178 citations · 248 across the 6 of their papers we have counts for
6 papers
Computing Representations for Lie Algebraic Networks
Noah Shutty, Casimir Wierzynski
Recent work has constructed neural networks that are equivariant to continuous symmetry groups such as 2D and 3D rotations. This is accomplished using explicit Lie group representa…
Optimizing User Interface Layouts via Gradient Descent
Peitong Duan, Casimir Wierzynski, Lama Nachman
Automating parts of the user interface (UI) design process has been a longstanding challenge. We present an automated technique for optimizing the layouts of mobile UIs. Our method…
Design of optical neural networks with component imprecisions
Michael Y. -S. Fang, Sasikanth Manipatruni, Casimir Wierzynski +2
For the benefit of designing scalable, fault resistant optical neural networks (ONNs), we investigate the effects architectural designs have on the ONNs' robustness to imprecise co…
nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data
Fabian Boemer, Anamaria Costache, Rosario Cammarota +1
In previous work, Boemer et al. introduced nGraph-HE, an extension to the Intel nGraph deep learning (DL) compiler, that enables data scientists to deploy models with popular frame…
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics
Yaron Meirovitch, Lu Mi, Hayk Saribekyan +3
Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object tracking or instance segmentation followed by o…
nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data
Fabian Boemer, Yixing Lao, Rosario Cammarota +1
Homomorphic encryption (HE)---the ability to perform computation on encrypted data---is an attractive remedy to increasing concerns about data privacy in deep learning (DL). Howeve…