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researcher

Haoji Hu

13 papers hereh-index 191.4k citations61 works total

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

author position
  • first author2
  • middle author8
  • last author3

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV6
  • cs.LG4
  • eess.IV2
  • cs.IR1
same name
  • Haoji Hu — 19 papers, h 8
  • Haoji Hu — 3 papers, h 7
  • Haoji Hu — 2 papers
  • Haoji Hu — 2 papers, h 4

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

activity
20182023
most citedPhysics-Guided Deep Neural Networks for Power Flow Analysis

213 citations · 255 across the 13 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023★ 5 cited

Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting

Arash Hajisafi, Haowen Lin, Sina Shaham +4

Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision-making for various application domains, from urban planning and…

cs.LG2023

Clustering Human Mobility with Multiple Spaces

Haoji Hu, Haowen Lin, Yao-Yi Chiang

Human mobility clustering is an important problem for understanding human mobility behaviors (e.g., work and school commutes). Existing methods typically contain two steps: choosin…

cs.LG2020★ 213 cited

Physics-Guided Deep Neural Networks for Power Flow Analysis

Xinyue Hu, Haoji Hu, Saurabh Verma +1

Solving power flow (PF) equations is the basis of power flow analysis, which is important in determining the best operation of existing systems, performing security analysis, etc.…

cs.LG2018★ 4 cited

Structured Pruning for Efficient ConvNets via Incremental Regularization

Huan Wang, Qiming Zhang, Yuehai Wang +2

Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance degrade. Despite its effectivene…

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