This year at the High Performance Extreme Computing Conference (HPEC), happening virtually from September 20- 24, Reservoir Labs presents four research papers: Knowledge-guided Tensor Decomposition for Baselining and Anomaly Detection (in collaboration with UMD’s Laboratory for Physical Sciences) An All-at-Once
Dr. Jordi Ros-Giralt, Fellow, and Noah Amsel, Research Engineer at Reservoir Labs, present “Designing Data Center Networks Using Bottleneck Structures,” at the flagship annual conference of the ACM Special Interest Group on Data Communication (SIGCOMM), Tuesday, August 24th, at 12:50pm
Dr. Jordi Ros-Giralt, Fellow at Reservoir Labs, presents “Gradient Graph Analytics: Using Bottleneck Structures to Accelerate Network Performance” at the Asia Pacific Advanced Network (APAN 52) this Wednesday, August 4, at 4pm WIB (Western Indonesia time) during the Asia Pacific
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CANDECOMP/PARAFAC (CP) tensor decomposition is a popular method for detecting latent behaviors in real– world data sets. As data sets grow larger and more elaborate, more sophisticated CP decomposition algorithms are required to enable these discoveries. Data sets from many applications can be represented as count tensors. To decompose count
Reservoir Labs, through a project titled MACH- B (Multipole Accelerator Codes for Hadron Beams), is developing a Fast Multipole Method [1]–[7] (FMM)-based tool for higher fidelity modeling of particle accelerators for high-energy physics with an initial focus on the next generation of Fermi- lab’s Synergia simulation package [8]. MACH-B incorporates
Drug repositioning (also called “drug repurposing”) is a drug development strategy that saves time and money by finding new uses for existing drugs. While a variety of computational approaches to drug repositioning exist, recent work has shown that tensor decomposition, an unsupervised learning technique for finding latent structure in multidimensional
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