2015. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. However, other neuroimaging modalities such as MRI are used for epileptic seizures detection. Stochastic backpropagation and approximate inference in deep generative models. AE is an unsupervised machine learning model for which the input is the same as output [30,31,32,33]. 2019. Covert I.C., Krishnan B., Najm I., Zhan J., Shore M., Hixson J., Po M.J. Temporal graph convolutional networks for automatic seizure detection; Proceedings of the Machine Learning for Healthcare Conference; Online. DOI:http://dx.doi.org/10.3115/v1/d14-1181 arxiv:1408.5882, Jingzhou Liu, Wei Cheng Chang, Yuexin Wu, and Yiming Yang. Another drawback of RNN is the vanishing gradient problem [30,31,32,33]. Gpu kernels for block-sparse weights. Table A1 shows the detailed summary of DL methods employed for automated detection of epileptic seizures. Retrieved from https://arXiv:1510.03820. Relationship between Quality of Life and the Complexity of Default Mode Network in Resting State Functional Magnetic Resonance Image in Down Syndrome. OpenAI Blog 1, 8 (2019), 9. 2004. Awesome Incremental Learning / Lifelong learning Survey. In 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). Unsupervised learning methods were applied to generate bounding box scales and ratios directly from training data. Self-Supervised Semi-Supervised Learning Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer. In Proceedings of the International Joint Conference on Neural Networks (IJCNN17). The performance of the model is evaluated using accuracy, sensitivity, and specificity. The RNN module consists of a unilateral GRU layer that extracts the temporal feature of epileptic seizures, which are finally classified using an FC layer. Stacking the RBMs forms a DBN; RBM is the building block of DBN. Datasets with non-MRI modalities are not available, and this has led to limited research in this area. FOIA Neural machine translation by jointly learning to align and translate. Cao J., Zhu J., Hu W., Kummert A. Epileptic Signal Classification with Deep EEG Features by Stacked CNNs. Deep learning for electroencephalogram (EEG) classification tasks: A review. Convolutional networks use filters convolved with input patterns instead of multiplying a weight vector (matrix), which reduces the number of trainable parameters dramatically. The most important superiority of 1D to 2D architectures is the possibility of employing pooling and convolutional layers with a larger size. [49] suggested this network to solve image classification problems, and then quickly used similar networks for different tasks such as medical image classification, in an effort to obviate the difficulties of previous networks and solve more intricate problems with better performance. Conceptually situated between supervised and unsupervised learning, it permits harnessing the large amounts of unlabelled data available in many use cases in combination with typically smaller sets of The EEG signals were digitized at a sample rate of 173.61 Hz by 12-bit A/D converter. Hosseini et al. In Proceedings of the International Conference on Machine Learning. Reading list for research topics in multimodal machine learning. Character-Aware neural language models. Retrieved from https://arXiv:1806.09828. DeepMeSH: Deep semantic representation for improving large-scale MeSH indexing. Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. Transforming auto-encoders. Alert messages may be generated to the family, relatives, the concerned hospital, and doctor in the detection of epileptic seizures through the handheld devices or wearables, and thus the patient can be provided with proper treatment in time. Semi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled data to perform certain learning tasks. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. A self-training-based label propagation method was proposed, and it outperformed supervised learning methods in which unlabeled samples were neglected. Explaining and harnessing adversarial examples. So far, little research has been accomplished on the implementation of ResNet networks to diagnose epilepsy, but this may grow significantly in the coming days. Hardware used for epileptic seizures detection is provided in Section 4. DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2025 March 2016; pp. Sketch of accuracy (%) obtained by authors using RNN models for seizure detection. Paul Neculoiu, Maarten Versteegh, and Mihai Rotaru. In Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB15). In [116], a high-performance automated EEG analysis system based on principles of machine learning and big data is presented, which consists of several parts. However, in DL, the extraction of features and classification are entirely automated. TwinBERT: Distilling knowledge to twin-structured BERT models for efficient retrieval. Afterwards, Han et al. RaviPrakash H., Korostenskaja M., Castillo E.M., Lee K.H., Salinas C.M., Baumgartner J., Anwar S.M., Spampinato C., Bagci U. 2017. : meta learning |, Thanks for your valuable contribution to the research community. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Therefore, noise removal can be omitted in many applications. Glue: A multi-task benchmark and analysis platform for natural language understanding. International Conference on Intelligent Computing. Everything about Transfer Learning. 2427 January 2018; pp. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Prentice Hall, NJ. Comput. 27562759. In another work, Rosas-Romero et al. Craley J., Johnson E., Jouny C., Venkataraman A. Pranav Rajpurkar, Robin Jia, and Percy Liang. 2020. Thomas J., Comoretto L., Jin J., Dauwels J., Cash S.S., Westover M.B. The authors in [51] presented a new 2D-CNN model that can extract the spectral and temporal characteristics of EEG signals and used them to learn the general structure of seizures. Do Explanations make VQA Models more Predictable to a Human? 2016. MixText: Linguistically-informed interpolation of hidden space for semi-supervised text classification. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. Hussein R., Palangi H., Ward R.K., Wang Z.J. Boyuan Pan, Yazheng Yang, Zhou Zhao, Yueting Zhuang, Deng Cai, and Xiaofei He. Character-aware neural language models. 2019. Learning to Communicate with Deep Multi-agent Reinforcement Learning, NIPS 2016. 910 March 2018; Los Alamitos, CA, USA: Institute of Electrical and Electronics Engineers (IEEE); 2018. pp. Finally, the proposed approach demonstrated better performance. 2014. Instead, inexpensive weak labels are employed with the Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of todays Fourth Industrial Revolution (4IR or Industry 4.0). In the proposed method, during the preprocessing, the input signals are split into time windows and spectrogram are obtained from them. FPGA Implementation of EEG Signal Analysis System for the Detection of epileptic seizure; Proceedings of the 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET); Hammamet, Tunisia. 2018. Retrieved from https://arXiv:1708.01353. The .gov means its official. MIT Press, 5754--5764. Background (An illustrative example of pool-based active learning. Fang Z., Leung H., Choy C.S. Effective approaches to attention-based neural machine translation. OpenAI Blog 1, 8 (2019), 9. Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2016. Wei Z., Zou J., Zhang J., Xu J. kaggle.[n. Federal government websites often end in .gov or .mil. Multimodal workshops @ CVPR 2021: Multimodal Learning and Applications, Sight and Sound, Visual Question Answering, Embodied AI, Language for 3D Scenes. Alizadehsani R., Khosravi A., Roshanzamir M., Abdar M., Sarrafzadegan N., Shafie D., Khozeimeh F., Shoeibi A., Nahavandi S., Panahiazar M., et al. A generative model for category text generation. Subhabrata Mukherjee and Ahmed Hassan Awadallah. The signals from these datasets are recorded either intracranial or from the scalp of humans or animals. Abcnn: Attention-based convolutional neural network for modeling sentence pairs. 404--411. zip: Compressing text classification models. Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning. Adversarial nli: A new benchmark for natural language understanding. Pattern Recogn. Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger. Suchin Gururangan, Tam Dang, Dallas Card, and Noah A Smith. Low-Shot Learning With Imprinted Weights. Typical sketch of the 1D-CNN model that can be used for epileptic seizure detection. [128] indicated a utilitarian product for the diagnosis of epileptic seizures, which comprised the user segment and the cloud segment. 2016. NIH Public Access, 397. Talathi S.S. DL is one of the state-of-the-art fields of epileptic seizure detection that has been employed for epileptic seizure detection since 2016. National Library of Medicine In Proceedings of the IEEE International Conference on Computer Vision. Montreal, 2015. How to fine-tune BERT for text classification?. 2023 August 2017; pp. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2015. If nothing happens, download GitHub Desktop and try again. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. Methods Biomech. Reuters. 