Structured Knowledge Discovery from Massive Text Corpus
arxiv.org/abs/1908.01837

How We Construct a Virtual Being’s Brain with Deep Learning
https://towardsdatascience.com/how-we-construct-a-virtual-beings-brain-with-deep-learning-8f8e5eafe3a9

Papers with Code Channel


@PaperswithCode

Cards against machine learning.

#FridayFeels

DEADLINE APPROACHING! Only one week left to the submission deadline for this year's Women in Machine Learning (#WiML2019) workshop (August 15th, 2019 11:59pm PST).

Submission format: 1-page contribution.

Submit here: https://cmt3.research.microsoft.com/WiML2019

For more info, visit: https://wimlworkshop.org/2019/cfp/


@ArtificialIntelligenceArticles

1st edition of "Interpretable Machine Learning
By Christoph Molnar.
‏
Web: https://christophm.github.io/interpretable-ml-book/

#artificalintelligence #deeplearning #machinelearning

Python Libraries for Interpretable Machine Learning
https://towardsdatascience.com/python-libraries-for-interpretable-machine-learning-c476a08ed2c7

Task-Embedded Control Networks, which employ ideas from metric learning in order to create a task embedding that can be used by a robot to learn new tasks from one or more demonstrations

Official code for "Task-Embedded Control Networks for Few-Shot Imitation Learning".
https://github.com/stepjam/TecNets

paper Task-Embedded Control Networks for Few-Shot Imitation Learning
https://arxiv.org/pdf/1810.03237.pdf

"AI is an incredibly powerful technology, and like any technology it can be used for a variety of purposes, some beneficial, and some less so.” Read more about why Andrew Ng is a tech optimist in TIME: https://time.com/5576442/tech-optimists/

MidiMe: Personalizing MusicVAE
Dinculescu et al.: https://magenta.tensorflow.org/midi-me
#ArtificialIntelligence #DeepLearning #Transformer

Sparse Networks from Scratch: Faster Training without Losing Performance

Tim Dettmers and Luke Zettlemoyer : https://arxiv.org/abs/1907.04840

#ArtificialIntelligence #MachineLearning #NeuralComputing

"Deep Image Prior": super-resolution, inpainting, denoising without learning on a dataset and pretrained networks. Comparable results to learned methods.
code https://github.com/DmitryUlyanov/deep-image-prior
project page https://dmitryulyanov.github.io/deep_image_prior

"Hamiltonian Neural Networks"
Greydanus et al.: https://arxiv.org/abs/1906.01563
Blog: https://greydanus.github.io/2019/05/15/hamiltonian-nns/
#Hamiltonian #NeuralNetworks #UnsupervisedLearning

Synthesizing Programs for Images using Reinforced Adversarial Learning
Ganin et al., 2018: https://proceedings.mlr.press/v80/ganin18a.html
Agents and environments : https://github.com/deepmind/spiral
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data

https://arxiv.org/pdf/1908.03190.pdf

Self-supervised Attention Model for Weakly Labeled Audio Event Classification arxiv.org/abs/1908.02876

Location Field Descriptors: Single Image 3D Model Retrieval in the Wild. arxiv.org/abs/1908.02853

Solar image denoising with convolutional neural networks. arxiv.org/abs/1908.02815

Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs. arxiv.org/abs/1908.02797

Ten quick tips for effective dimensionality reduction

Lan Huong Nguyen and Susan Holmes : https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006907

#ArtificialIntelligence #BigData #DataScience