1 code implementation • 3 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.
no code implementations • 2 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.
no code implementations • 16 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.
no code implementations • 8 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.
1 code implementation • 19 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.
1 code implementation • 7 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.
no code implementations • 2 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.
1 code implementation • 5 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.
no code implementations • 7 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.
no code implementations • 17 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.
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.
no code implementations • 30 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
no code implementations • 30 Oct 2017 • Haoze Wu, Yangyu Zhou
We applied machine learning to predict whether a gene is involved in axon regeneration.