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Hoda Eldardiry

Virginia Tech

46 papers hereh-index 181.2k citations119 works total

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

author position
  • middle author9
  • last author36

Across the 45 of 46 papers where every author was matched, so the position is known.

fields
  • cs.LG14
  • cs.CL8
  • cs.CV4
  • cs.IR4
  • stat.ML4
  • cs.CY3
affiliations
  • Virginia Tech
Homepage
same name
  • Hoda Eldardiry — 1 paper, h 0

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
20172026
most citedLearning Role-based Graph Embeddings

69 citations · 172 across the 44 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.LG2023

GradXKG: A Universal Explain-per-use Temporal Knowledge Graph Explainer

Chenhan Yuan, Hoda Eldardiry

Temporal knowledge graphs (TKGs) have shown promise for reasoning tasks by incorporating a temporal dimension to represent how facts evolve over time. However, existing TKG reasoni…

cs.IR2023★ 8 cited

Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction

Jiaying Gong, Wei-Te Chen, Hoda Eldardiry

Existing attribute-value extraction (AVE) models require large quantities of labeled data for training. However, new products with new attribute-value pairs enter the market every…

cs.LG2023

REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments

Humaid Ahmed Desai, Amr Hilal, Hoda Eldardiry

Federated Learning (FL) plays a critical role in distributed systems. In these systems, data privacy and confidentiality hold paramount importance, particularly within edge-based d…

math.OC2023★ 1 cited

Data-Driven Near-Optimal Control of Nonlinear Systems Over Finite Horizon

Vasanth Reddy, Hoda Eldardiry, Almuatazbellah Boker

We examine the problem of two-point boundary optimal control of nonlinear systems over finite-horizon time periods with unknown model dynamics by employing reinforcement learning.…

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