**SUbgraph Robust REpresentAtion Learning (SURREAL)**

The success of graph embeddings or node representation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years. Representation learning algorithms aim to preserve local and global network structure by identifying node neighborhood notions. However, many existing algorithms generate embeddings that fail to properly preserve the network structure, or lead to unstable representations due to random processes (e.g., random walks to generate context) and, thus, cannot generate to multi-graph problems. In this paper, we propose a robust graph embedding using connection subgraphs algorithm, entitled: SURREAL, a novel, stable graph embedding algorithmic framework. SURREAL learns graph representations using connection subgraphs by employing the analogy of graphs with electrical circuits. It preserves both local and global connectivity patterns, and addresses the issue of high-degree nodes. Further, it exploits the strength of weak ties and meta-data that have been neglected by baselines. The experiments show that SURREAL outperforms state-of-the-art algorithms by up to 36.85% on multi-label classification problem. Further, in contrast to baselines, SURREAL, being deterministic, is completely stable. … **KAMILA Clustering (KAMILA)**

KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. … **Shake-Shake Regularization**

The method introduced in this paper aims at helping deep learning practitioners faced with an overfit problem. The idea is to replace, in a multi-branch network, the standard summation of parallel branches with a stochastic affine combination. Applied to 3-branch residual networks, shake-shake regularization improves on the best single shot published results on CIFAR-10 and CIFAR-100 by reaching test errors of 2.86% and 15.85%. Experiments on architectures without skip connections or Batch Normalization show encouraging results and open the door to a large set of applications. Code is available at https://…/shake-shake.

Review: Shake-Shake Regularization (Image Classification) … **Multi-Layer Fast ISTA (ML-FISTA)**

Parsimonious representations in data modeling are ubiquitous and central for processing information. Motivated by the recent Multi-Layer Convolutional Sparse Coding (ML-CSC) model, we herein generalize the traditional Basis Pursuit regression problem to a multi-layer setting, introducing similar sparse enforcing penalties at different representation layers in a symbiotic relation between synthesis and analysis sparse priors. We propose and analyze different iterative algorithms to solve this new problem in practice. We prove that the presented multi-layer Iterative Soft Thresholding (ML-ISTA) and multi-layer Fast ISTA (ML-FISTA) converge to the global optimum of our multi-layer formulation at a rate of $\mathcal{O}(1/k)$ and $\mathcal{O}(1/k^2)$, respectively. We further show how these algorithms effectively implement particular recurrent neural networks that generalize feed-forward architectures without any increase in the number of parameters. We demonstrate the different architectures resulting from unfolding the iterations of the proposed multi-layer pursuit algorithms, providing a principled way to construct deep recurrent CNNs from feed-forward ones. We demonstrate the emerging constructions by training them in an end-to-end manner, consistently improving the performance of classical networks without introducing extra filters or parameters. …