Fast Fourier transform, Wavelets, and other well-known transforms in signal processing have a structured representation as a product of sparse matrices which are referred to as butterfly structures. Research in the recent past have used such structured linear networks along with randomness as pre-conditioners to improve the computational performance of large scale linear algebraic operations. With the advent of deep learning and AI and the computational efficiency of such structured matrices, it is natural to study sparse linear deep networks in which the location of the non-zero weights are predetermined by the butterfly structure. This work studies, both theoretically and empirically, the feasibility of training such networks in different scenarios. Unlike convolutional neural networks, which are structured sparse networks designed to recognize local patterns in lattices representing a spatial or a temporal structure, the butterfly architecture used in this work can replace any dense linear operator with a gadget consisting of a sequence of logarithmically (in the network width) many sparse layers, containing a total of near linear number of weights. This improves on the quadratic number of weights required in a standard dense layer, with little compromise in expressibility of the resulting operator. We show in a collection of empirical experiments that our proposed architecture not only produces results that match and often outperform existing known architectures, but it also offers faster training and prediction in deployment. This empirical phenomenon is observed in a wide variety of experiments that we report, including both supervised prediction on NLP and vision data, as well as in unsupervised representation learning using autoencoders. Preliminary theoretical results presented in the paper explain why training speed and outcome are not compromised by our proposed approach.

Semantic hashing is an emerging technique for large-scale similarity search based on representing high-dimensional data using similarity-preserving binary codes used for efficient indexing and search. It has recently been shown that variational autoencoders, with Bernoulli latent representations parametrized by neural nets, can be successfully trained to learn such codes in supervised and unsupervised scenarios, improving on more traditional methods thanks to their ability to handle the binary constraints architecturally. However, the scenario where labels are scarce has not been studied yet. This paper investigates the robustness of hashing methods based on variational autoencoders to the lack of supervision, focusing on two semi-supervised approaches currently in use. The first augments the variational autoencoder's training objective to jointly model the distribution over the data and the class labels. The second approach exploits the annotations to define an additional pairwise loss that enforces consistency between the similarity in the code (Hamming) space and the similarity in the label space. Our experiments show that both methods can significantly increase the hash codes' quality. The pairwise approach can exhibit an advantage when the number of labelled points is large. However, we found that this method degrades quickly and loses its advantage when labelled samples decrease. To circumvent this problem, we propose a novel supervision method in which the model uses its label distribution predictions to implement the pairwise objective. Compared to the best baseline, this procedure yields similar performance in fully supervised settings but improves the results significantly when labelled data is scarce. Our code is made publicly available at https://github.com/amacaluso/SSB-VAE.

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for the graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

Self-supervision as an emerging technique has been employed to train convolutional neural networks (CNNs) for more transferrable, generalizable, and robust representation learning of images. Its introduction to graph convolutional networks (GCNs) operating on graph data is however rarely explored. In this study, we report the first systematic exploration and assessment of incorporating self-supervision into GCNs. We first elaborate three mechanisms to incorporate self-supervision into GCNs, analyze the limitations of pretraining & finetuning and self-training, and proceed to focus on multi-task learning. Moreover, we propose to investigate three novel self-supervised learning tasks for GCNs with theoretical rationales and numerical comparisons. Lastly, we further integrate multi-task self-supervision into graph adversarial training. Our results show that, with properly designed task forms and incorporation mechanisms, self-supervision benefits GCNs in gaining more generalizability and robustness. Our codes are available at https://github.com/Shen-Lab/SS-GCNs.

User behavior modeling is important for industrial applications such as demographic attribute prediction, content recommendation, and target advertising. Existing methods represent behavior log as a sequence of adopted items and find sequential patterns; however, concrete location and time information in the behavior log, reflecting dynamic and periodic patterns, joint with the spatial dimension, can be useful for modeling users and predicting their characteristics. In this work, we propose a novel model based on graph neural networks for learning user representations from spatiotemporal behavior data. A behavior log comprises a sequence of sessions; and a session has a location, start time, end time, and a sequence of adopted items. Our model's architecture incorporates two networked structures. One is a tripartite network of items, sessions, and locations. The other is a hierarchical calendar network of hour, week, and weekday nodes. It first aggregates embeddings of location and items into session embeddings via the tripartite network, and then generates user embeddings from the session embeddings via the calendar structure. The user embeddings preserve spatial patterns and temporal patterns of a variety of periodicity (e.g., hourly, weekly, and weekday patterns). It adopts the attention mechanism to model complex interactions among the multiple patterns in user behaviors. Experiments on real datasets (i.e., clicks on news articles in a mobile app) show our approach outperforms strong baselines for predicting missing demographic attributes.

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters, in the long run, is the leveraging of computation. These two need not run counter to each other, but in practice, they tend to.

About Our Team: At our core, we are an analytics company. The insights we gain from our analyses guide our strategic path forward as we grow revenue, enter new markets, and strengthen our customer relationships. The Data Science team proactively leads and collaborates to identify the most valuable problems to solve, constructs a Roadmap to delivery, and executes our plan. All of the outputs of the Data Science team models will feed into the Product portfolio at DRIVIN, aiding tens of thousands of internal and external stakeholders in their decision-making processes. About Our Candidate: Can think for themselves and discover new and insightful ways to solve difficult problems without having a clear roadmap laid out Can communicate effectively with data science teammates and non-technical audiences alike Can deliver quality code in an Agile framework that ships to a production environment Has confidence, hustle, energy, and drive – accountability is key and the impact of your work is crucial to our success What You Will Be Doing: Own the machine learning engineering required to efficiently operate computer vision, optimization, and other data driven decision systems Partner with our computer vision and data scientists to improve model performance and responsiveness Work in an Agile environment with team members, delivering solutions quickly and continuously exploring ways to improve our results Work closely with colleagues in Engineering, Product, Operations, and Sales to structure problems and understand the impact across various departments within the company What You Need to Be Successful: Candidates tend to have at least a Bachelor's Degree in a quantitative field, but if you can explain how your experience and background can be leveraged as a senior contributor to a data science team we are all ears Significant experience (roughly 5 years) in a Data Science, Deep Learning, Machine Learning Engineering, Computer Vision, or Data Engineering position building data products in a production environment Experience in the full project lifecycle from requirements gathering to proof of concept to production delivery Experience designing and implementing machine learning models that are production-ready Familiarity with designing and implementing models for computer vision such as neural networks using deep learning frameworks like PyTorch or TensorFlow Experience coding with Python and SQL Familiarity with CUDA and C Experience with developing within a cloud environment.

Humans make mistakes because we get tired, distracted and overwhelmed. We miss our flights because we confuse 6 p.m. and 16:00; we use salt instead of sugar. We take it as an inevitability and deal with it. Well, with artificial intelligence (AI), we don't have to. The beauty of AI is that it acts like a perfect mind.

Dr. Arno Candel is Chief Architect at H2O.ai. He is considered one of the leading deep learning experts. He has over a decade of experience in high-performance computing. In past he has designed and implemented high-performance machine learning algorithms. Arno was named 2014 Big Data All-Star by Fortune Magazine.

Machine learning is all about computations, and libraries help machine learning researchers and developers to perform the computational tasks without repeating the complex lines of codes. It helps coders to run algorithms quickly. There are a plethora of libraries present in the field of machine learning and deep learning which makes it more accessible for the researchers to work with complex projects. In this article, we list down the top 10 libraries in C and C for machine learning. About: TensorFlow is a popular open-source software library for machine learning.