2017. Imanol Schlag, Paul Smolensky, Roland Fernandez, Nebojsa Jojic, Jrgen Schmidhuber, and Jianfeng Gao. Use Git or checkout with SVN using the web URL. Figure 1 shows that the TensorFlow and one of its high-level APIs, Keras, are widely used for epileptic seizure detection using DL in reviewed works due to their versatility and applicability. Few-shot image recognition by predicting parameters from activations. Publishers Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Single-Image-Blind-Motion-Deblurring (non-DL), Single-Image-Blind-Motion-Deblurring (DL), Defocus Deblurring and Potential Datasets, Removing camera shake from a single photograph, Single image motion deblurring using transparency, Psf estimation using sharp edge prediction, High-quality motion deblurring from a single image, Image deblurring and denoising using color priors, Efficient filter flow for space-variant multiframe blind deconvolution, Denoising vs. deblurring: HDR imaging techniques using moving cameras, Single image deblurring using motion density functions, Two-phase kernel estimation for robust motion deblurring, Space-variant single-image blind deconvolution for removing camera shake, Blind deconvolution using a normalized sparsity measure, Blur kernel estimation using the radon transform, Exploring aligned complementary image pair for blind motion deblurring, The non-parametric sub-pixel local point spread function estimation is a well posed problem, Blur-kernel estimation from spectral irregularities, Framelet-based Blind Motion deblurring from a single Image, Unnatural L0 sparse representation for natural image deblurring, Handling noise in single image deblurring using directional filters, Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty, Edge-based blur kernel estimation using patch priors, Deblurring Text Images via L0 -Regularized Intensity and Gradient Prior, Segmentation-Free Dynamic Scene Deblurring, Deblurring Low-light Images with Light Streaks, Joint depth estimation and camera shake removal from single blurry image, Hybrid Image Deblurring by Fusing Edge and Power Spectrum Information, Blind deblurring using internal patch recurrence, Burst Deblurring: Removing Camera Shake Through Fourier Burst Accumulation, Kernel Fusion for Better Image Deblurring, Blind image deblurring using dark channel prior, Robust Kernel Estimation with Outliers Handling for Image Deblurring, Blind image deconvolution by automatic gradient activation, Image deblurring via extreme channels prior, From local to global: Edge profiles to camera motion in blurred images, Project page & Results-on-benchmark-datasets, Blind Image Deblurring with Outlier Handling, Self-paced Kernel Estimation for Robust Blind Image Deblurring, Convergence Analysis of MAP based Blur Kernel Estimation, Deblurring Natural Image Using Super-Gaussian Fields, Blind Image Deblurring With Local Maximum Gradient Prior, Phase-Only Image Based Kernel Estimation for Single Image Blind Deblurring, A Variational EM Framework With Adaptive Edge Selection for Blind Motion Deblurring, Graph-Based Blind Image Deblurring From a Single Photograph, Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior, OID: Outlier Identifying and Discarding in Blind Image Deblurring, Enhanced Sparse Model for Blind Deblurring, Polyblur: Removing mild blur by polynomial reblurring, Fast blind deconvolution using a deeper sparse patch-wise maximum 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Deblurgan: Blind motion deblurring using conditional adversarial networks, Gated Fusion Network for Joint Image Deblurring and Super-Resolution, Gyroscope-Aided Motion Deblurring with Deep Networks, Dynamic Scene Deblurring With Parameter Selective Sharing and Nested Skip Connections, Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring, Unsupervised Domain-Specific Deblurring via Disentangled Representations, Douglas-Rachford Networks: Learning Both the Image Prior and Data Fidelity Terms for Blind Image Deconvolution, DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better, Blind image deconvolution using deep generative priors, Tell Me Where It is Still Blurry: Adversarial Blurred Region Mining and Refining, Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement, Learning to Deblur Face Images via Sketch Synthesis, Region-Adaptive Dense Network for Efficient Motion Deblurring, Neural Blind Deconvolution Using Deep Priors, Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring, Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training, Deblurring using Analysis-Synthesis Networks