353 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Hewlett Packard Enterprise (Ireland)IE11 papers
- Hewlett-Packard (United States)US10 papers
- Nanyang Technological UniversitySG6 papers
- Forschungszentrum JülichDE5 papers
- Stanford UniversityUS5 papers
- Georgia Institute of TechnologyUS4 papers
- RWTH Aachen UniversityDE4 papers
- The University of Texas at AustinUS4 papers
- University of California, BerkeleyUS4 papers
- Aalto UniversityFI3 papers
- Carnegie Mellon UniversityUS3 papers
- Centre National de la Recherche ScientifiqueFR3 papers
5 papers · 1 filter
The Boundary for Quantum Advantage in Gaussian Boson Sampling
Jacob F. F. Bulmer, Bryn A. Bell, Rachel S. Chadwick +9
Identifying the boundary beyond which quantum machines provide a computational advantage over their classical counterparts is a crucial step in charting their usefulness. Gaussian…
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning
Sean Hooten, Raymond G. Beausoleil, Thomas Van Vaerenbergh
We present a proof-of-concept technique for the inverse design of electromagnetic devices motivated by the policy gradient method in reinforcement learning, named PHORCED (PHotonic…
Tree-based machine learning performed in-memory with memristive analog CAM
Giacomo Pedretti, Catherine E. Graves, Can Li +5
Tree-based machine learning techniques, such as Decision Trees and Random Forests, are top performers in several domains as they do well with limited training datasets and offer im…
A Physics Based Multiscale Compact Model of p-i-n Avalanche Photodiodes
Sheikh Z. Ahmed, Samiran Ganguly, Yuan Yuan +4
III-V material based digital alloy Avalanche Photodiodes (APDs) have recently been found to exhibit low noise similar to Silicon APDs. The III-V materials can be chosen to operate…
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation
Zhuosheng Zhang, Jiarui Li, Shucheng Yu +1
For model privacy, local model parameters in federated learning shall be obfuscated before sent to the remote aggregator. This technique is referred to as \emph{secure aggregation}…