwell X. Cai, Jeroen Bédorf, Vikram A. Saletore +4
Galaxy mergers, the dynamical process during which two galaxies collide, are among the most spectacular phenomena in the Universe. During this process, the two colliding galaxies a…
NetSquid, a NETwork Simulator for QUantum Information using Discrete events
Tim Coopmans, Robert Knegjens, Axel Dahlberg +13
In order to bring quantum networks into the real world, we would like to determine the requirements of quantum network protocols including the underlying quantum hardware. Because…
Predicting atmospheric optical properties for radiative transfer computations using neural networks
Menno A. Veerman, Robert Pincus, Robin Stoffer +3
The radiative transfer equations are well-known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizati…
Deep-learning enhancement of large scale numerical simulations
Caspar van Leeuwen, Damian Podareanu, Valeriu Codreanu +11
Traditional simulations on High-Performance Computing (HPC) systems typically involve modeling very large domains and/or very complex equations. HPC systems allow running large mod…
Densifying Assumed-sparse Tensors: Improving Memory Efficiency and MPI Collective Performance during Tensor Accumulation for Parallelized Training of Neural Machine Translation Models
Derya Cavdar, Valeriu Codreanu, Can Karakus +11
Neural machine translation - using neural networks to translate human language - is an area of active research exploring new neuron types and network topologies with the goal of dr…
Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
Sydney Otten, Sascha Caron, Wieske de Swart +6
We present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physic…
Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train
Valeriu Codreanu, Damian Podareanu, Vikram Saletore
For the past 5 years, the ILSVRC competition and the ImageNet dataset have attracted a lot of interest from the Computer Vision community, allowing for state-of-the-art accuracy to…