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Basics of RNNs and its applications with following papers: - Generating Sequences With Recurrent Neural Networks, 2013 - Show and Tell: A Neural Image Caption Generator, 2014 - Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, 2015 - DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2015 - Deep Tracking- Seeing Beyond Seeing Using Recurrent Neural Networks, 2016 - Robust Modeling and Prediction in Dynamic Environments Using Recurrent Flow Networks, 2016 - Social LSTM- Human Trajectory Prediction in Crowded Spaces, 2016 - DESIRE- Distant Future Prediction in Dynamic Scenes with Interacting Agents, 2017 - Predictive State Recurrent Neural Networks, 2017
RNN and its applications
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Sungjoon Choi
Uncertainty in Deep Learning, Gal (2016) Representing Inferential Uncertainty in Deep Neural Networks Through Sampling, McClure & Kriegeskorte (2017) Uncertainty-Aware Reinforcement Learning from Collision Avoidance, Khan et al. (2016) Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, Lakshminarayanan et al. (2017) What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, Kendal & Gal (2017) Uncertainty-Aware Learning from Demonstration Using Mixture Density Networks with Sampling-Free Variance Modeling, Choi et al. (2017) Bayesian Uncertainty Estimation for Batch Normalized Deep Networks, Anonymous (2018)
Modeling uncertainty in deep learning
Modeling uncertainty in deep learning
Sungjoon Choi
Slides introducing GPLVM
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Sungjoon Choi
1. Y. Gal, Uncertainty in Deep Learning, 2016 2. P. McClure, Representing Inferential Uncertainty in Deep Neural Networks Through Sampling, 2017 3. G. Khan et al., Uncertainty-Aware Reinforcement Learning from Collision Avoidance, 2016 4. B. Lakshminarayanan et al., Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, 2017 5. A. Kendal and Y. Gal, What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, 2017 6. S. Choi et al., Uncertainty-Aware Learning from Demonstration Using Mixture Density Networks with Sampling-Free Variance Modeling, 2017 7. Anonymous, Bayesian Uncertainty Estimation for Batch Normalized Deep Networks, 2017
Uncertainty Modeling in Deep Learning
Uncertainty Modeling in Deep Learning
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1. Generative Model 2. Domain Adaptation 3. Meta-Learning 4. Uncertainty in Deep Learning
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LevDNN
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Basics of RNNs and its applications with following papers: - Generating Sequences With Recurrent Neural Networks, 2013 - Show and Tell: A Neural Image Caption Generator, 2014 - Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, 2015 - DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2015 - Deep Tracking- Seeing Beyond Seeing Using Recurrent Neural Networks, 2016 - Robust Modeling and Prediction in Dynamic Environments Using Recurrent Flow Networks, 2016 - Social LSTM- Human Trajectory Prediction in Crowded Spaces, 2016 - DESIRE- Distant Future Prediction in Dynamic Scenes with Interacting Agents, 2017 - Predictive State Recurrent Neural Networks, 2017
RNN and its applications
RNN and its applications
Sungjoon Choi
Uncertainty in Deep Learning, Gal (2016) Representing Inferential Uncertainty in Deep Neural Networks Through Sampling, McClure & Kriegeskorte (2017) Uncertainty-Aware Reinforcement Learning from Collision Avoidance, Khan et al. (2016) Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, Lakshminarayanan et al. (2017) What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, Kendal & Gal (2017) Uncertainty-Aware Learning from Demonstration Using Mixture Density Networks with Sampling-Free Variance Modeling, Choi et al. (2017) Bayesian Uncertainty Estimation for Batch Normalized Deep Networks, Anonymous (2018)
Modeling uncertainty in deep learning
Modeling uncertainty in deep learning
Sungjoon Choi
Slides introducing GPLVM
Gaussian Process Latent Variable Model
Gaussian Process Latent Variable Model
Sungjoon Choi