Pair, Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training, Efficient and Interpretable Deep Blind Image Deblurring Via Algorithm Unrolling, Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks, Dark and bright channel prior embedded network for dynamic scene deblurring, Dynamic Scene Deblurring by Depth Guided Model, Scale-Iterative Upscaling Network for Image Deblurring, Human Motion Deblurring using Localized Body Prior, Physics-Based Generative Adversarial Models for Image Restoration and Beyond, Blind Image Deconvolution using Deep Generative Priors, Blur Invariant Kernel-Adaptive Network for Single Image Blind deblurring, Exposure Trajectory 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Knowledge Distillation, Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition, Clean Images are Hard to Reblur: A New Clue for Deblurring, Deep residual fourier transformation for single image deblurring, Single-image deblurring with neural networks: A comparative survey, Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding, Deep Robust Image Deblurring via Blur Distilling and Information Comparison in Latent Space, Deep Feature Prior Guided Face Deblurring, Restormer: Efficient transformer for high-resolution image restoration, Maxim: Multi-axis mlp for image processing, Uformer: A general u-shaped transformer for image restoration, XYDeblur: Divide and Conquer for Single Image Deblurring, Deblur-NeRF: Neural Radiance Fields From Blurry Images, All-In-One Image Restoration for Unknown Corruption, Exploring and Evaluating Image Restoration Potential in Dynamic Scenes, Deep Generalized Unfolding Networks for Image 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push the limits of efficient fft-based image deconvolution, Deep Mean-Shift Priors for Image Restoration, Modeling Realistic Degradations in Non-Blind Deconvolution, Non-blind Deblurring: Handling Kernel Uncertainty with CNNs, Learning Data Terms for Non-blind Deblurring, Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation, Deep decoder: Concise image representations from untrained non-convolutional networks, Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels, Image deconvolution with deep image and kernel priors, Denoising prior driven deep neural network for image restoration, Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring, Deep Learning for Handling Kernel/model Uncertainty in Image Deconvolution, End-to-end interpretable learning of non-blind image deblurring, Bp-dip: A backprojection based deep image prior, Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring, Learning deep gradient descent optimization for 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Prior, Multi-Shot Imaging: Joint Alignment, Deblurring and Resolution Enhancement, Gyro-Based Multi-Image Deconvolution for Removing Handshake Blur, Hand-held video deblurring via efficient fourier aggregation, Removing camera shake via weighted fourier burst accumulation, Generalized Video Deblurring for Dynamic Scenes, Intra-Frame Deblurring by Leveraging Inter-Frame Camera Motion, Simultaneous stereo video deblurring and scene flow estimation, Deep Video Deblurring for Hand-Held Cameras, Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel, Online Video Deblurring via Dynamic Temporal Blending Network, Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks, Joint Blind Motion Deblurring and Depth Estimation of Light Field, Dynamic Video Deblurring using a Locally Adaptive Linear Blur Model, Reblur2deblur: Deblurring videos via self-supervised learning, LSD-Joint Denoising and Deblurring of Short and Long Exposure Images with 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Correspondence for Video Deblurring, Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes, Video Deblurring via Spatiotemporal Pyramid Network and Adversarial Gradient Prior, Multi-Scale Separable Network for Ultra-High-Definition Video Deblurring, Deep Recurrent Neural Network with Multi-Scale Bi-Directional Propagation for Video Deblurring, Efficient Video Deblurring Guided by Motion Magnitude, Spatio-Temporal Deformable Attention Network for Video Deblurring, ERDN: Equivalent Receptive Field Deformable Network for Video Deblurring, DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting, Towards Real-World Video Deblurring by Exploring Blur Formation Process, Real-Time Video Deblurring via Lightweight Motion Compensation, Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network, NTIRE 2019 Challenge on Video Deblurring: Methods and Results, NTIRE 2019 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