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E. Alves

4 papers hereh-index 559 citations7 works total

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

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.CR1
  • cs.LG1
  • cs.LO1
same name
  • E. Alves — 16 papers, h 14
  • E. Alves — 7 papers, h 3
  • E. Alves — 4 papers, h 27
  • E. Alves — 3 papers, h 5
  • E. Alves — 2 papers, h 10
  • E. Alves — 1 paper, h 52

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 citedQNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking

5 citations · 6 across the 4 of their papers we have counts for

collaborators

4 papers

cs.AI2021★ 5 cited

QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking

Xidan Song, Edoardo Manino, Luiz Sena +5

QNNVerifier is the first open-source tool for verifying implementations of neural networks that takes into account the finite word-length (i.e. quantization) of their operands. The…

cs.LG2021★ 1 cited

Verifying Quantized Neural Networks using SMT-Based Model Checking

Luiz Sena, Xidan Song, Erickson Alves +4

Artificial Neural Networks (ANNs) are being deployed for an increasing number of safety-critical applications, including autonomous cars and medical diagnosis. However, concerns ab…

cs.CR2021

Verifying Security Vulnerabilities in Large Software Systems using Multi-Core k-Induction

Thales Silva, Carmina Porto, Erickson Alves +2

Computer-based systems have been used to solve several domain problems, such as industrial, military, education, and wearable. Those systems need high-quality software to guarantee…

cs.LO2020

Incremental Verification of Fixed-Point Implementations of Neural Networks

Luiz Sena, Erickson Alves, Iury Bessa +2

Implementations of artificial neural networks (ANNs) might lead to failures, which are hardly predicted in the design phase since ANNs are highly parallel and their parameters are…

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