arXiv, 2021. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. The model uses learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Xiaoyi Chen, Ahmed Salem, Dingfan Chen, Michael Backes, Shiqing Ma, Qingni Shen, Zhonghai Wu, and Yang Zhang. Fanchao Qi, Yangyi Chen, Mukai Li, Zhiyuan Liu, and Maosong Sun. Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, and George Kesidis. arXiv, 2021. arXiv, 2022. [code], An Anomaly Detection Approach for Backdoored Neural Networks: Face Recognition as a Case Study. Self-attention just means that we are performing the attention operation on the sentence itself, as opposed to 2 different sentences (this is attention). Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. [link], Backdoor Attacks on the DNN Interpretation System. [pdf] [pdf], BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning. [arXiv-20], Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning. This website uses cookies to improve your experience while you navigate through the website. The number of devices involved in the training adds an element of unpredictability to the model training, as connection issues, irregular updates, and even different application use times can contribute to increased convergence time and decreased reliability. [pdf], CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing. [pdf], DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection. [pdf], Adversarial Unlearning of Backdoors via Implicit Hypergradient. [pdf], PTB: Robust Physical Backdoor Attacks against Deep Neural Networks in Real World. Huili Chen, Cheng Fu, Jishen Zhao, and Farinaz Koushanfar. [pdf], Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers. Then, we make [pdf], On the Trade-off between Adversarial and Backdoor Robustness. [code], Robust Anomaly Detection and Backdoor Attack Detection via Differential Privacy. arXiv, 2022. Photo: Jeromemetronome via Wikimedia Commons, CC By S.A. 4.0 (https://en.wikipedia.org/wiki/File:Federated_learning_process_central_case.png). Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.. Reinforcement learning differs from supervised learning arXiv, 2022. [pdf], Un-fair trojan: Targeted Backdoor Attacks against Model Fairness. It consists of a bunch of tutorial notebooks for various deep learning topics. [code], Poisoning and Backdooring Contrastive Learning. As you can see in the diagram above, be it a classification or a regression task, the T5 model still generates new text to get the output. [code], Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution. [pdf], Universal Post-Training Backdoor Detection. Haoqi Wang, Mingfu Xue, Shichang Sun, Yushu Zhang, Jian Wang, and Weiqiang Liu. arXiv, 2022. Hao Cheng, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Pu Zhao, and Xue Lin. Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. [pdf], Backdoor Attack in the Physical World. [pdf], LIRA: Learnable, Imperceptible and Robust Backdoor Attacks. [pdf], Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction. [pdf] Xinzhe Zhou, Wenhao Jiang, Sheng Qi, and Yadong Mu. [pdf], Just How Toxic is Data Poisoning? arXiv, 2022. Shaofeng Li, Minhui Xue, Benjamin Zi Hao Zhao, Haojin Zhu, and Xinpeng Zhang. [pdf], Detect and Remove Watermark in Deep Neural Networks via Generative Adversarial Networks. [pdf], VulnerGAN: A Backdoor Attack through Vulnerability Amplification against Machine Learning-based Network Intrusion Detection Systems. arXiv, 2021. Yiming Li, Yong Jiang, Zhifeng Li, and Shu-Tao Xia. Could a robot interpret a sarcastic remark? [pdf], RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models. Ren Pang, Hua Shen, Xinyang Zhang, Shouling Ji, Yevgeniy Vorobeychik, Xiapu Luo, Alex Liu, and Ting Wang. But that was precisely why I decided to introduce it at the end. arXiv, 2020. [pdf] Get to know the top 10 Deep Learning Algorithms with examples such as CNN, LSTM, RNN, GAN, & much more to enhance your knowledge in Deep Learning. [code], Mitigating Data Poisoning in Text Classification with Differential Privacy. Necessary cookies are absolutely essential for the website to function properly. Jun Yan, Vansh Gupta, and Xiang Ren. Yi Zeng, Won Park, Z. Morley Mao, and Ruoxi Jia. You also have the option to opt-out of these cookies. [link], Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection. The