1. Y. Gal, Uncertainty in Deep Learning, 2016 2. P. McClure, Representing Inferential Uncertainty in Deep Neural Networks Through Sampling, 2017 3. G. Khan et al., Uncertainty-Aware Reinforcement Learning from Collision Avoidance, 2016 4. B. Lakshminarayanan et al., Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, 2017 5. A. Kendal and Y. Gal, What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, 2017 6. S. Choi et al., Uncertainty-Aware Learning from Demonstration Using Mixture Density Networks with Sampling-Free Variance Modeling, 2017 7. Anonymous, Bayesian Uncertainty Estimation for Batch Normalized Deep Networks, 2017
Uncertainty Modeling in Deep Learning
Uncertainty Modeling in Deep Learning
Sungjoon Choi
1. Generative Model 2. Domain Adaptation 3. Meta-Learning 4. Uncertainty in Deep Learning
Recent Trends in Deep Learning
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Seminar@Google
Leveraged Gaussian Process
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Sungjoon Choi
Choi et. al., 'Scalable Robust Learning from Demonstration with Leveraged Deep Neural Network', IROS, 2017
LevDNN
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Sungjoon Choi
Presentation slides for IROS 2017 Choi et. al., 'Scalable Robust Learning from Demonstration with Leveraged Deep Neural Network', IROS, 2017
IROS 2017 Slides
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Sungjoon Choi
1. Ben-David, Shai, et al. "Analysis of representations for domain adaptation." NIPS, 2007 2. Ganin, Yaroslav, et al. "Domain-adversarial training of neural networks." JMLR, 201`6 3. Konstantinos Bousmalis, et al."Domain Separation Networks", NIPS, 2016 4. Tzeng, Eric, et al. "Adversarial discriminative domain adaptation." arXiv, 2017
Domain Adaptation Methods
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Slides introducing Yunzhu Li, Jiaming Song, Stefano Ermon, “Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs”, ArXiv, 2017 + Pollicy Gradient + InfoGAN + WGAN
InfoGAIL
InfoGAIL
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In this slide, we investigate the relationship between Bellman equation and Markov decision processes (MDPs). While the principle of optimality directly gives us the relationships, we derive this connection by solving the KKT conditions of infinite horizon optimal control problems.
Connection between Bellman equation and Markov Decision Processes
Connection between Bellman equation and Markov Decision Processes
Sungjoon Choi
Basic definitions and theorems for constructing RKHS Random processes to Gaussian processes Leveraged Gaussian processes and Leverage optimization
Kernel, RKHS, and Gaussian Processes
Kernel, RKHS, and Gaussian Processes
Sungjoon Choi
Introduces following IRL papers 2000 Algorithms for Inverse Reinforcement Learning 2004 Apprenticeship Learning via Inverse Reinforcement Learning 2006 Maximum Margin Planning 2010 Maximum Entropy Inverse Reinforcement Learning 2011 Nonlinear Inverse Reinforcement Learning with Gaussian Processes 2015 Maximum Entropy Deep Inverse Reinforcement Learning
Inverse Reinforcement Learning Algorithms
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Sungjoon Choi
CNN is not just used for efficient feature extractor but this paper finds an analogy between operations in CNN and value iteration algorithm in reinforcement learning.
Value iteration networks
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Deep Learning in Robotics - There are two major branches in applying deep learning techniques in robotics. - One is to combine DL with Q learning algorithms. For example, awesome work on playing Atari games done by deep mind is a representative study. While this approach can effectively handle several problems that can hardly be solved via traditional methods, these methods are not appropriate for real manipulators as it often requires an enormous number of training data. - The other branch of work uses a concept of guided policy search. It combines trajectory optimization methods with supervised learning algorithm like CNNs to come up with a robust 'policy' function that can actually be used in real robots, e.g., Baxter of PR2.
