Search results for Generative Adversarial Network

Generative Adversarial Networks – Paper Reading Road Map
...more about the technical sides of GANs, I wanted to share some papers I have read in the order that I read them. For a less technical introduction to Generative Adversarial Networks, have a look at here. Before reading these papers, I recommend you to revise the basics of deep learning if you are...https://www.kdnuggets.com/2018/10/generativeadversarialnetworkspaperreadingroadmap.html

Generative Adversarial Networks, an overview
...ating images. The following is the outline of this article A brief review of Deep Learning The image generation problem Key issue in generative tasks Generative Adversarial Networks Challenges Further reading Conclusion Sketch of a (feedforward) neural network, with input layer in brown,...https://www.kdnuggets.com/2018/01/generativeadversarialnetworksoverview.html

Intro to Adversarial Machine Learning and Generative Adversarial Networks
...ng, when we talk about generative models, we’re usually talking about one of four kinds of models: Autoregressive Reversible Variational Autoencoders Generative Adversarial Networks Autoregressive models: With autoregressive models, you generate an image by generating each pixel conditioned on the...https://www.kdnuggets.com/2019/10/adversarialmachinelearninggenerativeadversarialnetworks.html

The Rise of Generative Adversarial Networks
...ck, Generative Adversarial Networks(GANs) started a revolution in deep learning. This revolution has produced some major technological breakthroughs. Generative Adversarial Networks were introduced by Ian Goodfellow and others in the paper titled “Generative Adversarial Networks” —...https://www.kdnuggets.com/2019/04/risegenerativeadversarialnetworks.html

Deep Learning Research Review: Generative Adversarial Nets
...be looking at 3 papers that built on the pioneering work of Ian Goodfellow in 2014. Quick Summary of GANs I briefly mentioned Ian Goodfellow’s Generative Adversarial Network paper in one of my prior blog posts, 9 Deep Learning Papers You Should Know About. The basic idea of these networks is...https://www.kdnuggets.com/2016/10/deeplearningresearchreviewgenerativeadversarialnetworks.html

Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own
...generates become increasingly realistic as training progresses! Brief primer on GANs Since its inception in 2014 with Ian Goodfellow’s ‘Generative Adversarial Networks’ paper, progress with GANs has exploded and led to increasingly realistic outputs. 4.5 years of GAN progress on face...https://www.kdnuggets.com/2019/04/graduatinggansunderstandinggenerativeadversarialnetworks.html

Research Guide for Video Frame Interpolation with Deep Learning
...as the optimization strategy. The results obtained are shown below. source Frame Interpolation with MultiScale Deep Loss Functions and Generative Adversarial Networks (2019) In this paper, the authors propose a multiscale generative adversarial network for frame interpolation...https://www.kdnuggets.com/2019/10/researchguidevideoframeinterpolationdeeplearning.html

Generative Adversarial Networks – Key Milestones and State of the Art
...earn. After that, we’ll examine two promising GANs: the RadialGAN,[2]which is designed for numbers, and the StyleGAN, which is focused on images. The Generative Adversarial Network (GAN) The original GAN[3] was created by Ian Goodfellow, who described the GAN architecture in a paper published in...https://www.kdnuggets.com/2019/04/futuregenerativeadversarialnetworks.html

Generative Adversarial Neural Networks: Infinite Monkeys and The Great British Bake Off
...o see if they have written Shakespeare (or anything good) is called a Discriminator. These are the two components of an Generative Adversarial Neural Network. Adversarial Neural Networks are oddly named since they actually cooperate to make things. Cooks in the Kitchen: I am obsessed with the TV...https://www.kdnuggets.com/2018/05/generativeadversarialneuralnetworksinfinitemonkeysgreatbritishbakeoff.html

Deep Learning, Generative Adversarial Networks & Boxing – Toward a Fundamental Understanding
...problem as a game between the two. In this post we will see why GANs have so much potential, and frame GANs as a boxing match between two opponents. Generative adversarial networks aren’t so different 👊. Intuition behind deep learning Deep learning is famously biologically inspired and many...https://www.kdnuggets.com/2017/03/deeplearninggansboxingfundamentalunderstanding.html

