COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
Watters et al.: https://arxiv.org/abs/1905.09275
#MachineLearning #UnsupervisedLearning #ArtificialIntelligence

Best paper ICML 2019


Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Locatello et al.: https://arxiv.org/pdf/1811.12359.pdf
#deeplearning #disentangledrepresentations #unsupervisedlearning

Neurobiologists train artificial neural networks to map the brain
https://bit.do/eVNef

#cellularmorpoholopyneuralnetworks #unsupervisedlearning
#analyzinglargedatasets #CNN #AI

The human brain consists of about 86 billion nerve cells and about as many glial cells. In addition, there are about 100 trillion connections between the nerve cells alone. While mapping all the connections of a human brain remains out of reach, scientists have started to address the problem on a smaller scale. Through the development of serial block-face scanning electron microscopy, all cells and connections of a particular brain area can now be automatically surveyed and displayed in a three-dimensional image.

“It can take several months to survey a 0.3 mm3 piece of brain under an electron microscope. Depending on the size of the brain, this seems like a lot of time for a tiny piece. But even this contains thousands of cells. Such a data set would also require almost 100 terabytes of storage space. However, it is not the collection and storage but rather the data analysis that is the difficult part."

CS294-158 Deep Unsupervised Learning Spring 2019

Instructors: Pieter Abbeel, Peter Chen, Jonathan Ho, Aravind Srinivas - https://sites.google.com/view/berkeley-cs294-158-sp19/home

#unsupervisedlearning #machinelearning #deeplearning

What Does BERT Look At? An Analysis of BERT's Attention
Clark et al.: https://arxiv.org/abs/1906.04341
Code: https://github.com/clarkkev/attention-analysis
#bert #naturallanguage #unsupervisedlearning

Probing Neural Network Comprehension of Natural Language Arguments

"We are surprised to find that BERT's peak performance of 77% on the Argument Reasoning Comprehension Task reaches just three points below the average untrained human baseline. However, we show that this result is entirely accounted for by exploitation of spurious statistical cues in the dataset. We analyze the nature of these cues and demonstrate that a range of models all exploit them."

Timothy Niven and Hung-Yu Kao: https://arxiv.org/abs/1907.07355

#naturallanguage #neuralnetwork #reasoning #unsupervisedlearning

Self-supervised Learning for Video Correspondence Flow

Zihang Lai and Weidi Xie: https://zlai0.github.io/CorrFlow/

#MachineLearning #SelfSupervisedLearning #UnsupervisedLearning

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu et al.: https://arxiv.org/abs/1907.11692

#bert #naturallanguageprocessing #unsupervisedlearning

Tensorflow implementation of U-GAT-IT

Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

GitHub, by Junho Kim : https://github.com/taki0112/UGATIT

#tensorflow #unsupervisedlearning #generativemodels

Tensorflow implementation of U-GAT-IT

Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

GitHub, by Junho Kim : https://github.com/taki0112/UGATIT

#tensorflow #unsupervisedlearning #generativemodels

Energy-Based Adversarial Training and Video Prediction, NeurIPS 2016

By Yann LeCun, Facebook AI Research

YouTube: https://youtu.be/x4sI5qO6O2Y

#DeepLearning #EnergyBasedModels #UnsupervisedLearning

Unsupervised Separation of Dynamics from Pixels

Silvia Chiappa and Ulrich Paquet : https://arxiv.org/abs/1907.12906

#DeepLearning #MachineLearning #UnsupervisedLearning

U-GAT-IT

Official TensorFlow Implementation : https://github.com/taki0112/UGATIT

#DeepLearning #Tensorflow #UnsupervisedLearning

Compressing BERT for faster prediction
Blog by Sam Sucik : https://blog.rasa.com/compressing-bert-for-faster-prediction-2/
#ArtificialIntelligence #NaturalLanguageProcessing #UnsupervisedLearning

"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

Visualizing and Measuring the Geometry of BERT
Coenen et al.: https://arxiv.org/abs/1906.02715
#BERT #NaturalLanguageProcessing #UnsupervisedLearning

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

Momentum Contrast for Unsupervised Visual Representation Learning
He et al.: https://arxiv.org/abs/1911.05722
#ArtificialIntelligence #DeepLearning #UnsupervisedLearning

The Illustrated GPT-2 (Visualizing Transformer Language Models)
Blog by Jay Alammar : https://jalammar.github.io/illustrated-gpt2/
#ArtificialIntelligence #NLP #UnsupervisedLearning

"Fast Task Inference with Variational Intrinsic Successor Features"
Hansen et al.: https://arxiv.org/abs/1906.05030
#DeepLearning #ReinforcementLearning #UnsupervisedLearning