The authors build their work on top of the generalized conditional gradient (GCG) method for sparse optimization. In particular, GCG methods require computation of the polar operator for the sparse regularization function (an example is the dual norm if the regularization function is an atomic norm). In this work, the authors identify a class of regularization functions, which are based on an underlying subset cost function. The key idea is a to 'lift' the regularizer into a higher dimensional space together with some constraints in the higher-dimensional space, where it has the property of 'marginalized modularity' allowing it to be reformulated as a linear program. Finally, the approach is generalized to general proximal objectives. The results demonstrate that the method is able to achieve better objective values in much less CPU time when compared with another polar operator method and accelerated proximal gradient (APG) on group Lasso and path coding problems. 

This paper discusses an important bias in evaluation of methods using cross-validation. A method that makes decisions based of cross validation can appear to increase overall performance by simply dealing with the bias of cross-validation and not the real problem.

Problem
---------------
Video generation of human motion given:
1. Single appearance reference image
2. Skeleton motion sequence


Datasets
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* KTH - grayscale human actions
* Human3.6M - color multiview human actions


Approach
---------------
Conditional GANs.
The authors try both Stack GAN and Siamese GAN.
The later provides better result.

https://preview.ibb.co/ighxQQ/Skeleton_aided_Articulated_Motion_Generation.png

Questions
----------------
Isn't using a full sequence of human skeleton motion considered more then a "hint"?

From the late 1980s onward, the term “bioinformatics” mostly has been used to refer to computational methods for comparative analysis of genome data. However, the term was originally more widely defined as the study of informatic processes in biotic systems. In this essay, author traces this early history (from a personal point of view) and argues that the original meaning of the term is re-emerging.

Inducing word translations using only monolingual corpora for two languages. Separate embeddings are trained for each language and a mapping is learned though an adversarial objective, along with an orthogonality constraint on the most frequent words. A strategy for an unsupervised stopping criterion is also proposed.

https://i.imgur.com/HmME09P.png