9 Key Deep Learning Papers, Explained">9 Key Deep Learning Papers, Explained
...for the output volume. The way that the authors address this is by adding 1x1 conv operations before the 3x3 and 5x5 layers. The 1x1 convolutions (or network in network layer) provide a method of dimensionality reduction. For example, let’s say you had an input volume of 100x100x60 (This isn’t...https://www.kdnuggets.com/2016/09/9keydeeplearningpapersexplained.html

MNIST Generative Adversarial Model in Keras
By Tim O'Shea, O'Shea Research. Some of the generative work done in the past year or two using generative adversarial networks (GANs) has been pretty exciting and demonstrated some very impressive results. The general idea is that you train two models, one (G) to generate some sort of output...https://www.kdnuggets.com/2016/07/mnistgenerativeadversarialmodelkeras.html

Semisupervised learning with Generative Adversarial Networks
...ts and improve the performance of G, i.e., D is used to improve the performance of G. The paper discussed in this post, Semisupervised learning with Generative Adversarial Networks (https://arxiv.org/abs/1606.01583), utilizes a GAN architecture for multilabel classification. In order to...https://www.kdnuggets.com/2020/01/semisupervisedlearninggenerativeadversarialnetworks.html

Only Numpy: Implementing GANs and Adam Optimizer using Numpy">Only Numpy: Implementing GANs and Adam Optimizer using Numpy
...f you are interested. References Goodfellow, I., PougetAbadie, J., Mirza, M., Xu, B., WardeFarley, D., Ozair, S., … & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672–2680). Free Online Animated GIF Maker — Make GIF Images...https://www.kdnuggets.com/2018/08/onlynumpyimplementinggansadamoptimizer.html

Top 5 Deep Learning Resources, January
...is an interesting read, especially if you are a fan of the recent deep generative works such as Inceptionism, Deep Forger, or the deep convolutional generative adversarial networks (DCGANs) paper immediately below. 2. Unsupervised Representation Learning with Deep Convolutional Generative...https://www.kdnuggets.com/2016/01/deeplearningreadinglistjanuary.html

Overview of GANs (Generative Adversarial Networks) – Part I
...the training problems as well as make big improvements in realistic image generation. Original. Reposted with permission. Related: Overview of GANs (Generative Adversarial Networks) Part I Capsule Networks are Shaking Up AIHere's How to Use Them Top 10 Videos on Deep Learning in Python ...https://www.kdnuggets.com/2017/11/overviewgansgenerativeadversarialnetworkspart1.html

CAN (Creative Adversarial Network)  Explained
…e value of Equation 1.4 as small as possible by Maximizing the value of A more detailed explanation can be found at http://wiki.ubc.ca/Course:CPSC522/Generative_Adversarial_Networks Intuitive definition The Generator will try to modify itself to make the Discriminator pass its own creation as real,…https://www.kdnuggets.com/2017/07/creativeadversarialnetwork.html

The New Neural Internet is Coming
...neural networks (such as image classifier) as a left hemisphere of the neural network technology. With this in mind, it is easy to understand what is Generative Adversarial Network. It is a kind of right hemisphere — the one that is claimed to be responsible for creativity. The Generative...https://www.kdnuggets.com/2018/02/newneuralinternetcoming.html

Uber Creates Generative Teaching Networks to Better Train Deep Neural Networks
...learning to create the training data itself. GTNs leverage generative and metalearning models while also driving inspiration from techniques such as generative adversarial neural networks(GANs). The main idea in GTNs is to train a datagenerating network such that a learner network trained on data...https://www.kdnuggets.com/2020/01/ubergenerativeteachingnetworkstrainneuralnetworks.html

Generative Adversarial Networks – Hot Topic in Machine Learning
…Nothing to complain about. Original. Reposted by Permission. Bio: Al Gharakhanian is wellrounded executive with extensive experience in Product Marketing, Sales, and Business Development in Semiconductors, Machine Learning, and Data Science. Related: Deep Learning Research Review: Generative…https://www.kdnuggets.com/2017/01/generativeadversarialnetworkshottopicmachinelearning.html

Top arXiv Papers, January: ConvNets Advances, Wide Instead of Deep, Adversarial Networks Win, Learning to Reinforcement Learn
...on ImageNet. Our code and models are available at this https URL Adversarial Feature Learning Jeff Donahue, Philipp Krähenbühl, Trevor Darrell Generative Adversarial Networks (GANs) are a "hot topic" in machine learning. GANs do a great job mapping simple latent data distributions to more...https://www.kdnuggets.com/2017/02/toparxivpapersjanuaryconvnetswideadversarial.html