minimum number of state variables required to represent a given system, , is usually equal to the order of the system's defining differential equation, but not necessarily.If the system is represented in transfer arXiv, 2022. Yanjiao Chen, Zhicong Zheng, and Xueluan Gong. BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models. [pdf], Clean-Annotation Backdoor Attack against Lane Detection Systems in the Wild. An autoencoder is composed of an encoder and a decoder sub-models. arXiv, 2022. [code], Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks Linyang Li, Demin Song, Xiaonan Li, Jiehang Zeng, Ruotian Ma, and Xipeng Qiu. arXiv, 2022. General-Purpose Machine Learning. arXiv, 2022. October 18, 2016. Browse publications by this author arXiv, 2021. Guanlin Li, Shangwei Guo, Run Wang, Guowen Xu, and Tianwei Zhang. [pdf], TAD: Trigger Approximation based Black-box Trojan Detection for AI. In this tutorial, you will learn how to classify images of cats and dogs by using transfer learning from a pre-trained network. [pdf], Label-Consistent Backdoor Attacks. Step 2: Each partition is now a node in the Graph Neural Network. Yoon, and Ivan Beschastnikh. Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, and George Kesidis. arXiv, 2022. The entire process then repeats. Huiying Li, Arjun Nitin Bhagoji, Ben Y. Zhao, and Haitao Zheng. Alvin Chan, and Yew-Soon Ong. Im sure youve asked these questions before. Wanlun Ma, Derui Wang, Ruoxi Sun, Minhui Xue, Sheng Wen, and Yang Xiang. [pdf] [pdf], Neural Network Laundering: Removing Black-Box Backdoor Watermarks from Deep Neural Networks. [pdf] [pdf], Towards Adversarial and Backdoor Robustness of Deep Learning. Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg. The different devices/clients train their own copy of the model using the clients local data, and then the parameters/weights from the individual models are sent to a master device, or server, that aggregates the parameters and updates the global model. So, for example, the sentence I like going to New York will have the following partitions: Note: A sentence with n words will have 2*n 1 partitions, and in the end, you have a complete binary tree. Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, and Feng Yan. [pdf], Exploiting Missing Value Patterns for a Backdoor Attack on Machine Learning Models of Electronic Health Records: Development and Validation Study. arXiv, 2022. Mojan Javaheripi, Mohammad Samragh, Gregory Fields, Tara Javidi, and Farinaz Koushanfar. Training a deep autoencoder or a classifier on MNIST digits - Training a deep autoencoder or a classifier on MNIST digits[DEEP LEARNING]. [pdf], Adversarial Fine-tuning for Backdoor Defense: Connect Adversarial Examples to Triggered Samples. A deep CNN that uses sub-pixel convolution layers to upscale the input image. Yu Feng, Benteng Ma, Jing Zhang, Shanshan Zhao, Yong Xia, and Dacheng Tao. [code], Understanding the Threats of Trojaned Quantized Neural Network in Model Supply Chains. [pdf], FLAME: Taming Backdoors in Federated Learning. Tong Wang, Yuan Yao, Feng Xu, Miao Xu, Shengwei An, and Ting Wang. This repository contains material related to Udacity's Deep Learning Nanodegree Foundation program. The deep learning-based transfer learning method, also denoted as deep transfer learning, has shown great merits in establishing generalized model. Lingfeng Shen, Haiyun Jiang, Lemao Liu, and Shuming Shi. Zichao Li, Dheeraj Mekala, Chengyu Dong, and Jingbo Shang. [pdf], A Stealthy and Robust Fingerprinting Scheme for Generative Models. Harsh Chaudhari, Matthew Jagielski, and Alina Oprea. Guiyu Tian, Wenhao Jiang, Wei Liu, and Yadong Mu. In the first step, this generic model is sent out to the applications clients. [pdf] She is an active member of ACM, AAAI, IEEE, and INNS. arXiv, 2022. Le Feng, Sheng Li, Zhenxing Qian, and Xinpeng Zhang. Federated learning schemas typically fall into one of two different classes: multi-party systems and single-party systems. Panagiota Kiourti, Wenchao Li, Anirban Roy, Karan Sikka, and Susmit Jha. [pdf], TrojViT: Trojan Insertion in Vision Transformers. [pdf], FaceHack: Triggering Backdoored Facial Recognition Systems Using Facial Characteristics. arXiv, 2022. Byunggill Joe, Yonghyeon