Efficient hardware implementation of deep neural network processing Marian Verhelst
Deep learning comes with significant computational complexity, making it until recently only feasible on power-hungry server platforms. In the past years, we ...
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Introduction to Deep Learning - 12 Advanced Deep Learning Topics
Introduction to Deep Learning (I2DL) - Lecture 12 TUM Summer Semester 2020.
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Smart Inference for Multidigit Convolutional Neural Network based Barcode Decoding I ICPR - 2020
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Deep Neural Networks in Medical Imaging and Radiology
A Google TechTalk, 5/11/17, presented by Le Lu ABSTRACT: Deep Neural Networks in Medical Imaging and Radiology: Preventative and Precision Medicine ...
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Stanford Seminar - Neural Networks on Chip Design from the User Perspective
Yu Wang Tsinghua University October 9, 2019 To apply neural networks to different applications, various customized hardware architectures are proposed in the ...
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Stanford | Convolutional Neural Network for Visual Recognition | Image Classification | Lecture 2
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Neuromorphic computing with emerging memory devices
This Plenary speech was delivered by Prof. Daniele Ielmini (Politecnico Di Milano) during the first edition of Artificial Intelligence International Conference that ...
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Deep Learning and the Future of AI | Yann LeCun | Talk 1/2
LeCun has been on the editorial board of IJCV, IEEE PAMI, and IEEE Trans. Neural Networks, was program chair of CVPR'06, and is chair of ICLR 2013 and ...
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HC27-K1: Convolutional Neural Networks
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[ICIP 2020] Generalized Operational Neural Networks - Part 3 #icip2020 #icip #cvpr #iccv #eccv
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Porting a Convolutional Neural Network for Stereo Vision to Hardware | ICPR 2020
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Towards Explaining Adversarial Examples Phenomenon in Artificial Neural Networks | ICPR - 2020
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[ICIP 2020] Generalized Operational Neural Networks - Part 3.B
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NEURAL NETWORK AND LEARNING SYSTEM PROJECTS IN DELHI
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[ICIP 2020] Generalized Operational Neural Networks - Part 2 #icip2020 #icip #cvpr #iccv #eccv
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Michael Milford, Navigation Neuroscience and Neural Networks A Quest to Understand...
Intelligence and Build Better Technology for Robots and Autonomous Vehicles. A talk in STAEOnline seminar series. For slides and more information see ...
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Delving in the loss landscape to embed robust watermarks into neural networks | ICPR - 2020
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CT UNet An Improved Neural Network Based on U Net for Building Segmentation in Rem | ICPR - 2020
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Lecture 10 Training Neural Networks I
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NEURAL NETWORK AND LEARNING SYSTEMS PROJECTS IN WEST BENGAL
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Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Dis Part 5
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[NeurIPS 2020] Training Neural Networks in Low Dimensional Random Bases
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NeuralFP Out of distribution Detection using Fingerprints of Neural Networks | ICPR - 2020
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Epileptic Seizure Prediction A Semi Dilated Convolutional Neural Network | ICPR - 2020
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Introduction to Medical Image Analysis
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Module 3 Lecture 1 Neural Control A review
Lectures by Prof. Laxmidhar Behera, Department of Electrical Engineering, Indian Institute of Technology, Kanpur. For more details on NPTEL visit ...
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Yann LeCun | NYU Deep Learning | Training a Neural Network | Lec 4
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Revisiting the Training of Very Deep Neural Networks without Skip Connections
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ICML 2020 How Good is the Bayes Posterior in Deep Neural Networks Really
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Channel Planting for Deep Neural Networks Using Knowledge Distillation | ICPR - 2020
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Mendip 3D Medical Images Lossless Compressor Using Recurrent Neural Network LSTM | ICPR - 2020
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Testing Deep Neural Networks
The reliability of software that has a Deep Neural Network (DNN) as a component is urgently important today given the increasing number of critical applications ...
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Introduction to Convolutional Neural Networks for Visual Recognition Lecture 1
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Beyond the Patterns - Episode 7 - Jong Chul Ye - GAN for Medical image Reconstruction
It's a great pleasure to welcome Prof. Dr. Jong Chul Ye from KAIST for a presentation to our lab! Title: GAN for Medical Image Reconstruction Prof. Dr. Jong Chul ...
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ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the ...
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Technical Uni Munich | Introduction to Deep Learning | Recurrent Neural Networks | Lec 11
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Introduction of Neural Network Quantization & Model Compression
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ICML 2020 Intriguing generalization and simplicity of adversarially trained neural networks
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Writer Identification Using Deep Neural Networks Impact of Patch Size and Number | ICPR - 2020
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Backdoor Based Watermarks in Neural Networks with Limited Data | ICPR - 2020
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