@Machine_learn


Fast and Easy Infinitely Wide Networks with Neural Tangents


https://ai.googleblog.com/2020/03/fast-and-easy-infinitely-wide-networks.html

Colab notebook: https://colab.research.google.com/github/google/neural-tangents/blob/master/notebooks/neural_tangents_cookbook.ipynb#scrollTo=Lt74vgCVNN2b

Code: https://github.com/google/neural-tangents

Paper: https://arxiv.org/abs/1912.02803

@Machine_learn

Meta-Transfer Learning for Zero-Shot Super-Resolution

Code: https://github.com/JWSoh/MZSR

Paper: https://arxiv.org/abs/2002.12213v1

A new paper from Samsung AI Center (Moscow) on unpaired image-to-image translation. Now – without any domain labels, even on training time!
▶️ youtu.be/DALQYKt-GJc
📝 arxiv.org/abs/2003.08791
📉 @Machine_learn

seaborn tutorial
#book #python @Machine_learn

@Machine_learn


Anomaly detection with Keras, TensorFlow, and Deep Learning

In this tutorial, you will learn how to perform anomaly and outlier detection using autoencoders, Keras, and TensorFlow.

https://www.pyimagesearch.com/2020/03/02/anomaly-detection-with-keras-tensorflow-and-deep-learning/

@Machine_learn

Graph Machine Learning research groups: Le Song

Le Song (~1981)
- Affiliation: Georgia Institute of Technology;
- Education: Ph.D. at U. of Sydney in 2008 (supervised by Alex Smola);
- h-index: 59;
- Awards: best papers at ICML, NeurIPS, AISTATS;
- Interests: generative and adversarial graph models, social network analysis, diffusion models.

@Machine_learn

Rethinking Image Mixture for Unsupervised Visual Representation Learning

Code: https://github.com/szq0214/Rethinking-Image-Mixture-for-Unsupervised-Learning

Paper: https://arxiv.org/abs/2003.05438v1

@Machine_learn

An AI program that plays Flappy Bird using reinforcement learning.


Code: https://github.com/taivu1998/FlapAI-Bird

Model: https://stanford-cs221.github.io/autumn2019-extra/posters/18.pdf

Paper: https://arxiv.org/abs/2003.09579

Gradient boost trees with xgboost and scikit-learn #book #python
@Machine_learn

New paper by Yandex.MILAB 🎉
Tired of waiting for backprop to project your face into StyleGAN latent space to use some funny vector on it? Just distilate this tranformation by pix2pixHD!
arxiv.org/abs/2003.03581
@Machine_learn

@Machine_learn

Graph Isomorphism Software

Open-source software for finding isomorphism or canonical forms of graphs.

* Nauty/Traces
* Bliss
* saucy
* conauto
* Gi-ext

@Machine_learn
Train transformer language models with reinforcement learning.

https://lvwerra.github.io/trl/

Code: https://github.com/openai/lm-human-preferences

Paper: https://arxiv.org/pdf/1909.08593.pdf

@Machine_learn
Flows for simultaneous manifold learning and density estimation

A new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold.

Code: https://github.com/johannbrehmer/manifold-flow

Paper: https://arxiv.org/abs/2003.13913

@Machine_learn

Gradient Centralization: A New Optimization Technique for Deep Neural Networks

Code: https://github.com/Yonghongwei/Gradient-Centralization

Paper: https://arxiv.org/abs/2004.01461

! pip install covid ‌
🦠
@Machine_learn

Artificial Vision and Language Processing for Robotics
#vision
#languageprocessing
#python
@Machine_learn

@Machine_learn

Advancing Self-Supervised and Semi-Supervised Learning with SimCLR

https://ai.googleblog.com/2020/04/advancing-self-supervised-and-semi.html

Code and Pretrained-Models: https://github.com/google-research/simclr

Papare: https://arxiv.org/abs/2002.05709