UnsupervisedPapers.com

An open-source directory of unsupervised machine learning papers, tasks and methods with links to articles, websites, videos and code.

[UnsupervisedPapers.com @UnsuperPapers]

https://t.co/dd0ho6WFF1

[arxiv @arxiv_org]

Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images. https://t.co/4OVTDnPbI3

[arxiv @arxiv_org]

Taxonomy of multimodal self-supervised representation learning. https://t.co/HPLMk6kwmv

[Volodymyr Nechyporuk-Zloy @vnzloy]

"Self-Supervised Representation Learning for Astronomical Images
Md Abul Hayat, George Stein, Peter Harrington, Zarija Lukić, Mustafa Mustafa
https://t.co/81q2KmcnEC

I am looking for a job in Microscopy

[UnsupervisedPapers.com @UnsuperPapers]

NeurIPS 2020 | Conference Watch on Self-Supervised Learning by @Synced_Global https://t.co/3VyiAEW7DC

[Jim Bohnslav @CVPR @jbohnslav]

Self-supervised visual representation learning from hierarchical grouping
arxiv: https://t.co/YPyK7BKpNy

Use contours to partition image; group into hierarchies; loss is pairwise contrastive using positive/negatives from the grouping

[arxiv @arxiv_org]

ISD: Self-Supervised Learning by Iterative Similarity Distillation. https://t.co/BBcZq8qNPx

[DSN - Data Science Nigeria @dsn_ai_network]

Welcome, FERM from researchers @UCBerkeley, a framework that can train a robotic arm on 6 grasping tasks in less than 1 hour given only 10 demonstrations utilizing data augmentation, unsupervised and reinforcement learning for sample-efficient training.

https://t.co/IreF7V4XBc

[arxiv @arxiv_org]

Understanding graph embedding methods and their applications. https://t.co/yB3ol9zpHh

[Marcus Borba @marcusborba]

Pair-view Unsupervised Graph Representation Learning. #ArtificialIntelligence #MachineLearning #BigData #Analytics #Python #RStats #JavaScript #ReactJS #Serverless #Linux #IoT #Programming #100DaysOfCode #NeuralNetworks #DeepLearning #DataScience #AI
https://t.co/hNgaoNcDAI

[James Lucas @james_r_lucas]

1/6 Check our new paper on Flexible Few-Shot Learning! https://t.co/bMx7WqrcIs

We extend FSL to include flexible classification criterion in each episode. Unsupervised representation learning beats supervised approaches.

Short version @ #NeurIPS2020 metalearn workshop, 10am EST

[UnsupervisedPapers.com @UnsuperPapers]

Unsupervised part representation by Flow Capsules https://t.co/cVxh5ZzsLh

[UnsupervisedPapers.com @UnsuperPapers]

Unsupervised Learning of Dense Visual Representations https://t.co/1M8LW9Nesw

[UnsupervisedPapers.com @UnsuperPapers]

Unsupervised Learning of Object Landmarks via Self-Training Correspondence https://t.co/P7X2zJFae1

[UnsupervisedPapers.com @UnsuperPapers]

Space-Time Correspondence as a Contrastive Random Walk https://t.co/f7tnxtDcqP

[Andrea Tagliasacchi 🇨🇦→✈️→🇺🇲 (@CVPR) @taiyasaki]

Our new paper "𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐩𝐚𝐫𝐭 𝐫𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐛𝐲 𝐅𝐥𝐨𝐰 𝐂𝐚𝐩𝐬𝐮𝐥𝐞𝐬" is out https://t.co/0t4XH4FH8H

TL;DR: are newborns exposed to 14 million (i.e. ImageNet) labeled images? No! They learn by observing motion... in an unsupervised fashion

[UnsupervisedPapers.com @UnsuperPapers]

Unsupervised machine learning method of the day: the GIST descriptor
https://t.co/YxqPkE29jg

[UnsupervisedPapers.com @UnsuperPapers]

@hyonschu This is very interesting. Why do you think current unsupervised methods are not privy to natural categories? Why are they so elusive if they are natural?

[Zoubin Ghahramani @ZoubinGhahrama1]

This really resonates. AI/ML research is generally too focused on generating ‘novel’ methods, and re-branding tweaks on existing methods, rather than finding connections between existing methods, and solving real problems.