Write a Blog >>
ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Wed 23 Sep 2020 17:30 - 17:50 at Kangaroo - Software Engineering for AI (3) Chair(s): Iftekhar Ahmed

As neural networks make their way into safety-critical systems, where misbehavior can lead to catastrophes, there is a growing interest in certifying the equivalence of two structurally similar neural networks. For example, compression techniques are often used in practice for deploying trained neural networks on computationally- and energy-constrained devices, which raises the question of how faithfully the compressed network mimics the original network. Unfortunately, existing methods either focus on verifying a single network or rely on loose approximations to prove the equivalence of two networks. Due to overly conservative approximation, differential verification lacks scalability in terms of both accuracy and computational cost. To overcome these problems, we propose NeuroDiff, a symbolic and fine-grained approximation technique that drastically increases the accuracy of differential verification while achieving many orders-of-magnitude speedup. NeuroDiff has two key contributions. The first one is new convex approximations that more accurately bound the difference neurons of two networks under all possible inputs. The second one is judicious use of symbolic variables to represent neurons whose difference bounds have accumulated significant error. We also find that these two techniques are complementary, i.e., when combined, the benefit is greater than the sum of their individual benefits. We have evaluated NeuroDiff on a variety of differential verification tasks. Our results show that NeuroDiff is up to 1000X faster and 5X more accurate than the state-of-the-art tool.

Wed 23 Sep

Displayed time zone: (UTC) Coordinated Universal Time change

17:10 - 18:10
Software Engineering for AI (3)Research Papers / Tool Demonstrations at Kangaroo
Chair(s): Iftekhar Ahmed University of California at Irvine, USA
Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of VarianceACM Distinguished Paper
Research Papers
Hung Viet Pham University of Waterloo, Shangshu Qian Purdue University, Jiannan Wang Purdue University, Thibaud Lutellier University of Waterloo, Jonathan Rosenthal Purdue University, Lin Tan Purdue University, USA, Yaoliang Yu University of Waterloo, Nachiappan Nagappan Microsoft Research
NeuroDiff: Scalable Differential Verification of Neural Networks using Fine-Grained Approximation
Research Papers
Brandon Paulsen University of Southern California, Jingbo Wang University of Southern California, Jiawei Wang University of Southern California, Chao Wang USC
RepoSkillMiner: Identifying software expertise from GitHub repositories using Natural Language Processing
Tool Demonstrations
Efstratios Kourtzanidis University Of Macedonia, Alexander Chatzigeorgiou University of Macedonia, Apostolos Ampatzoglou University of Macedonia
Pre-print Media Attached File Attached