In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Artificial Neural Networks have been very successfully used in several machine learning applications. They are often the building blocks when building deep learning systems. We discuss the hypothesis, training with backpropagation, update methods, regularization techniques.
This presentation is a part of ML Course and this deals with some of the basic concepts such as different types of learning, definitions of classification and regression, decision surfaces etc. This slide set also outlines the Perceptron Learning algorithm as a starter to other complex models to follow in the rest of the course.
Discusses the concept of Language Models in Natural Language Processing. The n-gram models, markov chains are discussed. Smoothing techniques such as add-1 smoothing, interpolation and discounting methods are addressed.
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Chapter8Hakky St
This is the documentation of the study-meeting in lab.
Tha book title is "Hands-On Machine Learning with Scikit-Learn and TensorFlow" and this is the chapter 8.
Overview of TensorFlow For Natural Language Processingananth
TensorFlow open sourced recently by Google is one of the key frameworks that support development of deep learning architectures. In this slideset, part 1, we get started with a few basic primitives of TensorFlow. We will also discuss when and when not to use TensorFlow.
Artificial Neural Networks have been very successfully used in several machine learning applications. They are often the building blocks when building deep learning systems. We discuss the hypothesis, training with backpropagation, update methods, regularization techniques.
This presentation is a part of ML Course and this deals with some of the basic concepts such as different types of learning, definitions of classification and regression, decision surfaces etc. This slide set also outlines the Perceptron Learning algorithm as a starter to other complex models to follow in the rest of the course.
Discusses the concept of Language Models in Natural Language Processing. The n-gram models, markov chains are discussed. Smoothing techniques such as add-1 smoothing, interpolation and discounting methods are addressed.
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Chapter8Hakky St
This is the documentation of the study-meeting in lab.
Tha book title is "Hands-On Machine Learning with Scikit-Learn and TensorFlow" and this is the chapter 8.
Overview of TensorFlow For Natural Language Processingananth
TensorFlow open sourced recently by Google is one of the key frameworks that support development of deep learning architectures. In this slideset, part 1, we get started with a few basic primitives of TensorFlow. We will also discuss when and when not to use TensorFlow.
In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
Context-aware preference modeling with factorizationBalázs Hidasi
This talk was presented at the Doctoral Symposium of RecSys'15. It is a summary of the core part of my PhD research in the last few years. The research revolves around solving the implicit feedback based context-aware recommendation problem with factorization.
Associated paper: http://dl.acm.org/citation.cfm?id=2796543
Details of presented algorithms/methods (public versions available on http://hidasi.eu):
iTALS: http://link.springer.com/chapter/10.1007/978-3-642-33486-3_5
iTALSx: http://www.infocommunications.hu/documents/169298/1025723/InfocomJ_2014_4_5_Hidasi.pdf
ALS-CG/CD: http://link.springer.com/article/10.1007/s10115-015-0863-2
GFF: http://link.springer.com/article/10.1007/s10618-015-0417-y
Ranking and Diversity in Recommendations - RecSys Stammtisch at SoundCloud, B...Alexandros Karatzoglou
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This presentation contains the main points of my recommender systems related research. It describes the arc of my research starting from improving matrix factorization, through the developement of my context-aware algorithms & addressing scalability issues to developing a general factorization framework & dealing with context dimension modeling. The slides were presented at the Delft University of Technology where I was invited to give this introductory talk as part of the collaboration between participiants of the CrowdRec project. The presentation was given on 11th April 2014.
In this presentation we describe the formulation of the HMM model as consisting of states that are hidden that generate the observables. We introduce the 3 basic problems: Finding the probability of a sequence of observation given the model, the decoding problem of finding the hidden states given the observations and the model and the training problem of determining the model parameters that generate the given observations. We discuss the Forward, Backward, Viterbi and Forward-Backward algorithms.
In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
Context-aware preference modeling with factorizationBalázs Hidasi
This talk was presented at the Doctoral Symposium of RecSys'15. It is a summary of the core part of my PhD research in the last few years. The research revolves around solving the implicit feedback based context-aware recommendation problem with factorization.
