Regularized autoencoders learn the latent codes, a structure with the regularization under the distribution, which enables them the capability to infer the latent codes given observations and generate new samples given the codes. However, they are sometimes ambiguous as they tend to produce reconstructions that are not necessarily faithful reproduction of the inputs. The main reason is to enforce the learned latent code distribution to match a prior distribution while the true distribution remains unknown. To improve the reconstruction quality and learn the latent space a manifold structure, this work present a novel approach using the adversarially approximated autoencoder (AAAE) to investigate the latent codes with adversarial approximation. Instead of regularizing the latent codes by penalizing on the distance between the distributions of the model and the target, AAAE learns the autoencoder flexibly and approximates the latent space with a simpler generator. The ratio is estimated using generative adversarial network (GAN) to enforce the similarity of the distributions. Additionally, the image space is regularized with an additional adversarial regularizer. The proposed approach unifies two deep generative models for both latent space inference and diverse generation. The learning scheme is realized without regularization on the latent codes, which also encourages faithful reconstruction. Extensive validation experiments on four real-world datasets demonstrate the superior performance of AAAE. In comparison to the state-of-the-art approaches, AAAE generates samples with better quality and shares the properties of regularized autoencoder with a nice latent manifold structure.

In this work I propose a new way of using fully convolutional networks for classification while allowing for input of any size. I additionally propose two modifications on the idea of attention and the benefits and detriments of using the modifications. Finally, I show suboptimal results on the ITAmoji 2018 tweet to emoji task and provide a discussion about why that might be the case as well as a proposed fix to further improve results.

While reinforcement learning (RL) has the potential to enable robots to autonomously acquire a wide range of skills, in practice, RL usually requires manual, per-task engineering of reward functions, especially in real world settings where aspects of the environment needed to compute progress are not directly accessible. To enable robots to autonomously learn skills, we instead consider the problem of reinforcement learning without access to rewards. We aim to learn an unsupervised embedding space under which the robot can measure progress towards a goal for itself. Our approach explicitly optimizes for a metric space under which action sequences that reach a particular state are optimal when the goal is the final state reached. This enables learning effective and control-centric representations that lead to more autonomous reinforcement learning algorithms. Our experiments on three simulated environments and two real-world manipulation problems show that our method can learn effective goal metrics from unlabeled interaction, and use the learned goal metrics for autonomous reinforcement learning.

We address the problem of graph classification based only on structural information. Most standard methods require either the pairwise comparisons of all graphs in the dataset or the extraction of ad-hoc features to perform classification. Those methods respectively raise scalability issues when the number of samples in the dataset is large, and flexibility issues when discriminative information is characterized by exotic features. Recent advances in neural network architectures offer new possibilities for graph analysis in terms of scalability and feature learning. In this paper, we propose a new sequential approach using recurrent neural networks (RNN). Our model sequentially embeds information allowing to model final class membership probabilities. We also propose a regularization based on variational node prediction ending up with better learning and generalization. We experimentally show that our model reaches state-of-the-art classification results on several common molecular datasets. Finally, we perform a qualitative analysis and give some insights about how the joint node prediction helps the model to better classify graphs.

Circuit obfuscation is a recently proposed defense mechanism to protect digital integrated circuits (ICs) from reverse engineering by using camouflaged gates i.e., logic gates whose functionality cannot be precisely determined by the attacker. There have been effective schemes such as satisfiability-checking (SAT)-based attacks that can potentially decrypt obfuscated circuits, called deobfuscation. Deobfuscation runtime could have a large span ranging from few milliseconds to thousands of years or more, depending on the number and layouts of the ICs and camouflaged gates. And hence accurately pre-estimating the deobfuscation runtime is highly crucial for the defenders to maximize it and optimize their defense. However, estimating the deobfuscation runtime is a challenging task due to 1) the complexity and heterogeneity of graph-structured circuit, 2) the unknown and sophisticated mechanisms of the attackers for deobfuscation. To address the above mentioned challenges, this work proposes the first machine-learning framework that predicts the deobfuscation runtime based on graph deep learning techniques. Specifically, we design a new model, ICNet with new input and convolution layers to characterize and extract graph frequencies from ICs, which are then integrated by heterogeneous deep fully-connected layers to obtain final output. ICNet is an end-to-end framework which can automatically extract the determinant features for deobfuscation runtime. Extensive experiments demonstrate its effectiveness and efficiency.

Tech leaders need to put AI and its subcategories into practice–and into common business vocabulary that everyone can understand.

There are a record amount of conferences about Machine Learning and Deep Learning worldwide. To help you decide which conference to attend, sponsor or submit talks to, we created this summary with the most notable conferences. For some of them we added tags, remarkable speakers and discount codes. Please let us know in the comment section if you think of a conference that should be listed here. Want to be notified when new conferences are added to this list?

Starting in March 2019, the course will take you on a tour of how automation can revolutionise work in government from robotic process automation via machine learning to natural language processing and deep learning, covering ethics in emerging technology and how to get started in your organisation.

We have been developing Ludwig internally at Uber over the past two years to streamline and simplify the use of deep learning models in applied projects, as they usually require comparisons among different architectures and fast iteration. We have witnessed its value to several of Uber's own projects, including our Customer Obsession Ticket Assistant (COTA), information extraction from driver licenses, identification of points of interest during conversations between driver-partners and riders, food delivery time prediction, and much more. For this reason we decided to release it as open source, as we believe there is no other solution currently available with the same ease of use and flexibility.

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC. Previous editions can be accessed here: Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7 Part 8. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.