Machine learning is a powerful technology for products, research and automation. We present. Xinyang Zhang, Ningfei Wang, Shouling Ji, Hua Shen, and Ting Wang. However, machine learning models, especially deep neural networks, have been recently found to be vulnerable to carefully-crafted input called adversarial samples. USENIX Security Symposium 2020 Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. Adversarial Machine Learning: An Interpretation Perspective. Deep learning models have improved cutting-edge technologies in many research areas, but their black-box structure makes it difficult to understand their inner workings and the rationale behind their predictions. Abstract: Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. This may lead to unintended effects, such as being susceptible to adversarial attacks or the reinforcement of biases. Authors: Xinyang Zhang, Ningfei Wang, Hua Shen, Shouling Ji, Xiapu Luo, Ting Wang. Adversarial Attention Modeling for … I am a doctoral student working under the guidance of Dr. Shubhra Kanti Karmaker (Santu) at Auburn University. In Proceedings of USENIX Security Symposium (SEC). Techniques for Interpretable Machine Learning from CACM on Vimeo. 1846151 and 1910546. In Proc. terpretable deep learning system (IDLS). (2020) Xinyang Zhang, Ningfei Wang, Hua Shen, Shouling Ji, Xiapu Luo, and Ting Wang. The research covers most aspects of AIR including perception and interpretation of sensor data, learning about environments, learning to make decisions, automated planning and reasoning, and interaction of Hurricanes can experience rapid increases in intensity, in which they can strengthen from a tropical storm to a major hurricane in only a couple of days. Opera is a scalable local simulation network for experimental researches on distributed systems. The enhanced inter-pretability of IDLSes is believed to offer a sense of security by involving human in the decision process [57]. Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning. Demystifying deep learning in networking. X Zhang, N Wang, H Shen, S Ji, X Luo, T Wang. 04/23/2020 ∙ by Ninghao Liu, et al. 63 No. I am interested in Computer Vision, Natural Language Processing and Deep Learning. The DkNN model takes as input a trained neural net, a number of neighbors k, and an input. Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. AU - Wang, Ting. An interpretable deep learning framework that quantifies odds-ratio between radiographic findings and pulmonary diseases was developed without compromising the accuracy. Full-text … Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. Interpretable deep learning under fire. 3 Dec 2018 • Xinyang Zhang • Ningfei Wang • Hua Shen • Shouling Ji • Xiapu Luo • Ting Wang. Existing research has attempted to develop explanation techniques to provide interpretable explanations for each classification decision. view, 147, 2827-2845. Video: What you need to know about the SolarWinds Supply-Chain Attack A plethora of interpretation models have been proposed to help users understand the inner workings of DNNs: how does a DNN arrive at a specific decision for a given input?.. By Mengnan Du, Ninghao Liu, Xia Hu. 1, Pages 68-77. Interpretable deep learning under fire. However, given 2019. To the best of our knowledge, it is the first Java-based event-driven simulator for distributed systems with a modular network, induced churn and latency traces from real-world systems, full life cycle management of the nodes, and a production-grade simulation monitoring. “ (The Mummy). Deep learning has always been under fire for a lot of things in a lot of contexts. Providing explanations for complicated deep neural network (DNN) models is critical for their usability in security-sensitive domains. This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Naman Bansal. Title: Interpretable Deep Learning under Fire. AU - Shen, Hua. N Wang, S Ji, T Wang. You will be redirected to the full text document in the repository in a few seconds, if not click here. Discovering Interpretable Representations for Both Deep Generative and Discriminative Models Building interpretable machine learning models is not a purely computational model [...] what is or is not "interpretable" is defined by people, not algorithms. 10.1145/3359786. This paper describes the first approach to data-driven modelling of near-term precipitation using a combination of deep-learning concepts, that is, … in Communications and Information Systems from Wuhan University.He obtained his Ph.D. degree in Computer Science from the Hong Kong Polytechnic University, under the supervision of Prof. Rocky K.C. Modeling annotators: A generative approach to learning from annotator rationales. There is criticism about the arbitrariness of its hyperparameters and choice of architecture (Yann LeCun’s strong reaction to a rejected paper from CVPR’12). Go to arXiv Download as Jupyter Notebook: 2019-06-21 [1812.00891] Interpretable Deep Learning under Fire Through extensive empirical evaluation, we show that ACID attacks are effective against a wide range of DNNs and interpretation models, implying that the interpretability of existing IDLSes only offers a false sense of security 29th {USENIX} Security Symposium ({USENIX} Security 20), 2020. Interpretable Deep Learning under Fire . 2018; In this paper, the authors explain a new machine learning model, Deep K-Nearest Neighbors (DkNN). Interpretable Deep Learning under Fire. Interpretable Deep Learning under Fire USENIX Security 2020 (Rittika*) Sanity Checks for Saliency Maps NeurIPS 2018 (Rittika) Week 11: Nov 6: Attacking ML (application 1) AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning CCS 2019 (Rishabh C.) While deep learning has shown a great potential in various domains, the lack of transparency has limited its application in security or safety-critical areas. A plethora of interpretation models have been proposed to help users understand the inner workings of DNNs: how does a DNN arrive at a specific decision for a given input? It is often crucial that the machine learning models are interpretable. We trade explainability for a few shortcuts, like employing static ANN topologies and back propagation. We are not allowed to display external PDFs yet. Interpretable Deep Learning under Fire. Interpretable Deep Learning under Fire - CORE Reader. Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples. This may lead to unintended effects, such as being susceptible to adversarial attacks or the reinforcement of biases. Deep Learning and the Demand for Interpretability. AU - Wang, Ningfei. In Proc. Today machine learning is used, for example, to detect fraudulent financial transactions, recommend movies to watch and classify images. Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. Deep learning models have improved cutting-edge technologies in many research areas, but their black-box structure makes it difficult to understand their inner workings and the rationale behind their predictions. For example, in applications such as healthcare, users need to understand the rationale of the decisions. T1 - Interpretable deep learning under fire. ADV 2, a general class of attacks that generate adversarial in-. 29th {USENIX} Security Symposium ({USENIX} Security 20), 2020. 43: 2020: A tale of evil twins: Adversarial inputs versus poisoned models. 2018. Google Scholar Digital Library Xinyang Zhang, Ningfei Wang, Shouling Ji, Hua Shen, and Ting Wang. AU - Luo, Xiapu. SFS business. Sue Ellen Haupt. USENIX Security, 2020. in Communication Engineering and M.S. The proposed framework enables quantifying the attribution of each finding in making diagnostic decisions as commonly done by radiologists. X Zhang, N Wang, H Shen, S Ji, X Luo, T Wang. Vadim and Andre: Interpretable Deep Learning under Fire, Xinyang Zhang, Ningfei Wang, Hua Shen, Shouling Ji, Xiapu Luo, Ting Wang, Usenix Security 2020: Tues April 27: Final Project Talks: Karim and Deborah: Deceptive Previews: A Study of the Link Preview Trustworthiness in Social Platforms, Giada Stivala and Giancarlo Pellegrino, NDSS 2020 Download PDF. Interpretable deep learning under fire. ️Interpretable Deep Learning under Fire; ICML 2018. Communications of the ACM, Vol. 2020. Interpretable Deep Learning under Fire. Impact of Using Convolutional Figure 2: Workflow of an interpretable deep learning system (IDLS). Interpretable Deep Learning under Fire. from CACM. Interpretable Deep Learning under Fire X. Zhang et al. of interpretable deep learning systems ( IDLSes ). Interpretable Deep Learning under Fire A Survey Of Methods For Explaining Black Box Models Techniques for Interpretable Machine Learning CVPR18: Tutorial: Part 1: Interpreting and Explaining Deep Models in Computer Vision 4 SFS/DoD Scholarship Space; Research project/shadowing activities; Information Session on 2/3 and 2/16; Presentation. R Pang, H Shen, X Zhang, S Ji, Y Vorobeychik, X Luo, A Liu, T Wang. These rapid intensification periods are currently difficult to predict, but deep learning may be able to detect spatial patterns in the storms that are precursors to rapid intensification. “Mom, Dad, I can explain everything! Recommended publications. Authors: Xinyang Zhang, Ningfei Wang, Shouling Ji, Hua Shen, Ting Wang (Submitted on 3 Dec 2018) Abstract: Providing explanations for complicated deep neural network (DNN) models is critical for their usability in security-sensitive domains. Interpretable Deep Learning under FireEdit social preview. Machine Learning in Weather Forecasting Systems. These rapid intensification periods are currently difficult to predict, but deep learning may be able to detect spatial patterns in the storms that are precursors to rapid intensification. Interpretable Deep Learning under Fire. Interpretable Deep Learning under Fire. Zhang et al. Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen Shouling Ji† Xiapu Luo‡ Ting Wang Lehigh University †Zhejiang University ‡Hong Kong Polytechnic University Abstract Providing explanations for deep neural network (DNN) models is crucial for … The interpretable model does not have to work well globally, but it must approximate the black-box model well in a small neighborhood near the original input. 1/28/2021. Preprint. Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. Unfortunately, current methods are optimized for non-security tasks ( e.g., image analysis). Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. Overview The Artificial Intelligence and Robotics (AIR) group studies theory, algorithms, and systems for making intelligent decisions in complex and uncertain environments. DOI: 10.1175/MWR -D-18-0316.1. ∙ 0 ∙ share . Techniques for Interpretable Machine Learning. In USENIX Security Symposium 2020. Interpretable deep learning under fire. Save PDF. Local approximation-based explanation is based on the assumption the machine learning predictions around the neighborhood of a given input can be approximated by an interpretable white-box model. Interpretable Deep Learning under Fire Xinyang Zhang, Ningfei Wang, Shouling Ji, Hua Shen, Ting Wang ArXiv e-prints, 2018. A plethora of interpretation models have been proposed to help users understand the inner workings of DNNs: how does a DNN arrive at a specific decision for a given input? Discover more. Techniques for Interpretable Machine Learning. A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations. Interpretable Deep Learning under Fire. Prior to joining Auburn, I completed my undergraduate studies in … Daniel received his B.S. Written on May 2, 2017. Hurricanes can experience rapid increases in intensity, in which they can strengthen from a tropical storm to a major hurricane in only a couple of days. TextBugger: Generating Adversarial Text Against Real-world Applications Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, Ting Wang The 2019 Network and … ACM APNet, 2018. ... Zhang et al. Sometimes it is not enough for a DNN to produce an outcome. NCAR is sponsored by the National Science Foundation under CA # 1852977 . Google Scholar; Suyang Zhu, Shoushan Li, and Guodong Zhou. Google Scholar; Ying Zheng, Ziyu Liu, Xinyu You, Yuedong Xu, and Junchen Jiang. ... Wildland Fire Prediction ... 2019: Interpretable Deep Learning for Spatial Severe Hail Forecasting, Monthly Weather Re. Interpretable Deep Learning under Fire. AU - Ji, Shouling. arXiv preprint arXiv:1812.00891 (2018). N1 - Funding Information: This material is based upon work supported by the National Science Foundation under Grant No. 43: 2020: Integration of Static and Dynamic Code Stylometry Analysis for Programmer De-anonymization. AU - Zhang, Xinyang. Recent years have witnessed the significant advances of machine learning in a wide spectrum of applications. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (EMNLP).

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