
31st December 2018  10th January 2019
960 new papers

In this newsletter from Etymo, you can find out the latest development in machine learning research, including the most popular datasets used, the most frequently appearing keywords, the important research papers associated with the keywords, and the most trending papers in the past two weeks.
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Fortnight Summary
The new year 2019 has taken a slow start in machine learning related research, as the number of papers published in the past two weeks is significantly smaller than usual. Nevertheless, computer vision (CV) is still a main research area, as reflected on the popularity of the CV datasets and the most trending papers.
We present the emerging interests in research under the "Trending Phrases" section. The papers in this section shows some cutting edge results. There are three good papers related to differential evolution, a category of optimization algorithms applicable to problems that are not continuous and noisy. There is also a notable progress on foreground and background estimation using PCA for moving cameras. Please read the "Trending Phrases" section for more details.
Other notable development in research includes the following:
More efficient active tasks learning using midlevel visual representation than the fromscratch learning approach: MidLevel Visual Representations Improve Generalization and Sample Efficiency for Learning Active Tasks
A new model schedule approach to choose the model with the best predictive accuracy under a given budget: Costsensitive Selection of Variables by Ensemble of Model Sequences
A solution to determine layerwise parallelism for deep neural network training with an array of DNN accelerators: HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
To establish the fundamental limits of learning in deep neural networks by characterizing what is possible if no constraints on the learning algorithm and the amount of training data are imposed: Deep Neural Network Approximation Theory
A deep network embedding model to learn the lowdimensional node vector representations with structural balance preservation for the signed networks: Deep Network Embedding for Graph Representation Learning in Signed Networks
Some of the notable review papers include:
A Comprehensive Survey on Graph Neural Networks
An introduction to domain adaptation and transfer learning
Coevolution spreading in complex networks
FPGAbased Accelerators of Deep Learning Networks for Learning and Classification: A Review
A Survey on Multioutput Learning

Popular Datasets
Computer vision is still the main focus area of research.
Name 
Type 
Number of Papers 
MNIST 
Handwritten Digits 
41 
CIFAR10 
Tiny Image Dataset in 10 Classes 
28 
ImageNet 
Image Dataset 
23 
KITTI 
Autonomous Driving 
16 
COCO 
Common Objects in Context 
9 
Cityscapes 
Images from 50 different cities 
9 

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.

A Comprehensive Survey on Graph Neural Networks:
In this 22page review, the authors give a comprehensive overview of graph neural networks (GNN) in data mining and machine learning fields. They propose a new taxonomy to divide the stateoftheart graph neural networks into different categories. With a focus on graph convolutional networks, they review recently developed alternative architectures and discuss the applications of graph neural networks across various domains. They also summarize the open source codes and benchmarks of the existing algorithms on different learning tasks.

An introduction to domain adaptation and transfer learning:
The author introduces domain adaptation and transfer learning guided by the aim to generalize a classifier from a source to a target domain appropriately. The author starts with simpler dataset shifts, namely prior, covariate and concept shift. He then moves on to more complex domain shifts, including importanceweighting, subspace mapping, domaininvariant spaces, feature augmentation, minimax estimators and robust algorithms.

MidLevel Visual Representations Improve Generalization and Sample Efficiency for Learning Active Tasks:
One of the ultimate objectives of computer vision is to help robotic agents perform active tasks. The conventional approach using Deep Reinforcement Learning need to learn active tasks from scratch using images as input. The authors show that proper use of midlevel perception confers significant advantages over training from scratch. They implement a perception module as a set of midlevel visual representations and demonstrate that learning active tasks with midlevel features is significantly more sampleefficient than scratch and able to generalize in situations where the fromscratch approach fails. However, gaining these advantages requires careful selection of the particular midlevel features.

Frequent Words
"Learning", "Model", "Data" and "Training" are the most frequent words. The top two papers associated with each of the key words are:

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