26 citations · 35 across the 6 of their papers we have counts for
6 papers
PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment
Meghana Madhyastha, Kunal Lillaney, James Browne +2
We present methods to serialize and deserialize tree ensembles that optimize inference latency when models are not already loaded into memory. This arises whenever models are large…
Towards Marrying Files to Objects
Kunal Lillaney, Vasily Tarasov, David Pease +1
To deal with the constant growth of unstructured data, vendors have deployed scalable, resilient, and cost effective object-based storage systems built on RESTful web services. How…
A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data
Randal Burns, Eric Perlman, Alex Baden +27
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visual…
A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information
Kwame S. Kutten, Nicolas Charon, Michael I. Miller +7
CLARITY is a method for converting biological tissues into translucent and porous hydrogel-tissue hybrids. This facilitates interrogation with light sheet microscopy and penetratio…
Gradient-Domain Fusion for Color Correction in Large EM Image Stacks
Michael Kazhdan, Kunal Lillaney, William Roncal +3
We propose a new gradient-domain technique for processing registered EM image stacks to remove inter-image discontinuities while preserving intra-image detail. To this end, we proc…
The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience
Randal Burns, William Gray Roncal, Dean Kleissas +17
We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for t…