Stroke of Genius: #GauGAN Turns Doodles into Stunning, Photorealistic Landscapes

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During the last two days, some famous #MachineLearning researchers elucidated their own definition of #DeepLearning. You might check the related links to read full definitions and discussions on each.

Yann LeCun:
#DL is constructing networks of parameterized functional modules & training them from examples using gradient-based optimization. That's it.
This definition is orthogonal to the learning paradigm: reinforcement, supervised, or self-supervised.
https://www.facebook.com/722677142/posts/10156463919392143/

Andriy Burkov:
Looks like in late 2019, people still need a definition of deep learning, so here's mine: deep learning is finding parameters of a nested parametrized non-linear function by minimizing an example-based differentiable cost function using gradient descent.
https://www.linkedin.com/posts/andriyburkov_looks-like-in-late-2019-people-still-need-activity-6615377527147941888-ce68/

François Chollet:
Deep learning refers to an approach to representation learning where your model is a chain of modules (typically a stack / pyramid, hence the notion of depth), each of which could serve as a standalone feature extractor if trained as such.
https://twitter.com/fchollet/status/1210031900695449600

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Training Agents using Upside-Down #ReinforcementLearning

Traditional Reinforcement Learning (RL) algorithms either predict rewards with value functions or maximize them using policy search. We study an alternative: Upside-Down Reinforcement Learning (Upside-Down RL or #UDRL), that solves RL problems primarily using supervised learning techniques. Many of its main principles are outlined in a companion report [34]. Here we present the first concrete implementation of UDRL and demonstrate its feasibility on certain episodic learning problems. Experimental results show that its performance can be surprisingly competitive with, and even exceed that of traditional baseline algorithms developed over decades of research.

#JürgenSchmidhuber

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Paper

Swiss people eat chocolate more than other nations and have won the highest number of Nobel prize :)

Swedens have received many Noble prizes but eat less chocolate. It can be considered as an outlier :)

Germans eat chocolate a lot, but fewer Nobel prizes awarded. So, German chocolates are not good :)

Although the data is not fake, this paper is a joke! Read more here.

#Fun

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A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning

Introduction
Humans have an inherent ability to transfer knowledge across tasks. What we acquire as knowledge while learning about one task, we utilize in the same way to solve related tasks. The more related the tasks, the easier it is for us to transfer, or cross-utilize our knowledge. Some simple examples would be,
Know how to ride a motorbike ⮫ Learn how to ride a car
Know how to play classic piano ⮫ Learn how to play jazz piano
Know math and statistics ⮫ Learn machine learning

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An overview of model explainability in modern machine learning
Towards a better understanding of why machine learning models make the decisions they do, and why it matters

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#Bayesian Model Selection: As A Feature Reduction Technique
A gentle introduction to the application of Bayesian Model Selection to identify important features for machine learning model generation.

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#Facebook 's Head of #AI Says the Field Will Soon ‘Hit the Wall’

Jerome Pesenti leads the development of artificial intelligence at one of the world’s most influential—and controversial—companies. As VP of artificial intelligence at Facebook, he oversees hundreds of scientists and engineers whose work shapes the company’s direction and its impact on the wider world.

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Meet DiffGrad: New Deep Learning Optimizer that solves Adam’s ‘overshoot’ issue

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#Emotion Recognition using Deep Learning (Python)

YouTube

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👆

Yoshua Bengio:

Deep learning is inspired by neural networks of the brain to build learning machines which discover rich and useful internal representations, computed as a composition of learned features and functions.

Full definition:
https://www.facebook.com/yoshua.bengio/posts/2269432439828350

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Artificial Intelligence creates a video from several photos

YouTube

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Learning to Navigate Using Mid-Level Visual Priors

Abstract: How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. navigating a complex environment)? What are the consequences of not utilizing such visual priors in learning? We study these questions by integrating a generic perceptual skill set (a distance estimator, an edge detector, etc.) within a reinforcement learning framework (see Fig. 1). This skill set (“mid-level vision”) provides the policy with a more processed state of the world compared to raw images. Our large-scale study demonstrates that using mid-level vision results in policies that learn faster, generalize better, and achieve higher final performance, when compared to learning from scratch and/or using state-of-the-art visual and nonvisual representation learning methods. We show that conventional computer vision objectives are particularly effective in this regard and can be conveniently integrated into reinforcement learning frameworks. Finally, we found that no single visual representation was universally useful for all downstream tasks, hence we computationally derive a task-agnostic set of representations optimized to support arbitrary downstream tasks.

