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researcher

A. Lin

5 papers hereh-index 5192 citations12 works total

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

author position
  • first author1
  • middle author3
  • last author1

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.DB3
  • cs.CL1
  • cs.FL1
same name
  • A. Lin — 6 papers, h 2
  • A. Lin — 2 papers, h 2
  • A. Lin — 2 papers, h 2
  • A. Lin — 2 papers, h 2
  • A. Lin — 1 paper, h 0
  • A. Lin — 1 paper, h 1

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

collaborators

5 papers

cs.DB2026

Revisiting the Expressiveness Landscape of Data Graph Queries

Michael Benedikt, Anthony Widjaja Lin, Di-De Yen

The study of graph queries in database theory has spanned more than three decades, resulting in a multitude of proposals for graph query languages. We can identify three main famil…

cs.CL2026

The Counting Power of Transformers

Marco Sälzer, Chris Köcher, Alexander Kozachinskiy +2

Counting properties (e.g. determining whether certain tokens occur more than other tokens in a given input text) have played a significant role in the study of expressiveness of tr…

cs.DB2025

Answering Constraint Path Queries over Graphs

Heyang Li, Anthony Widjaja Lin, Domagoj Vrgoč

Constraints are powerful declarative constructs that allow users to conveniently restrict variable values that potentially range over an infinite domain. In this paper, we propose…

cs.DB2025

Complexity of Evaluating GQL Queries

Diego Figueira, Anthony W. Lin, Liat Peterfreund

GQL has recently emerged as the standard query language over graph databases (particularly, the property graph model). Indeed, this is analogous to the role of SQL for relational d…

cs.FL2025

The Role of Logic and Automata in Understanding Transformers

Anthony W. Lin, Pablo Barcelo

The advent of transformers has in recent years led to powerful and revolutionary Large Language Models (LLMs). Despite this, our understanding on the capability of transformers is…

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