Age of AI Conference 2018 – Day 1 Highlights
...tent. Here are the highlights of Day 1, Wednesday, January 31. Balaji Laxminarayanan, Senior Research Scientist, DeepMind Understanding Generative Adversarial Networks Key Points: This talk delves into some theory behind Generative Adversarial Networks (GANs). How do GANs relate to...https://www.kdnuggets.com/2018/02/ageaiconference2018day1.html

Which Face is Real?
...They created the StyleGAN. To learn a little more about this amazing technique, I have provided some resources and concise explanations below. Generative Adversarial Network For those wanting a refresher on GAN's, this playlist of tutorials on GAN's by Ahlad Kumar is quite helpful....https://www.kdnuggets.com/2019/04/whichfacerealstylegan.html

InfoGAN  Generative Adversarial Networks Part III
...urity and effectiveness. If you find a particular paper you’d like reviewed here, feel free to drop a note in the comments. Related Overview of GANs (Generative Adversarial Networks) – Part I Generative Adversarial Networks — Part II Understanding deep Convolutional Neural Networks with a practical...https://www.kdnuggets.com/2017/11/infogangenerativeadversarialnetworkspart3.html

6 areas of AI and Machine Learning to watch closely">6 areas of AI and Machine Learning to watch closely
…f similar faces. For more details on how these models work, see Ian Goodfellow’s awesome NIPS 2016 tutorial write up. The architecture he introduced, generative adversarial networks (GANs), are particularly hot right now in the research world because they offer a path towards unsupervised learning….https://www.kdnuggets.com/2017/01/6areasaimachinelearning.html

The Major Advancements in Deep Learning in 2016
...o data representation that can be useful for clustering, dimensionality reduction, improving supervised classification and even for data compression. Generative Adversarial Networks (GANs) Recently, a new approach based on generative models has emerged. Called Generative Adversarial...https://www.kdnuggets.com/2017/01/majoradvancementsdeeplearning2016.html

My favorite mindblowing Machine Learning/AI breakthroughs">My favorite mindblowing Machine Learning/AI breakthroughs
...icial faces that are interpolations between other faces or applications of the “style” of one face to another face. The work builds upon past work on Generative Adversarial Networks (GANs). GANs were invented in 2014 and have seen an explosion in research since then. The most basic concept of GANs...https://www.kdnuggets.com/2019/03/favoritemlaibreakthroughs.html

Generative Adversarial Networks — Part II
...e “latent code” from the noise, then generators would be more useful since you could control the output systematically and reliably without having to randomly walk the space. This problem and solution will be explored in Part III. Original. Reposted with permission. Related: Can (Creative...https://www.kdnuggets.com/2017/11/generativeadversarialnetworkspart2.html

Deep Learning Research Review: Reinforcement Learning
...g in Bioinformatics at UCLA. He is passionate about applying his knowledge of machine learning and computer vision to areas in healthcare where better solutions can be engineered for doctors and patients. Original. Reposted with permission. Related: Deep Learning Research Review: Generative...https://www.kdnuggets.com/2016/11/deeplearningresearchreviewreinforcementlearning.html

Deep Stubborn Networks – A Breakthrough Advance Towards Adversarial Machine Intelligence">Deep Stubborn Networks – A Breakthrough Advance Towards Adversarial Machine Intelligence
...as extent variable modeling. This technique is enough to cause massive confusion for the discriminator, and leads to soaring error rate levels in the generative network. Once this occurs, both networks become overwhelmed, leading to a humanlike condition similar to The Paradox of Choice. With both...https://www.kdnuggets.com/2017/04/deepstubbornnetworksganrefinement.html

Are Deep Neural Networks Creative?
...ingen in Germany, can extract the style from one image (say a painting by Van Gogh), and apply it to the content of another image (say a photograph). Generative adversarial networks (GANs), introduced by Ian Goodfellow, are capable of synthesizing novel images by modeling the distribution of seen...https://www.kdnuggets.com/2016/05/deepneuralnetworkscreativedeeplearningart.html

