The commonly accepted definition of a community is that nodes tend to be more densely connected within a community than with the rest of the graph. Communities are often hidden in practice and recovering the community structure directly from the graph is a key step in the analysis of these datasets. Spectral algorithms are popular methods for detecting communities [26], that consist in two phases. First, a spectral embedding is built, where then nodes of the graph are projected onto some low dimensional space generated by well-chosen eigenvectors of some matrix related to the graph (e.g., the adjacency matrix or a Laplacian matrix). Then, a clustering algorithm (e.g.,k -means ork -median) is applied to then embedded vectors to obtain a partition of the nodes into communities.

Deep Neural Networks (DNNs) are universal function approximators providing state-of- the-art solutions on wide range of applications. Common perceptual tasks such as speech recognition, image classification, and object tracking are now commonly tackled via DNNs. Some fundamental problems remain: (1) the lack of a mathematical framework providing an explicit and interpretable input-output formula for any topology, (2) quantification of DNNs stability regarding adversarial examples (i.e. modified inputs fooling DNN predictions whilst undetectable to humans), (3) absence of generalization guarantees and controllable behaviors for ambiguous patterns, (4) leverage unlabeled data to apply DNNs to domains where expert labeling is scarce as in the medical field. Answering those points would provide theoretical perspectives for further developments based on a common ground. Furthermore, DNNs are now deployed in tremendous societal applications, pushing the need to fill this theoretical gap to ensure control, reliability, and interpretability.

In this paper, we revisit the recently established theoretical guarantees for the convergence of the Langevin Monte Carlo algorithm of sampling from a smooth and (strongly) log-concave density. We improve, in terms of constants, the existing results when the accuracy of sampling is measured in the Wasserstein distance and provide further insights on relations between, on the one hand, the Langevin Monte Carlo for sampling and, on the other hand, the gradient descent for optimization. More importantly, we establish non-asymptotic guarantees for the accuracy of a version of the Langevin Monte Carlo algorithm that is based on inaccurate evaluations of the gradient. Finally, we propose a variable-step version of the Langevin Monte Carlo algorithm that has two advantages. First, its step-sizes are independent of the target accuracy and, second, its rate provides a logarithmic improvement over the constant-step Langevin Monte Carlo algorithm.

While strong progress has been made in image captioning recently, machine and human captions are still quite distinct. This is primarily due to the deficiencies in the generated word distribution, vocabulary size, and strong bias in the generators towards frequent captions. Furthermore, humans - rightfully so - generate multiple, diverse captions, due to the inherent ambiguity in the captioning task which is not explicitly considered in today's systems. To address these challenges, we change the training objective of the caption generator from reproducing groundtruth captions to generating a set of captions that is indistinguishable from human written captions. Instead of handcrafting such a learning target, we employ adversarial training in combination with an approximate Gumbel sampler to implicitly match the generated distribution to the human one. While our method achieves comparable performance to the state-of-the-art in terms of the correctness of the captions, we generate a set of diverse captions that are significantly less biased and better match the global uni-, bi-and trigram distributions of the human captions.

"Introduction to TensorFlow [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 1] [Rimini, Italy] Deep learning is at its peak, with scholars and startups releasing new amazing applications every other week, and TensorFlow is the main tool to work with it. In this talk, we will cover the explanation of core concepts of deep learning and TensorFlow totally from scratch, using simple examples and friendly visualizations. The talk will go through the next topics: • Why deep learning and what is it?

We are currently seeking a hands-on Machine Learning Scientist (Distributed Systems, Tensorflow) for our new research-led startup, focussing on the application of artificial intelligence in the real world; particularly smart city simulations and bots. We're looking for a hardcore Machine Learning Scientist/Engineer who thrives wants to work with the latest technology in multi-agent learning algorithms, Gaussian process and reinforcement learning. As a Machine Learning Scientist/Engineer, you will be a core member of the machine learning team; working closely with the Machine Learning researchers, transforming their algorithmic research into highly innovative products which will be attractive and accessible to the world. Key Skills: Machine Learning Engineer/ML Scientist, Tensorflow, C, C, Java, Python, C#, Distributed Algorithms. Distributed systems, BSc, MSc, MPhil, PhD, Post-Doc, Research, R&D, startup, Multithreading.

Ovarian cancer is difficult to diagnose, particularly in its early stages, when survival rates are much higher. Because there is no consistently reliable screening test to detect ovarian cancer, most women are diagnosed with the disease when it's in an advanced stage. However, researchers at Brigham and Women's Hospital and Dana-Farber Cancer Institute have developed a non-invasive diagnostic test using artificial intelligence for the accurate detection of true cases of early-stage disease. Results of their study were published online this week in the journal eLife. By combining next generation sequencing with artificial intelligence, researchers have created a novel blood test based on serum microRNAs--small, non-coding pieces of genetic material that help control where and when genes are activated--for the early diagnosis of ovarian cancer.

You're lying on your stomach, with your arms draped forwards, almost like you're going to get a shoulder massage. Except this is not a moment for relaxation. Through a VR headset, you see flashes of color, an unfamiliar view of the world, a group of red lines that looks something like a person. And now you have to make a decision, because you're rolling forward, head first, and your right hand is wrapped around the joystick that determines which way you're going. Do you continue forward, and risk hitting that blob that might be a human being?

The Budget will cut a planned 3.9 per cent hike in business rates and pave the way for building houses on the green belt, it was claimed today. Philip Hammond delivers his next Budget on November 22 against a backdrop of economic uncertainty over Brexit and needing to find billions to unwind earlier errors. Instead of increasing business rates by the RPI measure of inflation, the Chancellor will tell firms he will use the lower CPI. The move will save businesses hundreds of millions of pounds when the next round of rates kicks in from April. Talks between Mr Hammond and Communities Secretary Sajid Javid convened by Prime Minister Theresa May have also yielded agreement on housing plans, the Sunday Times said.

Art has always transcended eons of human existence. We can see its traces from pre-historic time as the Harappan art in the Indus Valley Civilization to the contemporary art in modern times. Mostly, art has been a means to express one's creativity, viewpoints of how we perceive the world. "Painting is poetry that is seen rather than felt". What we sometimes forget that most of the art follows a pattern.