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Deep Learning in Computer Vision Applications 1. Basics on Convolutional Neural Network 2. Otimization Methods (Momentum, AdaGrad, RMSProp, Adam, etc) 3. Semantic Segmentation 4. Class Activation Map 5. Object Detection 6. Recurrent Neural Network 7. Visual Question and Answering 8. Word2Vec (Word embedding) 9. Image Captioning
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N-Grade deals with the maintenance of university, department, faculty, student information within the university. N-Grade is an automation system, which is used to store the department, faculty, student, courses and information of a university. Starting from registration of a new student in the university, it maintains all the details regarding the attendance and marks of the students. The project deals with retrieval of information through an INTRANET based campus wide portal. It collects related information from all the departments of an organization and maintains files, which are used to generate reports in various forms to measure individual and overall performance of the students.
University management System project report..pdf
University management System project report..pdf
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Increased aeration of the soil; Stabilized soil structure; Higher and more diversified crop production; Better workability of the land; Earlier planting dates; Reduction of peak discharges by an increased temporary storage of water in the soil decomposition of organic matter; soil subsidence; reduced irrigation efficiency; increased risk of drought. excessive leaching of valuable nutrients from the soil; downstream environmental damage by salty or otherwise polluted drainage water; the presence of ditches, canals, and structures impending accessibility and interfering with other infrastructural elements of the land.
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chapter 5.pptx: drainage and irrigation engineering
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Más contenido relacionado
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1. Ben-David, Shai, et al. "Analysis of representations for domain adaptation." NIPS, 2007 2. Ganin, Yaroslav, et al. "Domain-adversarial training of neural networks." JMLR, 201`6 3. Konstantinos Bousmalis, et al."Domain Separation Networks", NIPS, 2016 4. Tzeng, Eric, et al. "Adversarial discriminative domain adaptation." arXiv, 2017
Domain Adaptation Methods
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Sungjoon Choi
Slides introducing Yunzhu Li, Jiaming Song, Stefano Ermon, “Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs”, ArXiv, 2017 + Pollicy Gradient + InfoGAN + WGAN
InfoGAIL
InfoGAIL
Sungjoon Choi
In this slide, we investigate the relationship between Bellman equation and Markov decision processes (MDPs). While the principle of optimality directly gives us the relationships, we derive this connection by solving the KKT conditions of infinite horizon optimal control problems.
Connection between Bellman equation and Markov Decision Processes
Connection between Bellman equation and Markov Decision Processes
Sungjoon Choi
Basic definitions and theorems for constructing RKHS Random processes to Gaussian processes Leveraged Gaussian processes and Leverage optimization
Kernel, RKHS, and Gaussian Processes
Kernel, RKHS, and Gaussian Processes
Sungjoon Choi
Introduces following IRL papers 2000 Algorithms for Inverse Reinforcement Learning 2004 Apprenticeship Learning via Inverse Reinforcement Learning 2006 Maximum Margin Planning 2010 Maximum Entropy Inverse Reinforcement Learning 2011 Nonlinear Inverse Reinforcement Learning with Gaussian Processes 2015 Maximum Entropy Deep Inverse Reinforcement Learning
Inverse Reinforcement Learning Algorithms
Inverse Reinforcement Learning Algorithms
Sungjoon Choi
CNN is not just used for efficient feature extractor but this paper finds an analogy between operations in CNN and value iteration algorithm in reinforcement learning.
Value iteration networks
Value iteration networks
Sungjoon Choi
Deep Learning in Robotics - There are two major branches in applying deep learning techniques in robotics. - One is to combine DL with Q learning algorithms. For example, awesome work on playing Atari games done by deep mind is a representative study. While this approach can effectively handle several problems that can hardly be solved via traditional methods, these methods are not appropriate for real manipulators as it often requires an enormous number of training data. - The other branch of work uses a concept of guided policy search. It combines trajectory optimization methods with supervised learning algorithm like CNNs to come up with a robust 'policy' function that can actually be used in real robots, e.g., Baxter of PR2.