Associated paper: http://dl.acm.org/citation.cfm?id=2796543
Details of presented algorithms/methods (public versions available on http://hidasi.eu):
iTALS: http://link.springer.com/chapter/10.1007/978-3-642-33486-3_5
iTALSx: http://www.infocommunications.hu/documents/169298/1025723/InfocomJ_2014_4_5_Hidasi.pdf
ALS-CG/CD: http://link.springer.com/article/10.1007/s10115-015-0863-2
GFF: http://link.springer.com/article/10.1007/s10618-015-0417-y
Ranking and Diversity in Recommendations - RecSys Stammtisch at SoundCloud, B...Alexandros Karatzoglou
Slides from my talk at the RecSys Stammtisch at SoundCloud in Berlin. The presentation is split in two part one focusing on ranking and relevance and one on diversity and how to achieve it using genres. We introduce a novel diversity metric called Binomial Diversity.
Utilizing additional information in factorization methods (research overview,...Balázs Hidasi
This presentation contains the main points of my recommender systems related research. It describes the arc of my research starting from improving matrix factorization, through the developement of my context-aware algorithms & addressing scalability issues to developing a general factorization framework & dealing with context dimension modeling. The slides were presented at the Delft University of Technology where I was invited to give this introductory talk as part of the collaboration between participiants of the CrowdRec project. The presentation was given on 11th April 2014.
In this presentation we describe the formulation of the HMM model as consisting of states that are hidden that generate the observables. We introduce the 3 basic problems: Finding the probability of a sequence of observation given the model, the decoding problem of finding the hidden states given the observations and the model and the training problem of determining the model parameters that generate the given observations. We discuss the Forward, Backward, Viterbi and Forward-Backward algorithms.
This presentation is for my Seminar Course at the University of Tehran. in this presentation, I will introduce some of the newest and also exciting developments in Generative Adversarial Networks.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Ryohei Suzuki and Takeo Igarashi, Collaborative 3D Modeling by the Crowd, in Proceedings of the 43rd International Conference on Graphics, Visualization & Human-computer Interaction (GI 2017)
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In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
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Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
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3. Why GAN?
• GANs can learn to mimic any distribution and generate data
• The data may be images, speech or music
• The outputs from GANs are found to be quite realistic and impressive
• Thus, GANs have a number of applications: From being a feature in products like
Photoshop to generating synthetic datasets for image augmentation
10. Different Variants of GAN
Ref: https://github.com/lukedeo/keras-acgan/blob/master/acgan-analysis.ipynb
11. Cycle GAN (2017)
• Original Paper: “Unpaired Image-to-Image Translation using Cycle-Consistent
Adversarial Networks”, Zhu et al
12. Image to Image Translation
• Image to image translation is aimed at finding a mapping
between an input image (X) and its corresponding output
image (Y), where the pair X, Y are provided in the dataset
• This assumes that we are provided with such a labelled
dataset with pairings
• CycleGAN attempts to find a mapping between images from
source and target domains in the absence of paired
examples
Learn G: X → Y such that the distribution of images from G(X) is
indistinguishable from the distribution Y using an adversarial
loss.
Couple this with an inverse mapping F: Y → X and enforce a
cycle consistency loss to enforce F(G(X)) ≈ X
14. Cycle GAN: Objective Function
• Two discriminators: Dx and Dy where Dx aims to distinguish between images {x}
and translated images {F(y)}. In the same way Dy aims to discriminate between {y}
and {G(x)}
• The objective function has 2 parts representing the losses:
• adversarial losses for matching the distribution of generated images to the data distribution
in the target domain
• Cycle consistency losses that prevent the learned mappings G and F from contradicting each
other
16. Exercises
• Go through the original paper and answer the following:
• How is the model evaluated? What are the metrics?
• What are the main applications discussed in the paper?
• What are the limitations and future work?
17. SAGAN (2018) Zhang et al Abstract
• GANs often use a CNN as a generator
• CNNs capture short range dependencies very well (local receptive fields) but not
effective to capture long distance correlations
• Self Attention Generative Adversarial Networks (SAGAN) is aimed at generating
images that take in to account both short and long distance dependencies in the
source images