Search Results for author: Zhendong Su

Found 12 papers, 5 papers with code

CNNSAT: Fast, Accurate Boolean Satisfiability using Convolutional Neural Networks

no code implementations ICLR 2019 Yu Wang, Fengjuan Gao, Amin Alipour, Linzhang Wang, Xuandong Li, Zhendong Su

Boolean satisfiability (SAT) is one of the most well-known NP-complete problems and has been extensively studied.

Precise and Generalized Robustness Certification for Neural Networks

1 code implementation11 Jun 2023 Yuanyuan Yuan, Shuai Wang, Zhendong Su

We identify two key properties, independence and continuity, that convert the latent space into a precise and analysis-friendly input space representation for certification.

Autonomous Driving Style Transfer

ShapeFlow: Dynamic Shape Interpreter for TensorFlow

1 code implementation26 Nov 2020 Sahil Verma, Zhendong Su

We present ShapeFlow, a dynamic abstract interpreter for TensorFlow which quickly catches tensor shape incompatibility errors, one of the most common bugs in deep learning code.

Testing Machine Translation via Referential Transparency

no code implementations22 Apr 2020 Pinjia He, Clara Meister, Zhendong Su

Machine translation software has seen rapid progress in recent years due to the advancement of deep neural networks.

Machine Translation Medical Diagnosis +1

On the Unusual Effectiveness of Type-aware Mutations for Testing SMT Solvers

1 code implementation19 Apr 2020 Dominik Winterer, Chengyu Zhang, Zhendong Su

Among the 909 bugs found by OpFuzz, 130 were soundness bugs, the most critical bugs in SMT solvers, and 501 were in the default modes of the solvers.

Software Engineering Programming Languages

Metamorphic Testing for Object Detection Systems

no code implementations19 Dec 2019 Shuai Wang, Zhendong Su

To fill this critical gap, we introduce the design and realization of MetaOD, the first metamorphic testing system for object detectors to effectively reveal erroneous detection results by commercial object detectors.

Autonomous Driving Object +2

Structure-Invariant Testing for Machine Translation

2 code implementations19 Jul 2019 Pinjia He, Clara Meister, Zhendong Su

Despite its apparent importance, validating the robustness of machine translation systems is very difficult and has, therefore, been much under-explored.

Dependency Parsing Machine Translation +4

Learning Blended, Precise Semantic Program Embeddings

no code implementations3 Jul 2019 Ke Wang, Zhendong Su

Learning on the same set of functions (more than 170K in total), \liger significantly outperforms code2seq, the previous state-of-the-art for method name prediction.

Method name prediction Representation Learning

How Training Data Affect the Accuracy and Robustness of Neural Networks for Image Classification

no code implementations ICLR 2019 Suhua Lei, huan zhang, Ke Wang, Zhendong Su

In light of a recent study on the mutual influence between robustness and accuracy over 18 different ImageNet models, this paper investigates how training data affect the accuracy and robustness of deep neural networks.

General Classification Image Classification

Dynamic Neural Program Embeddings for Program Repair

no code implementations ICLR 2018 Ke Wang, Rishabh Singh, Zhendong Su

Our evaluation results show that the semantic program embeddings significantly outperform the syntactic program embeddings based on token sequences and abstract syntax trees.

Code Completion Fault localization

Dynamic Neural Program Embedding for Program Repair

1 code implementation20 Nov 2017 Ke Wang, Rishabh Singh, Zhendong Su

Evaluation results show that our new semantic program embedding significantly outperforms the syntactic program embeddings based on token sequences and abstract syntax trees.

Fault localization

Interactive, Intelligent Tutoring for Auxiliary Constructions in Geometry Proofs

no code implementations20 Nov 2017 Ke Wang, Zhendong Su

Although there exist many intelligent tutoring systems proposed for geometry proofs, few teach students how to find auxiliary constructions.

Automated Theorem Proving

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