Interpolation in Autoencoders via an Adversarial Regularizer
...ure and its training, allowing the autoencoder to learn the underlying data manifold and to create meaningful interpolations. By borrowing ideas from Generative Adversarial Networks (Goodfellow et al., 2014), ACAI effectively integrates the interpolation process into the autoencoder architecture....https://www.kdnuggets.com/2019/03/interpolationautoencodersadversarialregularizer.html

Deep Learning for NLP: An Overview of Recent Trends">Deep Learning for NLP: An Overview of Recent Trends
...d in previous language modeling techniques. Deep Generative Models Deep generative models, such as variational autoenconders (VAEs) and generative adversarial networks (GANs), are also applied in NLP to discover rich structure in natural language through the process of generating...https://www.kdnuggets.com/2018/09/deeplearningnlpoverviewrecenttrends.html

First Steps of Learning Deep Learning: Image Classification in Keras
...eural Algorithm of Artistic Style style transfer (and for videos!) Realtime Face Capture and Reenactment Colorful Image Colorization Plug & Play Generative Networks for photorealistic image generation Dermatologistlevel classification of skin cancer along with other medical diagnostic tools...https://www.kdnuggets.com/2017/08/firststepslearningdeeplearningimageclassificationkeras.html

Top /r/MachineLearning Posts, November: TensorFlow, Deep Convolutional Generative Adversarial Networks, and lolz
...of the system (with some foreshadowing of its recent big news). Find the video below. 5. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks +173 Generative adversarial networks creating their own image representations. I won't give away much more than that....https://www.kdnuggets.com/2015/12/topredditmachinelearningnovember.html

Deep Learning Transcends the Bag of Words
...models repurpose convolutional neural nets, which are traditionally discriminative models, to generate images. Also of interest are recent papers on generative adversarial learning, which learn to generate images such that they fool a discriminative model trained to distinguish between real and...https://www.kdnuggets.com/2015/12/deeplearningoutgrowsbagwordsrecurrentneuralnetworks.html

ICLR 2019 highlights: Ian Goodfellow and GANs, Adversarial Examples, Reinforcement Learning, Fairness, Safety, Social Good, and all that jazz
...ture prediction as a sequential decision making process and focused on papers that leverage the advances in deep RL to improve structured prediction. Generative Adversarial Models and Adversarial Examples Besides Goodfellow’s keynote talk, which focused on GAN’s applications to different ML...https://www.kdnuggets.com/2019/05/iclr2019highlights.html

Cooperative Trust Among Neural Networks Drives Deeper Learning
...s the feeding forward of an authenticseeming faux pattern or other digital object (eg., photo) that was generated by one neural network (called the “generative network”) for ingest by another neural network (called the “discriminative network”). The former network relies on supervised learning in...https://www.kdnuggets.com/2017/02/ibmcooperativetrustneuralnetworksdeeperlearning.html

More Deep Learning “Magic”: Paintings to photos, horses to zebras, and more amazing imagetoimage translation
...rks). Collection style transfer. The following is from the abstract of the authors' paper, Unpaired ImagetoImage Translation using CycleConsistent Adversarial Networks: Imagetoimage translation is a class of vision and graphics problems where the goal is to learn the mapping between an input...https://www.kdnuggets.com/2017/04/unpairedimagetranslationcyclegan.html

7 Steps to Understanding Deep Learning
...ctive Boltzmann Machines are an obvious exclusion which comes to mind, as are autoencoders, and a whole series of related generative models including Generative Adversarial Networks. However, a line had to be drawn somewhere, or this post would continue ad infinitum. For those interested in...https://www.kdnuggets.com/2016/01/sevenstepsdeeplearning.html

State of Deep Learning and Major Advances: H2 2018 Review
...re. The library has over 8,000 stars on GitHub. Most activity: NLP and GANs Looking at the top fifty implementations, the hottest fields appear to be generative methods and natural language processing (NLP). Within generative methods, popular implementations on GitHub included: vid2vid, DeOldify,...https://www.kdnuggets.com/2018/12/deeplearningmajoradvancesreview.html

