This paper examines the problem of approximate graph matching (isomorphism). Given graphs G, H with p nodes, represented by respective adjacency matrices A, B, Find a permutation matrix P that best "matches" AP and PB. 

This paper poses the multimodal graph matching problem as a convex optimization problem, and solves it using augmented Langrangian techniques (viz., ADMM). This is an important problem with application in several fields. Experimental results on synthetic and multiple real world datasets demonstrate effectiveness of the proposed approach.

The Authors provide a bag of tricks for training GAN's in the image domain. Using these, they achieve very strong semi-supervised results on SHVN, MNIST, and CIFAR.

The authors then train the improved model on several images datasets, evaluate it on different tasks: semi-supervised learning, and generative capabilities, and achieve state-of-the-art results.

This paper investigates several techniques to stabilize GAN training and encourage convergence. Although lack of theoretical justification, the proposed heuristic techniques give better-looking samples. In addition to human judgement, the paper proposes a new metric called Inception score by applying pre-trained deep classification network on the generated samples. By introducing free labels with the generated samples as new category, the paper proposes the experiment using GAN under semi-supervised learning setting, which achieve SOTA semi-supervised performance on several benchmark datasets (MNIST, CIFAR-10, and SVHN).

This paper analyses fully connected networks with dropout and ReLU activation functions.

This is the best paper I have read that talking about imbalanced data. It is very comprehensive and practical. The best about this paper is that it uses very simple figures and easy-understanding languages to explain the complex ideas clearly. 

http://deliprao.com/archives/187