What is the fuss about TensorFuzz?
It is the fun automated software “testing” for neural networks,
adapting traditional coverage guided fuzzing techniques.
Run #TensorFuzz to take your test coverage to levels other methods cannot reach (e.g. activation coverage, not just class coverage)

Great work by Augustus Odena and @Ian Goodfellow
Join the #ICML2019 talk at 9:40am today. Grand Ballroom

Read at https://arxiv.org/pdf/1807.10875.pdf
Code: https://github.com/brain-research/tensorfuzz

Shapes and Context:
In-the-wild Image Synthesis & Manipulation

https://www.cs.cmu.edu/~aayushb/OpenShapes/

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

paper — arxiv
https://arxiv.org/pdf/1905.08233.pdf

video — youtube
https://www.youtube.com/watch?v=p1b5aiTrGzY

Best Deep Learning Research of 2019 So Far

https://medium.com/@ODSC/best-deep-learning-research-of-2019-so-far-7bea0ed22e38

Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
Arivazhagan et al.: https://arxiv.org/abs/1906.05218
#ArtificialIntelligence #DeepLearning #MachineLearning

Beating the bookies with their own numbers - and how the online sports betting market is rigged
Kaunitz et al.: https://arxiv.org/abs/1710.02824
#Gambling #ComputerScience #Statistics

Stabilizing the Lottery Ticket Hypothesis
Frankle et al.: https://arxiv.org/abs/1903.01611
#ArtificialIntelligence #DeepLearning #NeuralNetworks

Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference
Covell et al.: https://arxiv.org/abs/1906.04798
#ArtificialIntelligence #DeepLearning #MachineLearning

Understanding Generalization through Visualizations. arxiv.org/abs/1906.03291

Adaptive Nonparametric Variational Autoencoder. arxiv.org/abs/1906.03288

​​Stanford researchers develop artificial intelligence tool to help detect brain aneurysms

Neural net for brain aneurysms detection

Andrew ng
https://news.stanford.edu/2019/06/07/ai-tool-helps-radiologists-detect-brain-aneurysms/

Write With Transformer
Built by the Hugging Face team: https://transformer.huggingface.co
#ArtificialIntelligence #MachineLearning #GPT2 #GenerativePreTrainedTransformer

Deep Flow-Guided Video Inpainting
Xu et al.: https://nbei.github.io/video-inpainting.html
#AritifcialIntelligence #DeepLearning #MachineLearning

TensorNetwork: A Library for Physics and Machine Learning

“TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of other research areas, including machine learning. Authors demonstrate the use of the API with applications both physics and machine learning, with details appearing in companion papers.”

Paper: https://arxiv.org/pdf/1905.01330.pdf

Getting Started With MarathonEnvs v0.5.0a
Blog by Joe Booth: https://towardsdatascience.com/gettingstartedwithmarathonenvs-v0-5-0a-c1054a0b540c
#MachineLearning #ArtificialIntelligence #DeepLearning #ReinforcementLearning #Robotics

The Neural Aesthetic is finished! Notes and around 30 hours of video lectures

The Neural Aesthetic @ ITP-NYU, Fall 2018

Gene Kogan

https://ml4a.github.io/classes/itp-F18/

An Introduction to Variational Autoencoders [93pp]


https://arxiv.org/abs/1906.02691v1

Is Optimization a Sufficient Language for Understanding Deep Learning?
https://www.offconvex.org/2019/06/03/trajectories/

iPython notebook for Attentive Neural Processes
https://arxiv.org/pdf/1901.05761.pdf

A special case are Neural Processes
https://arxiv.org/pdf/1807.01622.pdf

Try running the code on your browser (or phone) at:
https://colab.research.google.com/github/deepmind/neural-processes/blob/master/attentive_neural_process.ipynb