A Summary of DeepMind’s Protein Folding Upset at CASP13">A Summary of DeepMind’s Protein Folding Upset at CASP13
...es were actually a result of gradient descent optimization. The DeepMind team tried a ‘fancier’ strategy involving fragment assembly using Generative Adversarial Networks (GANs), but in the end, the best results were obtained by gradient descent optimization. Gradient descent was applied to a...https://www.kdnuggets.com/2019/07/deepmindproteinfoldingupset.html

Beyond the Fence, and the Advent of the Creative Machines
...sic composition has been undertaken in the recent past by different learning algorithms, including Restricted Boltzmann Machines and Recurrent Neural Networks. Generative neural networks have become a part of the rich deep learning research landscape. Online searches turn up new models weekly, be...https://www.kdnuggets.com/2016/01/beyondfenceadventcreativemachines.html

How to Organize Data Labeling for Machine Learning: Approaches and Tools
...r. Synthetic data is produced by a generative model that is trained and validated on an original dataset. There are three types of generative models: Generative Adversarial Networks (or GANs), Autoregressive models (ARs), and Variational Autoencoders (VAEs). Generative Adversarial Networks. GAN...https://www.kdnuggets.com/2018/05/datalabelingmachinelearning.html

KDnuggets™ News 19:n17, May 1: The most desired skill in data science; Seeking KDnuggets Editors, work remotely
...uating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own Normalization vs Standardization  Quantitative analysis Generative Adversarial Networks  Key Milestones and State of the Art Attention Craving RNNS: Building Up To Transformer Networks Opinions The...https://www.kdnuggets.com/2019/n17.html

KDnuggets™ News 19:n16, Apr 24: Data Visualization in Python with Matplotlib & Seaborn; Getting Into Data Science: The Ultimate Q&A
...Part 2: Setting your DataOps Environment Top KDnuggets tweets, Apr 1016: Math for Programmers teaches you the #math you need to know; The Third Wave Data Scientist  what skills are required? Image of the week From The Rise of Generative Adversarial Networks...https://www.kdnuggets.com/2019/n16.html

GANs in TensorFlow from the Command Line: Creating Your First GitHub Project
...sis management, as CEO and Data Scientist in the areas of strategic planning and restructuring, physical and digital marketing, social networks analysis, personnel management, customer database analysis. He has 13 years of Market Research expertise and 10 years of data analysis experience. Related:...https://www.kdnuggets.com/2018/05/zimbresfirstgithubprojectgans.html

Yann LeCun Quora Session Overview
...in deep learning? LeCun first selects one exemplar breakthrough. The most important one, in my opinion, is adversarial training (also called GAN for Generative Adversarial Networks). This is an idea that was originally proposed by Ian Goodfellow when he was a student with Yoshua Bengio at the...https://www.kdnuggets.com/2016/08/yannlecunquorasession.html

KDnuggets™ News 18:n41, Oct 31: Introduction to Deep Learning with Keras; Easy Named Entity Recognition with ScikitLearn
...he latest KDnuggets poll, How Important is Understanding Machine Learning Models? Features Introduction to Deep Learning with Keras Generative Adversarial Networks  Paper Reading Road Map New Poll: How Important is Understanding Machine Learning Models? Named Entity Recognition and...https://www.kdnuggets.com/2018/n41.html

Top /r/MachineLearning Posts, April: Why Momentum Really Works; Machine Learning with ScikitLearn & TensorFlow
...new book from O'Reilly on machine learning with ScikitLearn and TensorFlow, find out about a selfdriving car course, have some fun with generative adversarial networks, explore the mysteries of Go, and read about DeepMind solving AGI... and summoning the demons. The top /r/MachineLearning posts...https://www.kdnuggets.com/2017/05/topredditmachinelearningapril.html

Artificial Intelligence and Data Science Advances in 2018 and Trends for 2019
...perceptron (MLP), neural net training techniques like backpropagation and backpropagation through time (BPTT), residual networks, the introduction of Generative Adversarial Networks (GANs), and deep Qlearning networks (DQN). ”So, looking back to memorable ones I listed before, there weren’t ‘brand...https://www.kdnuggets.com/2019/02/aidatascienceadvancestrends.html

Top 20 Deep Learning Papers, 2018 Edition">Top 20 Deep Learning Papers, 2018 Edition
...atConvNet and gives the technical details of each computational block in the toolbox. 9. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , by Alec R., Luke M. & Soumith C. (2015) (Cited: 1,054) In this work, we hope to help bridge the gap between the...https://www.kdnuggets.com/2018/03/top20deeplearningpapers2018.html