Deep Learning in Robotics
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Sungjoon Choi
Deep Learning in Computer Vision Applications 1. Basics on Convolutional Neural Network 2. Otimization Methods (Momentum, AdaGrad, RMSProp, Adam, etc) 3. Semantic Segmentation 4. Class Activation Map 5. Object Detection 6. Recurrent Neural Network 7. Visual Question and Answering 8. Word2Vec (Word embedding) 9. Image Captioning
Deep Learning in Computer Vision
Deep Learning in Computer Vision
Sungjoon Choi
Semantic Segmentation Methods FCN, DeconvNet, and DeepLab with Atrous Convolution
Semantic Segmentation Methods using Deep Learning
Semantic Segmentation Methods using Deep Learning
Sungjoon Choi
Object Detection Methods R-CNN, Fast R-CNN, SPPnet, and Faster R-CNN
Object Detection Methods using Deep Learning
Object Detection Methods using Deep Learning
Sungjoon Choi
CNN Tutorial with brief description of AlexNet, VGG, GoogLeNet, and ResNet.
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Image Classification using Custom Dataset (No More MNIST!)
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TensorFlow Tutorial Part2
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Robot, Learning from Data 1. Direct Policy Learning in RKHS with learning theory 2. Inverse Reinforcement Learning Methods Sungjoon Choi (sungjoon.choi@cpslab.snu.ac.kr)
Robot, Learning From Data
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Sungjoon Choi
Learning Contact-Rich Manipulation Skills with Guided Policy Search, Sergey Levine, Nolan Wagener, and Pieter Abbeel in ICRA 2015
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Recent Trends in Neural Net Policy Learning
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InfoGAIL
Connection between Bellman equation and Markov Decision Processes
Connection between Bellman equation and Markov Decision Processes
Kernel, RKHS, and Gaussian Processes
Kernel, RKHS, and Gaussian Processes
Inverse Reinforcement Learning Algorithms
Inverse Reinforcement Learning Algorithms
Value iteration networks
Value iteration networks
Deep Learning in Robotics
Deep Learning in Robotics
Deep Learning in Computer Vision
Deep Learning in Computer Vision
Semantic Segmentation Methods using Deep Learning
Semantic Segmentation Methods using Deep Learning
Object Detection Methods using Deep Learning
Object Detection Methods using Deep Learning
CNN Tutorial
CNN Tutorial
TensorFlow Tutorial Part2
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TensorFlow Tutorial Part1
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N-Grade deals with the maintenance of university, department, faculty, student information within the university. N-Grade is an automation system, which is used to store the department, faculty, student, courses and information of a university. Starting from registration of a new student in the university, it maintains all the details regarding the attendance and marks of the students. The project deals with retrieval of information through an INTRANET based campus wide portal. It collects related information from all the departments of an organization and maintains files, which are used to generate reports in various forms to measure individual and overall performance of the students.
University management System project report..pdf
University management System project report..pdf
Kamal Acharya
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Increased aeration of the soil; Stabilized soil structure; Higher and more diversified crop production; Better workability of the land; Earlier planting dates; Reduction of peak discharges by an increased temporary storage of water in the soil decomposition of organic matter; soil subsidence; reduced irrigation efficiency; increased risk of drought. excessive leaching of valuable nutrients from the soil; downstream environmental damage by salty or otherwise polluted drainage water; the presence of ditches, canals, and structures impending accessibility and interfering with other infrastructural elements of the land.
chapter 5.pptx: drainage and irrigation engineering
chapter 5.pptx: drainage and irrigation engineering
mulugeta48
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REFRIGERATION AND AIR CONDITIONING Vapour compression refrigeration cycle - working principle - Effect of super heat and sub cooling - performance calculations - working principle of vapour absorption system - ammonia - water, lithium bromide water systems (description only) - air conditioning system - types and working principles
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Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the start
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Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the start
KubeKraft presentation @CloudNativeHooghly
KubeKraft presentation @CloudNativeHooghly
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Unleashing the Power of the SORA AI lastest leap
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FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
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