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researcher

T. Nguyen

19 papers hereh-index 7242 citations26 works total

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

author position
  • middle author5
  • last author12

Across the 17 of 19 papers where every author was matched, so the position is known.

fields
  • cs.CL12
  • cs.LG4
  • cs.AI2
  • cs.CV1
same name
  • T. Nguyen — 143 papers, h 0
  • T. Nguyen — 70 papers, h 19
  • T. Nguyen — 37 papers, h 35
  • T. Nguyen — 22 papers, h 17
  • T. Nguyen — 19 papers
  • T. Nguyen — 19 papers, h 22

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
20242026
most citedA Survey of Small Language Models

6 citations · 10 across the 19 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

CRISP: Cliff-awaRe Input-adaptive Sparse Prefilling with Structural-Mass-Motivated Routing

Huu Huy Nguyen, Chien Van Nguyen, Franck Dernoncourt +4

The attention prefilling phase of long-context LLM inference scales quadratically, making self-attention a severe computational bottleneck. Traditional sparse attention methods mit…

cs.LG2026

Geometric and Stochastic Analysis of Discontinuities in Sparse Mixture-of-Experts

Tho Tran Huu, Huu-Tuan Nguyen, Thien-Hai Nguyen +4

Sparse Mixture-of-Experts (SMoE) architectures are now widely deployed in state-of-the-art language and vision models, where conditional routing allows scaling to very large networ…

cs.LG2026

Orthrus: Memory-Efficient Parallel Token Generation via Dual-View Diffusion

Chien Van Nguyen, Chaitra Hegde, Van Cuong Pham +3

We introduce Orthrus, a simple and efficient dual-architecture framework that unifies the exact generation fidelity of autoregressive Large Language Models (LLMs) with the high-spe…

cs.LG2025

From Selection to Generation: A Survey of LLM-based Active Learning

Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie +31

Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent…

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