AI Masterpieces: But is it Art?
...e room for the concept of art itself, even calling it “… more of a parlor trick than the next revolution in fine art.” comments Image synthesis using generative adversarial networks Robbie Barrat, now a researcher at Stanford, had the idea of using generative adversarial networks (GANs) to...https://www.kdnuggets.com/2018/10/aiart.html

Research Guide: Advanced Loss Functions for Machine Learning Models
...loss function are able to generate higher quality images than regular GANs. A comparison of the two is shown in the next figure. NIPS 2016 Tutorial: Generative Adversarial Networks This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs)....https://www.kdnuggets.com/2019/11/researchguideadvancedlossfunctionsmachinelearningmodels.html

Recent Advances for a Better Understanding of Deep Learning">Recent Advances for a Better Understanding of Deep Learning
...zation? Role of Depth: How does depth help a neural network to converge? What is the link between depth and generalization? Generative Models: Why do Generative Adversarial Networks (GANs) work so well? What theoretical properties could we use to stabilize them or avoid mode collapse? In this...https://www.kdnuggets.com/2018/10/recentadvancesdeeplearning.html

A “Weird” Introduction to Deep Learning">A “Weird” Introduction to Deep Learning
...constant. This argument is required if you are… BTW: It was shown by Liao and Poggio (2016) that ResNets == RNNs, arXiv:1604.03640v1. 9. Idea: Generative Adversarial Networks (GANs). [1406.2661v1] Generative Adversarial Networks Abstract: We propose a new framework for estimating generative...https://www.kdnuggets.com/2018/03/weirdintroductiondeeplearning.html

Using Deep Learning to Solve Real World Problems">Using Deep Learning to Solve Real World Problems
...sking for sophisticated solutions such as generating text, transforming images, and custom object detection. We are exploring Reinforcement Learning, Generative Adversarial Nets, and Bayesian Casual Inference. The tools are now widely available and free. Machine learning is no longer a software...https://www.kdnuggets.com/2017/12/usingdeeplearningsolverealworldproblems.html

A comprehensive survey on graph neural networks
...Networks. Graph Autoencoders combine the familiar encoderdecoder pairs, but using graph representations on both sides. Finally, the class of Graph Generative Networks aim to generate plausible structures from data. The following table highlights some of the key approaches in these extended...https://www.kdnuggets.com/2019/02/comprehensivesurveygraphneuralnetworks.html

How GOAT Taught a Machine to Love Sneakers
...h a Constrained Variational Framework Understanding disentangling in βVAE Wasserstein AutoEncoders Visualizing Data using tSNE Sampling Generative Networks Generative Adversarial Networks Original. Reposted with permission. Related: Deep Learning, The Curse of Dimensionality, and Autoencoders...https://www.kdnuggets.com/2018/08/goattaughtmachinelovesneakers.html

Top Data Science and Machine Learning Methods Used in 2017">Top Data Science and Machine Learning Methods Used in 2017
....4% Conv Nets, 15.8% Recurrent Neural Networks (RNN), 10.5% Hidden Markov Models (HMM), 4.6% Reinforcement Learning, 4.2% Markov Logic Networks, 2.5% Generative Adversarial Networks (GAN), 2.3% The largest decline in share of usage was for Singular Value Decomposition (SVD), 48% down, from 15.4%...https://www.kdnuggets.com/2017/12/topdatasciencemachinelearningmethods.html

Top 20 Recent Research Papers on Machine Learning and Deep Learning">Top 20 Recent Research Papers on Machine Learning and Deep Learning
...e new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity. Generative adversarial nets, by Bengio, Y., Courville, A.C., Goodfellow, I.J., Mirza, M., Ozair, S., PougetAbadie, J., WardeFarley, D., & Xu,...https://www.kdnuggets.com/2017/04/top20papersmachinelearning.html

Awesome Deep Learning: Most Cited Deep Learning Papers">Awesome Deep Learning: Most Cited Deep Learning Papers
...ovariate shift (2015), S. Loffe and C. Szegedy [pdf] 3. Unsupervised / Generative Models Unsupervised representation learning with deep convolutional generative adversarial networks (2015), A. Radford et al. [pdf] 4. Convolutional Neural Network Models Deep residual learning for image recognition...https://www.kdnuggets.com/2017/04/awesomedeeplearningmostcitedpapers.html

