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Jie Shi

4 papers hereh-index 13924 citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • eess.SY2
same name
  • Jie Shi — 12 papers, h 13
  • Jie Shi — 8 papers, h 1
  • Jie Shi — 6 papers, h 4
  • Jie Shi — 5 papers, h 2
  • Jie Shi — 4 papers, h 4
  • Jie Shi — 4 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedBatch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration

9 citations · 23 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2020★ 3 cited

Power System Event Identification based on Deep Neural Network with Information Loading

Jie Shi, Brandon Foggo, Nanpeng Yu

Online power system event identification and classification is crucial to enhancing the reliability of transmission systems. In this paper, we develop a deep neural network (DNN) b…

eess.SY2020★ 9 cited

Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration

Yuanqi Gao, Wei Wang, Jie Shi +1

Dynamic distribution network reconfiguration (DNR) algorithms perform hourly status changes of remotely controllable switches to improve distribution system performance. The proble…

eess.SY2019★ 3 cited

Smart Building Energy Management using Nonlinear Economic Model Predictive Control

Mohammad Ostadijafari, Anamika Dubey, Yang Liu +2

Owing to the call for energy efficiency, the need to optimize the energy consumption of commercial buildings-- responsible for over 40% of US energy consumption--has recently gaine…

cs.LG2019★ 8 cited

Information Losses in Neural Classifiers from Sampling

Brandon Foggo, Nanpeng Yu, Jie Shi +1

This paper considers the subject of information losses arising from the finite datasets used in the training of neural classifiers. It proves a relationship between such losses as…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.