Very interesting paper about optimization of models, with step by step pruning process.
ICLR'19 best paper
https://arxiv.org/abs/1905.01067
ICLR'19 best paper
https://arxiv.org/abs/1905.01067
arXiv.org
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed that a simple approach to creating sparse networks (keeping the large weights) results in models that are trainable from...
New interesting paper about segmentation https://arxiv.org/pdf/1905.01892v1.pdf
Unsupervised scene decomposition
https://arxiv.org/abs/1901.11390
https://arxiv.org/abs/1901.11390
arXiv.org
MONet: Unsupervised Scene Decomposition and Representation
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interactions and other...
+2.8M images with precise segmentation masks from Google https://ai.googleblog.com/2019/05/announcing-open-images-v5-and-iccv-2019.html?fbclid=IwAR1jiDKPtKVfZItxpZLMIYE8fS8n5PboLsB6VU5YRMlpBpQqhXPHnazrnVY
research.google
Announcing Open Images V5 and the ICCV 2019 Open Images Challenge
Posted by Vittorio Ferrari, Research Scientist, Machine Perception In 2016, we introduced Open Images, a collaborative release of ~9 million imag...
Очередной крутой пейпер от NVidia. На сей раз о собачках))
https://nvlabs.github.io/FUNIT/index.html
https://nvlabs.github.io/FUNIT/index.html
nvlabs.github.io
Few-Shot Unsupervised Image-to-Image Translation
And here are two examples:
1) https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb
2) https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/reflectance.ipynb
1) https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb
2) https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/reflectance.ipynb
Google
6dof alignment.ipynb
Run, share, and edit Python notebooks
190 Tutorials for young specialists
https://www.kaggle.com/kashnitsky/mlcourse/kernels
https://www.kaggle.com/kashnitsky/mlcourse/kernels
Kaggle
mlcourse.ai
Datasets and notebooks of the open Machine Learning course mlcourse.ai
Museum of Dali in Florida, used DeepFake model to create interactive video box with Dali.
"""Next step are movies.
https://youtu.be/BIDaxl4xqJ4
"""Next step are movies.
https://youtu.be/BIDaxl4xqJ4
YouTube
Behind the Scenes: Dalí Lives
Dalí Lives – Art Meets Artificial Intelligence. Exclusively at The Dalí Museum.
The Dalí Museum in St. Petersburg, Florida partnered with Goodby Silverstein & Partners to create a groundbreaking Artificial Intelligence (AI) experience. "Dalí Lives" will…
The Dalí Museum in St. Petersburg, Florida partnered with Goodby Silverstein & Partners to create a groundbreaking Artificial Intelligence (AI) experience. "Dalí Lives" will…
computer age statistical inference.pdf
8.1 MB
Free book - Computer Age Statistical Inference - Algorithms, Evidence, & Data Science
Table of Content:
Part I Classic Statistical Inference
1 Algorithms and Inference
2 Frequentist Inference
3 Bayesian Inference
4 Fisherian Inference and Maximum Likelihood Estimation
5 Parametric Models and Exponential Families
Part II Early Computer-Age Methods
6 Empirical Bayes
7 James–Stein Estimation and Ridge Regression
8 Generalized Linear Models and Regression Trees
9 Survival Analysis and the EM Algorithm
10 The Jackknife and the Bootstrap
11 Bootstrap Confidence Intervals
12 Cross-Validation and Cp Estimates of Prediction Error
13 Objective Bayes Inference and MCMC
14 Postwar Statistical Inference and Methodology
Part III Twenty-First-Century Topics
15 Large-Scale Hypothesis Testing and FDRs
16 Sparse Modeling and the Lasso
17 Random Forests and Boosting
18 Neural Networks and Deep Learning
19 Support-Vector Machines and Kernel Methods
20 Inference After Model Selection
21 Empirical Bayes Estimation Strategies
Table of Content:
Part I Classic Statistical Inference
1 Algorithms and Inference
2 Frequentist Inference
3 Bayesian Inference
4 Fisherian Inference and Maximum Likelihood Estimation
5 Parametric Models and Exponential Families
Part II Early Computer-Age Methods
6 Empirical Bayes
7 James–Stein Estimation and Ridge Regression
8 Generalized Linear Models and Regression Trees
9 Survival Analysis and the EM Algorithm
10 The Jackknife and the Bootstrap
11 Bootstrap Confidence Intervals
12 Cross-Validation and Cp Estimates of Prediction Error
13 Objective Bayes Inference and MCMC
14 Postwar Statistical Inference and Methodology
Part III Twenty-First-Century Topics
15 Large-Scale Hypothesis Testing and FDRs
16 Sparse Modeling and the Lasso
17 Random Forests and Boosting
18 Neural Networks and Deep Learning
19 Support-Vector Machines and Kernel Methods
20 Inference After Model Selection
21 Empirical Bayes Estimation Strategies
Slides about PyTorch internals http://web.mit.edu/~ezyang/Public/pytorch-internals.pdf
Interesting and resultative type of training/regularization
https://arxiv.org/pdf/1710.09412.pdf
Demo code here: https://github.com/facebookresearch/mixup-cifar10
https://arxiv.org/pdf/1710.09412.pdf
Demo code here: https://github.com/facebookresearch/mixup-cifar10
Awesome idea to make Depth prediction more accurate from Google guys
https://ai.googleblog.com/2019/05/moving-camera-moving-people-deep.html
https://ai.googleblog.com/2019/05/moving-camera-moving-people-deep.html
research.google
Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction
Posted by Tali Dekel, Research Scientist and Forrester Cole, Software Engineer, Machine Perception The human visual system has a remarkable abili...
One more paper that tries to move classification problem from supervised to unsupervised area https://arxiv.org/abs/1905.09272