📌 Top 10 Deep Learning Github Repositories 2018

- In this article, we bring you a list of the Top 10 Deep Learning Github Repositories on a trend that has been sorted by the number of stars.

The Top 10 Deep Learning Repositories along with their respective links are:

1️⃣ Tensorflow

2️⃣ Keras

3️⃣ OpenCV

4️⃣ Caffe

5️⃣ Tensorflow-Examples

6️⃣ Machine-Learning-For-Software-Engineers

7️⃣ Deeplearningbook-Chinese

8️⃣ Deep-Learning-Papers-Reading-Roadmap

9️⃣ Pytorch

🔟 Awesome-Deep-Learning-Papers

References: https://www.techleer.com/articles/547-top-10-deep-learning-github-repositories-2018

Two positions in all areas of computer science

https://aprecruit.ucmerced.edu/JPF00834

Awesome Fraud Detection Research Papers.
https://github.com/benedekrozemberczki/awesome-fraud-detection-papers

SDNet: Semantically Guided Depth Estimation Network. arxiv.org/abs/1907.10659

Semi-parametric Object Synthesis. arxiv.org/abs/1907.10634

'The Deep Learning Revolution' - Geoffrey Hinton - RSE President's Lecture 2019


https://www.youtube.com/watch?v=re-SRA5UZQw&feature=youtu.be


https://t.iss.one/ArtificialIntelligenceArticles

best paper awards at #AC L2019

arxiv.org/abs/1906.01603

BEST PAPER AWARDS at #AC L2019
https://www.acl2019.org/EN/nominations-for-acl-2019-best-paper-awards.xhtml

PhD position at the Donders: machine learning, sleep enhancement, lucid dreaming.

dreslerlab.org/arenar

Also, if you have a strong MEG/EEG/BCI background, please inquire for positions.

MIT 6.S191: Recurrent Neural Networks
https://www.youtube.com/watch?v=_h66BW-xNgk

10 Exciting Ideas of 2018 in NLP
https://ruder.io/10-exciting-ideas-of-2018-in-nlp/

Great applications for mobile robotics!

Real-time Vision-based Depth Reconstruction

https://www.profillic.com/paper/arxiv:1907.07210

They experiment with several FCNN architectures and introduce a few enhancements aimed at increasing both the effectiveness and the efficiency of the inference.

Speech2Face: Learning the Face Behind a Voice
Oh et al.: https://arxiv.org/abs/1905.09773
#ArtificialIntelligence #MachineLearning #Multimedia

Optuna: A Next-generation Hyperparameter Optimization Framework

Akiba et al.: https://arxiv.org/abs/1907.10902

#ArtificialIntelligence #DataScience #MachineLearning

It’s hard to think of a better place than #Vancouver for #CVPR 2023. Announcing our bid -- a strong organizing team at a beautiful convention centre in a great city.

Greg Mori, Fei-Fei Li, Michael Brown, Yoichi Sato as General Chairs; Vladlen Koltun, Svetlana Lazebnik, Ross Girshick, Andreas Geiger as Program Chairs; Olga Russakovsky and Serena Yeung as Workshop Chairs, Jianxin Wu and Siyu Tang as Tutorial Chairs, Kwang Moo Yi and Leonid Sigal as Local Arrangements Chairs, Catherine Qi Zhao as Doctoral Consortium Chair, Gim Hee Lee and Jon Barron as Demo Chairs.

Check out the full bid document:
www2.cs.sfu.ca/~mori/cvpr2023_vancouver.pdf

Go-Explore: a New Approach for Hard-Exploration Problems
Ecoffet et al.: https://arxiv.org/abs/1901.10995
#MachineLearning #ArtificialIntelligence

"The Bitter Lesson"
The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.
In computer chess, the methods that defeated the world champion, Kasparov, in 1997, were based on massive, deep search (…) A similar pattern of research progress was seen in computer Go, only delayed by a further 20 years.
One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation, even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.
Rich Sutton, March 13, 2019: https://www.incompleteideas.net/IncIdeas/BitterLesson.html
#Learning #ReinforcementLearning #Search

"Subspace Neural Physics: Fast Data-Driven Interactive Simulation"
Holden et al.: https://theorangeduck.com/media/uploads/other_stuff/deep-cloth-paper.pdf
#machinelearning #neuralnetworks #physics

Optuna: A Next-generation Hyperparameter Optimization Framework

Akiba et al.: https://arxiv.org/abs/1907.10902

#ArtificialIntelligence #DataScience #MachineLearning

Y-Autoencoders: disentangling latent representations via sequential-encoding

Article: https://arxiv.org/abs/1907.10949

GitHub: https://github.com/mpatacchiola/Y-AE