Search Results for author: Haoze Wu

Found 13 papers, 5 papers with code

Convex Bounds on the Softmax Function with Applications to Robustness Verification

1 code implementation3 Mar 2023 Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark Barrett, Eitan Farchi

The softmax function is a ubiquitous component at the output of neural networks and increasingly in intermediate layers as well.

VeriX: Towards Verified Explainability of Deep Neural Networks

no code implementations2 Dec 2022 Min Wu, Haoze Wu, Clark Barrett

We present VeriX, a system for producing optimal robust explanations and generating counterfactuals along decision boundaries of machine learning models.

On Optimizing Back-Substitution Methods for Neural Network Verification

no code implementations16 Aug 2022 Tom Zelazny, Haoze Wu, Clark Barrett, Guy Katz

A key component in many state-of-the-art verification schemes is computing lower and upper bounds on the values that neurons in the network can obtain for a specific input domain -- and the tighter these bounds, the more likely the verification is to succeed.

Toward Certified Robustness Against Real-World Distribution Shifts

no code implementations8 Jun 2022 Haoze Wu, Teruhiro Tagomori, Alexander Robey, Fengjun Yang, Nikolai Matni, George Pappas, Hamed Hassani, Corina Pasareanu, Clark Barrett

We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts.

Efficient Neural Network Analysis with Sum-of-Infeasibilities

1 code implementation19 Mar 2022 Haoze Wu, Aleksandar Zeljić, Guy Katz, Clark Barrett

Given a convex relaxation which over-approximates the non-convex activation functions, we encode the violations of activation functions as a cost function and optimize it with respect to the convex relaxation.

Adversarial Attack Efficient Neural Network

Scalable Verification of GNN-based Job Schedulers

1 code implementation7 Mar 2022 Haoze Wu, Clark Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh

Recently, Graph Neural Networks (GNNs) have been applied for scheduling jobs over clusters, achieving better performance than hand-crafted heuristics.


DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers

no code implementations2 Mar 2021 Colin Paterson, Haoze Wu, John Grese, Radu Calinescu, Corina S. Pasareanu, Clark Barrett

We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations such as blur, haze, and changes in image contrast.

An SMT-Based Approach for Verifying Binarized Neural Networks

1 code implementation5 Nov 2020 Guy Amir, Haoze Wu, Clark Barrett, Guy Katz

One novelty of our technique is that it allows the verification of neural networks that include both binarized and non-binarized components.

Global Optimization of Objective Functions Represented by ReLU Networks

no code implementations7 Oct 2020 Christopher A. Strong, Haoze Wu, Aleksandar Zeljić, Kyle D. Julian, Guy Katz, Clark Barrett, Mykel J. Kochenderfer

However, individual "yes or no" questions cannot answer qualitative questions such as "what is the largest error within these bounds"; the answers to these lie in the domain of optimization.

Parallelization Techniques for Verifying Neural Networks

no code implementations17 Apr 2020 Haoze Wu, Alex Ozdemir, Aleksandar Zeljić, Ahmed Irfan, Kyle Julian, Divya Gopinath, Sadjad Fouladi, Guy Katz, Corina Pasareanu, Clark Barrett

Inspired by recent successes with parallel optimization techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics that aim to leverage parallel computing to improve the scalability of neural network verification.

G2SAT: Learning to Generate SAT Formulas

1 code implementation NeurIPS 2019 Jiaxuan You, Haoze Wu, Clark Barrett, Raghuram Ramanujan, Jure Leskovec

The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, and theorem proving.

Automated Theorem Proving

Improve SAT-solving with Machine Learning

no code implementations30 Oct 2017 Haoze Wu

We first used a logistic regression model to predict the satisfiability of propositional boolean formulae after fixing the values of a certain fraction of the variables in each formula.

BIG-bench Machine Learning Multi-Stage Campaigning Optimization +1

Gene Ontology (GO) Prediction using Machine Learning Methods

no code implementations30 Oct 2017 Haoze Wu, Yangyu Zhou

We applied machine learning to predict whether a gene is involved in axon regeneration.

BIG-bench Machine Learning

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