Park, Jihun Hamm, Insik Shin, and Jiyeon Lee. Once all these entities are retrieved, the weight of each entity is calculated using the softmax-based attention function. A memristor (/ m m r s t r /; a portmanteau of memory resistor) is a non-linear two-terminal electrical component relating electric charge and magnetic flux linkage.It was described and named in 1971 by Leon Chua, completing a theoretical quartet of fundamental electrical components which comprises also the resistor, capacitor and inductor.. Chua and Kang later Ill cover 6 state-of-the-art text classification pretrained models in this article. [code], One-Pixel Signature: Characterizing CNN Models for Backdoor Detection. Welcome to this course on Getting started with TensorFlow 2! Esha Sarkar, Hadjer Benkraouda, and Michail Maniatakos. This is called a binary partitioning. [pdf], Federated Learning in Adversarial Settings. Train a sparse autoencoder with hidden size 4, 400 maximum epochs, and linear transfer function for [pdf], PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion. [pdf], Online Defense of Trojaned Models using Misattributions. [code], FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis. [pdf], PerD: Perturbation Sensitivity-based Neural Trojan Detection Framework on NLP Applications. Lun Wang, Zaynah Javed, Xian Wu, Wenbo Guo, Xinyu Xing, and Dawn Song. [code], Don't Trigger Me! Jinyin Chen, Xueke Wang, Yan Zhang, Haibin Zheng, Shanqing Yu, and Liang Bao. arXiv, 2020. Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Haitao Zheng, and Ben Y. Zhao. [pdf] [pdf], Hidden Trigger Backdoor Attacks. Hui Xia, Xiugui Yang, Xiangyun Qian, and Rui Zhang. Nan Zhong, Zhenxing Qian, and Xinpeng Zhang. Detecto - Train and run a computer vision model with 5-10 lines of code. Analytics Vidhya App for the Latest blog/Article, An Essential Guide to Pretrained Word Embeddings for NLP Practitioners, TensorFlow 2.0 Tutorial for Deep Learning, We use cookies on Analytics Vidhya websites to deliver our services, analyze web traffic, and improve your experience on the site. [pdf], Widen The Backdoor To Let More Attackers In. [pdf], Learning to Detect Malicious Clients for Robust Federated Learning. Media recommendation engines, of the type used by Netflix or Amazon, could be trained on data gathered from thousands of users. arXiv, 2021. [pdf] [pdf], Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases. Deep Learning Nanodegree Foundation. The BP Transformer again uses the transformer, or rather an enhanced version of it for text classification, machine translation, etc. There was a problem preparing your codespace, please try again. The benefit of having a copy of the model on the various devices is that network latencies are reduced or eliminated. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. This happens periodically, on a set schedule. Tim Clifford, Ilia Shumailov, Yiren Zhao, Ross Anderson, and Robert Mullins. It simultaneously understands the nouns New York, and I; understand the verb like, and infers that New York is a place. [pdf], Understanding and Mitigating the Impact of Backdooring Attacks on Deep Neural Networks. ERNIE achieves a SOTA F1-Score of 88.32 on the Relation Extraction Task. [link], Geometric Properties of Backdoored Neural Networks. Suyi Li, Yong Cheng, Wei Wang, Yang Liu, and Tianjian Chen. [code], Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks. Yuhua Sun, Tailai Zhang, Xingjun Ma, Pan Zhou, Jian Lou, Zichuan Xu, Xing Di, Yu Cheng, and Lichao Sun. Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, and Arjun Gupta. In the third step, the server aggregates the learned parameters when it receives them. Jiawang Bai, Kuofeng Gao, Dihong Gong, Shu-Tao Xia, Zhifeng Li, and Wei Liu. [pdf], Client-Wise Targeted Backdoor in Federated Learning. Shuwen Chai and Jinghui Chen. arXiv, 2022. 2022. [pdf], Backdoor Attacks in Neural Networks. [pdf] Pengfei Xia, Ziqiang Li, Wei Zhang, and Bin Li. In most cases, the notebooks lead you through implementing models such as convolutional networks, recurrent networks, and GANs. Miguel Villarreal-Vasquez, and Bharat Bhargava. [pdf] Xijie Huang, Moustafa Alzantot, and Mani Srivastava. arXiv, 2022. arXiv, 2022. [pdf], A Study of the Attention Abnormality in Trojaned BERTs. [code], Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips. Note: This has been released on TensorFlow too: c4. There was a problem preparing your codespace, please try again. Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet. [code], Excess Capacity and Backdoor Poisoning. Xiangyu Qi, Tinghao Xie, Ruizhe Pan, Jifeng Zhu, Yong Yang, and Kai Bu. Shuhao Fu, Chulin Xie, Bo Li, and Qifeng Chen. [pdf], Few-Shot Backdoor Attacks on Visual Object Tracking. Though BERTs autoencoder did take care of this aspect, it did have other disadvantages like assuming no correlation between the masked words. October 18, 2016. [pdf] [pdf], Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning. [pdf], BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label. William Aiken, Hyoungshick Kim, and Simon Woo. arXiv, 2020. [code], A Feature-Based On-Line Detector to Remove Adversarial-Backdoors by Iterative Demarcation. Marchi E, Schuller B. [pdf], DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation. By using Analytics Vidhya, you agree to our, A Comprehensive Guide to Understand and Implement Text Classification in Python, XLNet: Generalized Autoregressive Pretraining for Language Understanding, ERNIE: Enhanced Language Representation with Informative Entities, Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, https://github.com/google-research/text-to-text-transfer-transformer, BP-Transformer: Modelling Long-Range Context via Binary Partitioning, Neural Attentive Bag-of-Entities Model for Text Classification, https://github.com/wikipedia2vec/wikipedia2vec/tree/master/examples/text_classification, Rethinking Complex Neural Network Architectures for Document Classification, It has reduced the cost of training a new deep learning model every time, These datasets meet industry-accepted standards, and thus the pretrained models have already been vetted on the quality aspect, Rethinking Complex Neural Network Architectures, Generalized Autoregressive Pretraining for Language Understanding, A recurrence at specific segments which gives the context between 2 sequences, A relative positional embedding which contains information on the similarity between 2 tokens, can generate the output of more than 1 task at the same time. How Data Heterogeneity Affects the Robustness of Federated Learning. Zhenting Wang, Juan Zhai, and Shiqing Ma. arXiv, 2022. [pdf], A Backdoor Attack against 3D Point Cloud Classifiers. October 18, 2016. PySyft is an open-source federated learning library based on the deep learning library PyTorch. arXiv, 2022. It is able to learn a function that a set of 256x256-pixel face images, for example, to a vector of length 100, and also the inverse function that transforms the vector back into a Stefanos Koffas, Stjepan Picek, and Mauro Conti. [link], Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systems. Kuofeng Gao, Jiawang Bai, Bin Chen, Dongxian Wu, and Shu-Tao Xia. [code], Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes. [code], Kallima: A Clean-label Framework for Textual Backdoor Attacks. Ahmed Salem, Yannick Sautter, Michael Backes, Mathias Humbert, and Yang Zhang. [pdf], ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks. arXiv, 2021. Xueluan Gong, Yanjiao Chen, Huayang Huang, Yuqing Liao, Shuai Wang, and Qian Wang. Transfer Learning [10, 11] is another interesting paradigm to prevent overfitting. [link], Can You Hear It? [pdf], A New Backdoor Attack in CNNS by Training Set Corruption Without Label Poisoning. The task which is to be performed is encoded as a prefix along with the input. Jiqiang Gao, Baolei Zhang, Xiaojie Guo, Thar Baker, Min Li, and Zheli Liu. [pdf], Hidden Backdoor Attack against Semantic Segmentation Models. [pdf] We are now able to use a pre-existing model built on a huge dataset and tune it to achieve other tasks on a different dataset. arXiv, 2022. [pdf], Constrained Optimization with Dynamic Bound-scaling for Effective NLPBackdoor Defense. But opting out of some of these cookies may affect your browsing experience. [link] Binghui Wang, Xiaoyu Cao, Jinyuan jia, and Neil Zhenqiang Gong. arXiv, 2020. arXiv, 2021. [pdf], TRAPDOOR: Repurposing Backdoors to Detect Dataset Bias in Machine Learning-based Genomic Analysis. There's also live online events, interactive content, certification prep materials, and more. The NABoE model performs particularly well on Text Classification tasks: Now, it might appear counter-intuitive to study all these advanced pretrained models and at the end, discuss a model that uses plain (relatively) old Bidirectional LSTM to achieve SOTA performance. Zhen Xiang, David J. Miller, Hang Wang, and George Kesidis. [code], ProFlip: Targeted Trojan Attack with Progressive Bit Flips. Chang Xu, Jun Wang, Francisco Guzmn, Benjamin I. P. Rubinstein, and Trevor Cohn. Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Quoc Viet Hung Nguyen, and Lizhen Cui. WeiGuo, Benedetta Tondi, and Mauro Barni. If nothing happens, download GitHub Desktop and try again. The internal state variables are the smallest possible subset of system variables that can represent the entire state of the system at any given time. [pdf] [pdf], TrojDRL: Evaluation of Backdoor Attacks on Deep Reinforcement Learning. [pdf], Versatile Weight Attack via Flipping Limited Bits. Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Mahesh Subedar, Nilesh Ahuja, Ranganath Krishnan, Ibrahima J. Ndiour, and Omesh Tickoo. What is an adversarial example? X is an 8-by-4177 matrix defining eight attributes for 4177 different abalone shells: sex (M, F, and I (for infant)), length, diameter, height, whole weight, shucked weight, viscera weight, shell weight. Tianlong Chen, Zhenyu Zhang, Yihua Zhang*, Shiyu Chang, Sijia Liu, and Zhangyang Wang. [pdf] [code], Rethinking Stealthiness of Backdoor Attack against NLP Models. [pdf], The Limitations of Federated Learning in Sybil Settings. [pdf], Spinning Sequence-to-Sequence Models with Meta-Backdoors. arXiv, 2020. [link], Multi-Target Invisibly Trojaned Networks for Visual Recognition and Detection. differentiable or subdifferentiable).It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated [pdf], Defending Against Backdoor Attack on Graph Nerual Network by Explainability. Shangxi Wu, Qiuyang He, Yi Zhang, and Jitao Sang. [pdf], Stealthy Backdoors as Compression Artifacts. Mohammad Naseri, Jamie Hayes, and Emiliano De Cristofaro. Daniel hopes to help others use the power of AI for social good. arXiv, 2022. She has more than 40 publications in international journals and conferences. arXiv, 2020. Tong Wang, Yuan Yao, Feng Xu, Shengwei An, and Ting Wang. Like its predecessor, ERNIE 2.0 brings another innovation to the table in the form of Continual Incremental Multi-task Learning. [code], Defending Neural Backdoors via Generative Distribution Modeling. Yiming Li, Ziqi Zhang, Jiawang Bai, Baoyuan Wu, Yong Jiang, and Shu-Tao Xia. Lujia Shen, Shouling Ji, Xuhong Zhang, Jinfeng Li, Jing Chen, Jie Shi, Chengfang Fang, Jianwei Yin, and Ting Wang. This training process can then be repeated until a desired level of accuracy is attained. [pdf], Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution. [pdf], Using Honeypots to Catch Adversarial Attacks on Neural Networks. [pdf], Low-Loss Subspace Compression for Clean Gains against Multi-Agent Backdoor Attacks. Keita Kurita, Paul Michel, and Graham Neubig. Contribute to gbstack/CVPR-2022-papers development by creating an account on GitHub. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Lubin Meng, Jian Huang, Zhigang Zeng, Xue Jiang, Shan Yu, Tzyy-Ping Jung, Chin-Teng Lin, Ricardo Chavarriaga, and Dongrui Wu. Jie Wang, Ghulam Mubashar Hassan, and Naveed Akhtar. Yiming Li, Linghui Zhu, Xiaojun Jia, Yong Jiang, Shu-Tao Xia, and Xiaochun Cao. Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors. Ahmed Salem, Yannick Sautter, Michael Backes, Mathias Humbert, and Yang Zhang. [pdf], Backdoor Scanning for Deep Neural Networks through K-Arm Optimization. 20180428 IJCAI-18 knowledge distilationtransfer learningBetter and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification arXiv, 2019. [code], Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems. A tag already exists with the provided branch name. As we know, transformers were an alternative to recurrent neural networks (RNN) in the sense that they allowed non-adjacent tokens to be processed together as well. arXiv, 2022. arXiv, 2022. [pdf] arXiv, 2021. [pdf] For more information on the dataset, type help abalone_dataset in the command line.. Wenbo Jiang, Tianwei Zhang, Han Qiu, Hongwei Li, and Guowen Xu. [pdf], Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World. Charles Jin, Melinda Sun, and Martin Rinard. Yuntao Liu, Ankit Mondal, Abhishek Chakraborty, Michael Zuzak, Nina Jacobsen, Daniel Xing, and Ankur Srivastava. Xuankai Liu, Fengting Li, Bihan Wen, and Qi Li. [pdf] Guanhong Tao, Guangyu Shen, Yingqi Liu, Shengwei An, Qiuling Xu, Shiqing Ma, Pan Li, and Xiangyu Zhang. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Python Tutorial: Working with CSV file for Data Science. We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. Zhaoyuan Yang, Naresh Iyer, Johan Reimann, and Nurali Virani. arXiv, 2022. [code], WeDef: Weakly Supervised Backdoor Defense for Text Classification. It consists of a bunch of tutorial notebooks for various deep learning topics. arXiv, 2021. [pdf] Transfer Learning [10, 11] is another interesting paradigm to prevent overfitting. [pdf] To combat this, XLNet proposes a technique called Permutation Language Modeling during the pre-training phase. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.. Reinforcement learning differs from supervised learning If nothing happens, download Xcode and try again. Zhiyuan Zhang, Lingjuan Lyu, Weiqiang Wang, Lichao Sun, and Xu Sun. Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. [pdf], Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks. [pdf] [pdf], Can You Really Backdoor Federated Learning? David Marco Sommer, Liwei Song, Sameer Wagh, and Prateek Mittal. Each directory has a requirements.txt describing the minimal dependencies required to run the notebooks in that directory. [pdf] Arezoo Rajabi, Bhaskar Ramasubramanian, and Radha Poovendran. [pdf], BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements. Our approach first feeds the visible patches into the encoder, extracting the representations. Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, and Tom Goldstein. Zhenting Wang, Hailun Ding, Juan Zhai, and Shiqing Ma. [pdf], Backdoor Attacks on Self-Supervised Learning. [link], Triggerless Backdoor Attack for NLP Tasks with Clean Labels. [link], BDDR: An Effective Defense Against Textual Backdoor Attacks. arXiv, 2022. [pdf], Stealthy and Flexible Trojan in Deep Learning Framework. Adnan Siraj Rakin, Zhezhi He, and Deliang Fan. Zhiyuan Zhang, Xuancheng Ren, Qi Su, Xu Sun, and Bin He. arXiv, 2022. [pdf], Light Can Hack Your Face! arXiv, 2021. [pdf], Backdoor Defense via Decoupling the Training Process. arXiv, 2021. [code], Manipulating SGD with Data Ordering Attacks. [pdf] The loss is also calculated accordingly for the combined tasks, uses the output of previous tasks for the next task incrementally. Kun Shao, Junan Yang, Yang Ai, Hui Liu, and Yu Zhang. Reena Zelenkova, Jack Swallow, M. A. P. Chamikara, Dongxi Liu, Mohan Baruwal Chhetri, Seyit Camtepe, Marthie Grobler, and Mahathir Almashor. To install these dependencies with pip, you can issue pip3 install -r requirements.txt. [pdf] M. Caner Tol, Saad Islam, Berk Sunar, and Ziming Zhang. [link], Adversarial Neuron Pruning Purifies Backdoored Deep Models. [pdf], Compression-Resistant Backdoor Attack against Deep Neural Networks. Antonio Emanuele Cin, Kathrin Grosse, Sebastiano Vascon, Ambra Demontis, Battista Biggio, Fabio Roli, and Marcello Pelillo. Autoencoders are fast becoming one of the most exciting areas of research in machine learning. This category only includes cookies that ensures basic functionalities and security features of the website. Thats primarily the reason weve seen a lot of research in text classification. arXiv, 2021. [pdf] Panagiota Kiourti, Kacper Wardega, Susmit Jha, and Wenchao Li. [Link], Blind Backdoors in Deep Learning Models. These cookies will be stored in your browser only with your consent. [code], Anti-Backdoor Learning: Training Clean Models on Poisoned Data. arXiv, 2021. [pdf], BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models. [pdf] [link] PySyft is intended to ensure private, secure deep learning across servers and agents using encrypted computation. Sakshi Udeshi, Shanshan Peng, Gerald Woo, Lionell Loh, Louth Rawshan, and Sudipta Chattopadhyay. arXiv, 2018. Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, and Jun Zhu. Jiyang Guan, Zhuozhuo Tu, Ran He, and Dacheng Tao.
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