10 New Things I Learnt from fast.ai Course V3
...and generating embeddings. Some experience of the following would be great too: image classification, text classification, semantic segmentation and generative adversarial networks. I organised the content of my 10 learning points as such: from the theory of neural networks, to architectures, to...https://www.kdnuggets.com/2019/06/thingslearntfastaicourse.html

What You Are Too Afraid to Ask About Artificial Intelligence (Part I): Machine Learning
...., Duan, X., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P. (2016). “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets”. arXiv:1606.03657. Giustolisi, O., Savic, D.A. (2006). “A symbolic datadriven technique based on evolutionary...https://www.kdnuggets.com/2016/12/tooafraidaskaboutartificialintelligencemachinelearning.html

The Five Capability Levels of Deep Learning Intelligence
...jectives. We actually do se primitive versions of this in adversarial networks, that learn to perform generalization with competing discriminator and generative networks Expand that concept further into gametheoretic driven networks that are able to perform strategically and tactically solving...https://www.kdnuggets.com/2016/12/5capabilitylevelsdeeplearningintelligence.html

Xavier Amatriain’s Machine Learning and Artificial Intelligence 2019 Yearend Roundup
...lassifier on synthetic data but predicting labels on real data. While this is not strictly speaking a novel idea (see e.g. “LRGAN: Layered Recursive Generative Adversarial Networks for Image Generation”), and the paper applies it only on GANs for image, it does show an interesting path for...https://www.kdnuggets.com/2019/12/xavieramatriainmachinelearningaiyearendroundup.html

Machine Learning & AI Main Developments in 2018 and Key Trends for 2019">Machine Learning & AI Main Developments in 2018 and Key Trends for 2019
...eded or are standard, raw data is close to what the machine expects as the input, and the data is in abundance). 2) Marketing automation: with mature generative adversarial networks and variational autoencoders it is becoming possible to generate thousands of pictures of the same person or paysage...https://www.kdnuggets.com/2018/12/predictionsmachinelearningai2019.html

Best (and Free!!) Resources to Understand Nuts and Bolts of Deep Learning
...iques) is here. I, however, used this quick tutorial to learn about them. Some other good reviews/tutorials A good tutorial about GANs (Generative Adversarial Networks) and generative models in general is what Goodfellow gave in ICLR 2016. It can be found here. Neural Networks have...https://www.kdnuggets.com/2018/07/bestfreeresourcesunderstandnutsboltsdeeplearning.html

Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017">Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017
...phaGo (DeepMind's network which beat the Go world champion using deep RL). Over the whole year we have seen a series of papers showing the success of generative adversarial networks (for unsupervised learning of generative models). Also in the area of unsupervised learning, we have seen the...https://www.kdnuggets.com/2016/12/machinelearningartificialintelligencemaindevelopments2016keytrends2017.html

Medical Image Analysis with Deep Learning
...up. He works with research, technology and business leaders to derive insights from data. Original. Reposted with permission. Related: Deep Learning, Generative Adversarial Networks & Boxing – Toward a Fundamental Understanding I’m a data scientist – mind if I do surgery on your heart? arXiv Paper...https://www.kdnuggets.com/2017/03/medicalimageanalysisdeeplearning.html

10 Free Mustread Books on AI">10 Free Mustread Books on AI
...Google Brain research team after which he joined the newly founded OpenAI institute before returning to Google Research in 2017. Known for inventing generative adversarial networks, Ian is also a lead author of the textbook Deep Learning, was cited in MIT Technology Review's 35 Innovators Under...https://www.kdnuggets.com/2019/11/10freemustreadbooksai.html

Another 10 Free MustSee Courses for Machine Learning and Data Science">Another 10 Free MustSee Courses for Machine Learning and Data Science
...will be covered include convolutional neural networks for image classification and object detection, recurrent neural networks for modeling text, and generative adversarial networks for generating new data. 4. Practical AI Goku Mohandas A practical approach to learning and using machine...https://www.kdnuggets.com/2019/04/another10freemustseecoursesmachinelearningdatascience.html