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#Google Cloud Team Releases #AutoML Natural Language

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👆

Danilo J. Rezende: (DeepMind)

DL is a collection of tools to build complex modular differentiable functions. These tools are devoid of meaning, it is pointless to discuss what DL can or cannot do. What gives meaning to it is how it is trained and how the data is fed to it.

https://twitter.com/DeepSpiker/status/1209862283368816641

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AI Debate 2019 has caused many discussions in both deep learning and cognitive sciences communities about the nature of DL. If you are interested in following these hot discussions, please check Gary Marcus or Yann LeCun recent tweets.

Related links:
https://news.1rj.ru/str/deepgravity/343
https://news.1rj.ru/str/deepgravity/347
https://news.1rj.ru/str/deepgravity/357
https://news.1rj.ru/str/deepgravity/361

🔭 @DeepGravity

:)

#Fun

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Crowdfunding Dynamics Tracking: A #ReinforcementLearning Approach

Recent years have witnessed the increasing interests in research of crowdfunding mechanism. In this area, dynamics tracking is a significant issue but is still under exploration. Existing studies either fit the fluctuations of time-series or employ regularization terms to constrain learned tendencies. However, few of them take into account the inherent decision-making process between investors and crowdfunding dynamics. To address the problem, in this paper, we propose a Trajectory-based Continuous Control for Crowdfunding (TC3) algorithm to predict the funding progress in crowdfunding. Specifically, actor-critic frameworks are employed to model the relationship between investors and campaigns, where all of the investors are viewed as an agent that could interact with the environment derived from the real dynamics of campaigns. Then, to further explore the in-depth implications of patterns (i.e., typical characters) in funding series, we propose to subdivide them into fast-growing and slow-growing ones. Moreover, for the purpose of switching from different kinds of patterns, the actor component of TC3 is extended with a structure of options, which comes to the TC3-Options. Finally, extensive experiments on the Indiegogo dataset not only demonstrate the effectiveness of our methods, but also validate our assumption that the entire pattern learned by TC3-Options is indeed the U-shaped one.

Paper

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Asymmetric #GAN for Unpaired Image-to-image Translation

Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data. Current works like the well-acknowledged Cycle GAN provide a general solution for any two domains through modeling injective mappings with a symmetric structure. While in situations where two domains are asymmetric in complexity, i.e., the amount of information between two domains is different, these approaches pose problems of poor generation quality, mapping ambiguity, and model sensitivity. To address these issues, we propose Asymmetric GAN (AsymGAN) to adapt the asymmetric domains by introducing an auxiliary variable (aux) to learn the extra information for transferring from the information-poor domain to the information-rich domain, which improves the performance of state-of-the-art approaches in the following ways. First, aux better balances the information between two domains which benefits the quality of generation. Second, the imbalance of information commonly leads to mapping ambiguity, where we are able to model one-to-many mappings by tuning aux, and furthermore, our aux is controllable. Third, the training of Cycle GAN can easily make the generator pair sensitive to small disturbances and variations while our model decouples the ill-conditioned relevance of generators by injecting aux during training. We verify the effectiveness of our proposed method both qualitatively and quantitatively on asymmetric situation, label-photo task, on Cityscapes and Helen datasets, and show many applications of asymmetric image translations. In conclusion, our AsymGAN provides a better solution for unpaired image-to-image translation in asymmetric domains.

Paper

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A Complete Guide To #Math And #Statistics For #DataScience

Link

Deep (Learning) Gravity, [02.01.20 09:39]
Asymmetric #GAN for Unpaired Image-to-image Translation

Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data. Current works like the well-acknowledged Cycle GAN provide a general solution for any two domains through modeling injective mappings with a symmetric structure. While in situations where two domains are asymmetric in complexity, i.e., the amount of information between two domains is different, these approaches pose problems of poor generation quality, mapping ambiguity, and model sensitivity. To address these issues, we propose Asymmetric GAN (AsymGAN) to adapt the asymmetric domains by introducing an auxiliary variable (aux) to learn the extra information for transferring from the information-poor domain to the information-rich domain, which improves the performance of state-of-the-art approaches in the following ways. First, aux better balances the information between two domains which benefits the quality of generation. Second, the imbalance of information commonly leads to mapping ambiguity, where we are able to model one-to-many mappings by tuning aux, and furthermore, our aux is controllable. Third, the training of Cycle GAN can easily make the generator pair sensitive to small disturbances and variations while our model decouples the ill-conditioned relevance of generators by injecting aux during training. We verify the effectiveness of our proposed method both qualitatively and quantitatively on asymmetric situation, label-photo task, on Cityscapes and Helen datasets, and show many applications of asymmetric image translations. In conclusion, our AsymGAN provides a better solution for unpaired image-to-image translation in asymmetric domains.

Paper

🔭 @DeepGravity