Capacity, Bandwidth, and Compositionality in Emergent Language Learning
Resnick et al.: https://arxiv.org/abs/1910.11424
#ArtificialIntelligence #MachineLearning #MultiagentSystems

Credit Risk Analysis Using Machine Learning and Deep Learning Models By Peter Martey Dominique Guegan and Bertrand Hassani.

Github: https://github.com/brainy749/CreditRiskPaper
Paper/Article: https://www.mdpi.com/2227-9091/6/2/38/htm

https://t.iss.one/ArtificialIntelligenceArticles

Kaggle:
"Competition Launch: TensorFlow 2.0 Question Answering"
More: https://www.kaggle.com/c/tensorflow2-question-answering

A deep learning framework for neuroscience - Blake A. Richards et al.

@ArtificialIntelligenceArticles
https://www.nature.com/articles/s41593-019-0520-2
@ArtificialIntelligenceArticles

Deep Learning Drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
GitHub by Marimuthu Kalimuthu: https://github.com/kmario23/deep-learning-drizzle
Webpage: https://deep-learning-drizzle.github.io
#artificialintelligence #deeplearning #machinelearning #reinforcementlearning

Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
Gupta et al.: https://arxiv.org/abs/1910.11956
Website : https://relay-policy-learning.github.io
#ReinforcementLearning #MachineLearning #Robotics

Deep learning is good at finding patterns in reams of data, but can't explain how they're connected. Turing Award winner Yoshua Bengio wants to change that.
https://www.wired.com/story/ai-pioneer-algorithms-understand-why/
#DeepLearning #AI

Deep causal representation learning for unsupervised domain adaptation
Moraffah et al.: https://arxiv.org/abs/1910.12417
#DeepLearning #MachineLearning #UnsupervisedLearning

Neural Network Distiller: A Python Package For DNN Compression Research
Zmora et al.: https://arxiv.org/abs/1910.12232
#DeepLearning #MachineLearning #Python

Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Greg Yang : https://arxiv.org/abs/1910.12478
#ArtificialIntelligence #DeepLearning #MachineLearning

Top reads in Data Science for today.

Beginner :

1. Understanding the Bias-Variance Tradeoff: For a beginner nothing is more important than understanding Bias Variance Tradeoff

Read at : https://scott.fortmann-roe.com/docs/BiasVariance.html

2. The Complete Guide to Resampling Methods and Regularization in Python :

Read at : https://towardsdatascience.com/the-complete-guide-to-resampling-methods-and-regularization-in-python-5037f4f8ae23

Intermediate :

1. Choosing a Machine Learning Model: The part art, part science of picking the perfect machine learning model.
Read at : https://towardsdatascience.com/part-i-choosing-a-machine-learning-model-9821eecdc4ce

2. Data Science’s Most Misunderstood Hero : It is the kind of beast where excellence in one area beats mediocrity in two.Each of the three data science disciplines has its own excellence. Statisticians bring rigor, ML engineers bring performance, and analysts bring speed.

Read at : https://towardsdatascience.com/data-sciences-most-misunderstood-hero-2705da366f40

Advanced :

1. Top 10 roles in AI and data science : If you’re keen to make your data useful with a decision intelligence engineering approach.

Read at : https://hackernoon.com/top-10-roles-for-your-data-science-team-e7f05d90d961

2. Trade and Invest Smarter — The Reinforcement Learning Way : Fantastic introduction to TensorTrade — the Python framework for trading and investing using deep reinforcement learning.
#androidabcd #instilllearning AndroidAbcd Instill Learning

Open MMLab Detection Toolbox and Benchmark https://github.com/open-mmlab/mmdetection

Yoshua Bengio reflects his view on Deep Learning and Cognition
our intelligence is not gained through a big bag of tricks, but rather the use of mechanisms used to specifically acquire knowledge
bio-inspired techniques: curriculum learning, cultural evolution, lateral connections, attention, distributed representations
The ability for humans to generalize allows us to have a more powerful understanding of the world than machines currently do
three computational aspects of consciousness are that of access consciousness, self-consciousness and qualia (subjective perception)
Deep learning needs:
generalize faster and "further"
additional compositionality from reasoning & consciousness
causal structure
unsupervised exploration
disentangled representations
attention, intention
Link
https://blog.re-work.co/deep-learning-and-cognition-a-keynote-from-yoshua-bengio/

Advanced Deep Learning Topics
https://lilianweng.github.io/lil-log/

Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
Alexia Jolicoeur-Martineau and Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
#GenerativeAdversarialNetworks #RelativisticGAN #SVM