This may lead to unintended effects, such as being susceptible to adversarial attacks or the reinforcement of biases. This paper describes the first approach to data-driven modelling of near-term precipitation using a combination of deep-learning concepts, that is, … Communications of the ACM, Vol. 3 Dec 2018 • Xinyang Zhang • Ningfei Wang • Hua Shen • Shouling Ji • Xiapu Luo • Ting Wang. 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. 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. Interpretable deep learning under fire. For example, in applications such as healthcare, users need to understand the rationale of the decisions. AU - Luo, Xiapu. of interpretable deep learning systems ( IDLSes ). 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. Daniel received his B.S. Sue Ellen Haupt. 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 ∙ 0 ∙ share . We trade explainability for a few shortcuts, like employing static ANN topologies and back propagation. Interpretable Deep Learning under FireEdit social preview. 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. However, given Zhang et al. AU - Shen, Hua. ... Zhang et al. Unfortunately, current methods are optimized for non-security tasks ( e.g., image analysis). We present. Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning. 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. 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. 2018; In this paper, the authors explain a new machine learning model, Deep K-Nearest Neighbors (DkNN). In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (EMNLP). Providing explanations for complicated deep neural network (DNN) models is critical for their usability in security-sensitive domains. 10.1145/3359786. 63 No. Interpretable Deep Learning under Fire. 1846151 and 1910546. 43: 2020: Integration of Static and Dynamic Code Stylometry Analysis for Programmer De-anonymization. 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. 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 1, Pages 68-77. Discovering Interpretable Representations for Both Deep Generative and Discriminative Models Figure 2: Workflow of an interpretable deep learning system (IDLS). Recent years have witnessed the significant advances of machine learning in a wide spectrum of applications. Existing research has attempted to develop explanation techniques to provide interpretable explanations for each classification decision. 29th {USENIX} Security Symposium ({USENIX} Security 20), 2020. Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. R Pang, H Shen, X Zhang, S Ji, Y Vorobeychik, X Luo, A Liu, T Wang. ... Wildland Fire Prediction ... 2019: Interpretable Deep Learning for Spatial Severe Hail Forecasting, Monthly Weather Re. view, 147, 2827-2845. Xinyang Zhang, Ningfei Wang, Shouling Ji, Hua Shen, and Ting Wang. 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. T1 - Interpretable deep learning under fire. 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? Adversarial Attention Modeling for … Download PDF. NCAR is sponsored by the National Science Foundation under CA # 1852977 . The proposed framework enables quantifying the attribution of each finding in making diagnostic decisions as commonly done by radiologists. 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?.. Deep Learning and the Demand for Interpretability. Interpretable Deep Learning under Fire. 04/23/2020 ∙ by Ninghao Liu, et al. Google Scholar; Ying Zheng, Ziyu Liu, Xinyu You, Yuedong Xu, and Junchen Jiang. 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. Building interpretable machine learning models is not a purely computational model [...] what is or is not "interpretable" is defined by people, not algorithms. TextBugger: Generating Adversarial Text Against Real-world Applications Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, Ting Wang The 2019 Network and … Preprint. Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. Interpretable deep learning under fire. ️Interpretable Deep Learning under Fire; ICML 2018. arXiv preprint arXiv:1812.00891 (2018). ACM APNet, 2018. Xinyang Zhang, Ningfei Wang, Shouling Ji, Hua Shen, and Ting Wang. Interpretable deep learning under fire. 2018. 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). In USENIX Security Symposium 2020. Save PDF. Naman Bansal. Machine Learning in Weather Forecasting Systems. AU - Ji, Shouling. 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. 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. “Mom, Dad, I can explain everything! 1/28/2021. Written on May 2, 2017. 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.) By Mengnan Du, Ninghao Liu, Xia Hu. 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. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. You will be redirected to the full text document in the repository in a few seconds, if not click here. Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. “ (The Mummy). ADV 2, a general class of attacks that generate adversarial in-. SFS business. We are not allowed to display external PDFs yet. Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. Techniques for Interpretable Machine Learning. Video: What you need to know about the SolarWinds Supply-Chain Attack Today machine learning is used, for example, to detect fraudulent financial transactions, recommend movies to watch and classify images. Overview The Artificial Intelligence and Robotics (AIR) group studies theory, algorithms, and systems for making intelligent decisions in complex and uncertain environments. 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. 2019. An interpretable deep learning framework that quantifies odds-ratio between radiographic findings and pulmonary diseases was developed without compromising the accuracy. The DkNN model takes as input a trained neural net, a number of neighbors k, and an input. USENIX Security, 2020. I am interested in Computer Vision, Natural Language Processing and Deep Learning. Interpretable Deep Learning under Fire. in Communication Engineering and M.S. DOI: 10.1175/MWR -D-18-0316.1. 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. AU - Wang, Ningfei. Interpretable Deep Learning under Fire Xinyang Zhang, Ningfei Wang, Shouling Ji, Hua Shen, Ting Wang ArXiv e-prints, 2018. N1 - Funding Information: This material is based upon work supported by the National Science Foundation under Grant No. Interpretable Deep Learning under Fire. Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. In Proc. In Proc. 29th {USENIX} Security Symposium ({USENIX} Security 20), 2020. Title: Interpretable Deep Learning under Fire. Techniques for Interpretable Machine Learning. Recommended publications. A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations. I am a doctoral student working under the guidance of Dr. Shubhra Kanti Karmaker (Santu) at Auburn University. Interpretable Deep Learning under Fire X. Zhang et al. 2020. Interpretable Deep Learning under Fire. X Zhang, N Wang, H Shen, S Ji, X Luo, T Wang. In Proceedings of USENIX Security Symposium (SEC). (2020) Xinyang Zhang, Ningfei Wang, Hua Shen, Shouling Ji, Xiapu Luo, and Ting Wang. It is often crucial that the machine learning models are interpretable. 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? AU - Wang, Ting. However, machine learning models, especially deep neural networks, have been recently found to be vulnerable to carefully-crafted input called adversarial samples. X Zhang, N Wang, H Shen, S Ji, X Luo, T Wang. Google Scholar; Suyang Zhu, Shoushan Li, and Guodong Zhou. Discover more. 43: 2020: A tale of evil twins: Adversarial inputs versus poisoned models. Machine learning is a powerful technology for products, research and automation. AU - Zhang, Xinyang. The enhanced inter-pretability of IDLSes is believed to offer a sense of security by involving human in the decision process [57]. Interpretable Deep Learning under Fire - CORE Reader. This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Full-text … 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 N Wang, S Ji, T Wang. 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 terpretable deep learning system (IDLS). Demystifying deep learning in networking. Interpretable Deep Learning under Fire. Google Scholar Digital Library SFS/DoD Scholarship Space; Research project/shadowing activities; Information Session on 2/3 and 2/16; Presentation. Authors: Xinyang Zhang, Ningfei Wang, Hua Shen, Shouling Ji, Xiapu Luo, Ting Wang. Prior to joining Auburn, I completed my undergraduate studies in … Impact of Using Convolutional Sometimes it is not enough for a DNN to produce an outcome. Interpretable Deep Learning under Fire . Interpretable Deep Learning under Fire. Deep learning has always been under fire for a lot of things in a lot of contexts. 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 … Techniques for Interpretable Machine Learning from CACM on Vimeo. Opera is a scalable local simulation network for experimental researches on distributed systems. Modeling annotators: A generative approach to learning from annotator rationales. from CACM. Abstract: Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. Interpretable Deep Learning under Fire.

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