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Semantic Segmentation Methods using Deep Learning
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Semantic Segmentation Methods FCN, DeconvNet, and DeepLab with Atrous Convolution
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TensorFlow Tutorial Part2
TensorFlow Tutorial Part2
TensorFlow Tutorial Part1
TensorFlow Tutorial Part1
Object Detection Methods using Deep Learning
Object Detection Methods using Deep Learning
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
Deep Learning in Robotics
Deep Learning in Robotics
Robot, Learning From Data
Robot, Learning From Data
Inverse Reinforcement Learning Algorithms
Inverse Reinforcement Learning Algorithms
crfasrnn_presentation
crfasrnn_presentation
#6 PyData Warsaw: Deep learning for image segmentation
#6 PyData Warsaw: Deep learning for image segmentation
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Semantic segmentation
Word Embeddings - Introduction
Word Embeddings - Introduction
Deep Learning for Computer Vision: Segmentation (UPC 2016)
Deep Learning for Computer Vision: Segmentation (UPC 2016)
Robot Era
Robot Era
Recent Trends in Neural Net Policy Learning
Recent Trends in Neural Net Policy Learning
(Semantic Web Technologies and Applications track) "A Quantitative Comparison...
(Semantic Web Technologies and Applications track) "A Quantitative Comparison...
Semantic-Aware Sky Replacement (SIGGRAPH 2016)
Semantic-Aware Sky Replacement (SIGGRAPH 2016)
Value iteration networks
Value iteration networks
Improving Spatial Codification in Semantic Segmentation
Improving Spatial Codification in Semantic Segmentation
Mehr von Sungjoon Choi
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
Hybrid computing using a neural network with dynamic external memory
Hybrid computing using a neural network with dynamic external memory
Hybrid computing using a neural network with dynamic external memory
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
Recent Trends in Deep Learning
Sungjoon Choi
Seminar@Google
Leveraged Gaussian Process
Leveraged Gaussian Process
Sungjoon Choi
Choi et. al., 'Scalable Robust Learning from Demonstration with Leveraged Deep Neural Network', IROS, 2017
LevDNN
LevDNN
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
IROS 2017 Slides
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
Domain Adaptation Methods
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
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RNN and its applications
RNN and its applications
Hybrid computing using a neural network with dynamic external memory
Hybrid computing using a neural network with dynamic external memory
Modeling uncertainty in deep learning
Modeling uncertainty in deep learning
Gaussian Process Latent Variable Model
Gaussian Process Latent Variable Model
Uncertainty Modeling in Deep Learning
Uncertainty Modeling in Deep Learning
Recent Trends in Deep Learning
Recent Trends in Deep Learning
Leveraged Gaussian Process
Leveraged Gaussian Process
LevDNN
LevDNN
IROS 2017 Slides
IROS 2017 Slides
Domain Adaptation Methods
Domain Adaptation Methods
InfoGAIL
InfoGAIL
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Hostel management system project report..pdf
Hostel management system project report..pdf
Kamal Acharya
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GAS POWER CYCLES Cycles: Otto, Diesel, Dual, Brayton - Calculation of mean effective pressure - Air standard efficiency - Comparison of cycles INTERNAL COMBUSTION ENGINES Classification - Components and their function - valve timing diagram and port timing diagram - actual and theoretical p-v diagram of two stroke and four stroke engines – carburettor - diesel pump and injector system - battery and magneto ignition system - principles of combustion and detonation in CI engines - lubrication and cooling systems - performance parameters and calculations.
Thermal Engineering Unit - I & II . ppt
Thermal Engineering Unit - I & II . ppt
DineshKumar4165
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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This presentation was presented by Ronit Banerjee, CNCF ambassador on the occasion of our offline event named as KubeKraft
KubeKraft presentation @CloudNativeHooghly
KubeKraft presentation @CloudNativeHooghly
sanyuktamishra911
This is safety stuff
Work-Permit-Receiver-in-Saudi-Aramco.pptx
Work-Permit-Receiver-in-Saudi-Aramco.pptx
JuliansyahHarahap1
Anna University Regulation 2021 - CE3404 Soil Mechanics Unit 1 solved problems.
Unit 1 - Soil Classification and Compaction.pdf
Unit 1 - Soil Classification and Compaction.pdf
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When making accessible products, where do you start, and how do you continuously drive to be better?
Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the start
Quintin Balsdon
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22-prompt engineering noted slide shown.pdf
22-prompt engineering noted slide shown.pdf
22-prompt engineering noted slide shown.pdf
203318pmpc
Air Compressors
UNIT - IV - Air Compressors and its Performance
UNIT - IV - Air Compressors and its Performance
sivaprakash250
STEAM NOZZLES AND TURBINES Flow of steam through nozzles, shapes of nozzles, effect of friction, critical pressure ratio, supersaturated flow - impulse and reaction principles, velocity diagram, work done and efficiency – types of compounding - governors. AIR COMPRESSORS Classification - working principle - type of compressors, work of compression with and without clearance - volumetric efficiency - isothermal and isentropic efficiency of reciprocating compressors - multistage air compressor with inter cooling.
Thermal Engineering -unit - III & IV.ppt
Thermal Engineering -unit - III & IV.ppt
DineshKumar4165
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Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...
Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
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Generative AI or GenAI technology based PPT
Generative AI or GenAI technology based PPT
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Hostel management system project report..pdf
Hostel management system project report..pdf
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Thermal Engineering Unit - I & II . ppt
Thermal Engineering Unit - I & II . ppt
University management System project report..pdf
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Semantic Segmentation Methods using Deep Learning
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Semantic segmentation Sungjoon Choi (sungjoon.choi@cpslab.snu.ac.kr)
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