Alexander Gerhard Schwing

20072019
If you made any changes in Pure, your changes will be visible here soon.

Fingerprint Fingerprint is based on mining the text of the expert's scholarly documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

  • 1 Similar Profiles
Message passing Engineering & Materials Science
Neural networks Engineering & Materials Science
Labels Engineering & Materials Science
Semantics Engineering & Materials Science
Random variables Engineering & Materials Science
Reinforcement learning Engineering & Materials Science
Decomposition Engineering & Materials Science
Labeling Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2007 2019

Accelerating distributed reinforcement learning with in-switch computing

Li, Y., Liu, I. J., Yuan, Y., Chen, D., Schwing, A. G. & Huang, J., Jun 22 2019, ISCA 2019 - Proceedings of the 2019 46th International Symposium on Computer Architecture. Institute of Electrical and Electronics Engineers Inc., p. 279-291 13 p. (Proceedings - International Symposium on Computer Architecture).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Reinforcement learning
Agglomeration
Switches
Scalability
Packet networks

Knowledge flow: Improve upon your teachers

Liu, I. J., Peng, J. & Schwing, A. G., Jan 1 2019.

Research output: Contribution to conferencePaper

Students
teacher
Tuning
knowledge
student

Learning to play in a day: Faster deep reinforcement learning by optimality tightening

He, F. S., Liu, Y., Schwing, A. G. & Peng, J., Jan 1 2019.

Research output: Contribution to conferencePaper

Reinforcement learning
reinforcement
Constrained optimization
learning
reward

Optical inspection of nanoscale structures using a novel machine learning based synthetic image generation algorithm

Purandare, S., Zhu, J., Zhou, R., Popescu, G., Schwing, A. G. & Goddard, L. L., Jan 1 2019, In : Optics Express. 27, 13, p. 17743-17762 20 p.

Research output: Contribution to journalArticle

machine learning
inspection
defects
education
wafers

A network-centric hardware/algorithm co-design to accelerate distributed training of deep neural networks

Li, Y., Park, J., Alian, M., Yuan, Y., Qu, Z., Pan, P., Wang, R., Schwing, A. G., Esmaeilzadeh, H. & Kim, N. S., Dec 12 2018, Proceedings - 51st Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2018. IEEE Computer Society, p. 175-188 14 p. 8574540. (Proceedings of the Annual International Symposium on Microarchitecture, MICRO; vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hardware
Communication
Interfaces (computer)
Particle accelerators
Agglomeration