31st May - 13th June 2019

2747 new papers

Etymo Newsletter provides the latest development in machine learning research, including the most popular datasets and the most trending papers in the past two weeks.

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Fortnight Summary

Popular datasets include MNIST, CIFAR-10, ImageNet, COCO, and SVHN. Our trending phrases are Learned Optimization, Blue Noise, and Document Ranking. Survey and review papers are very popular in the past two weeks. In our trending papers section, we include an introductory paper on Variational Autoencoders, and a survey on Generative Adversarial Networks (GANs). We also include a paper on a new network design, Mesh R-CNN, to detect 2D images and produce the full 3D details. We also include two very interesting papers on robotics, to sum up the recent advancements in the robotics industry.

Popular Datasets

Here are most mentioned datasets over the last two weeks.

Name Type Number of Papers
MNIST Handwritten Digits 129
CIFAR-10 Tiny Image Dataset in 10 Classes 76
ImageNet Image Dataset 72
COCO Common Objects in Context 31
SVHN The Street View House Numbers Dataset 20

Trending Phrases

In this section, we present a list of phrases that appeared significantly more in this newsletter than the previous newsletters.

Etymo Trending

Presented below is a list of the most trending papers added in the last two weeks.

  • An Introduction to Variational Autoencoders:
    Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions.

  • Generative Adversarial Networks: A Survey and Taxonomy:
    A study on the architecture-variants and loss-variants on Generative Adversarial Networks (GAN), and the proposal of using loss and architecture-variants for classifying most popular GANs. There is also a review of 16 GANs.

  • Mesh R-CNN:
    The authors unify advances in both 2D and 3D shape prediction. They propose a system that detects objects in real-world images and produces a triangle mesh giving the full 3D shape of each detected object. The system, called Mesh R-CNN, augments Mask R-CNN with a mesh prediction branch.

Two papers on Robotics

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