Such behavior is desirable in multivariate structured prediction tasks, where handcrafted per-pixel loss-based methods inadequately capture strong correlations between output dimensions. CNFs are efficient in sampling and inference, they can be trained with a likelihood-based objective, and CNFs, being generative flows, do not suffer from mode collapse or training instabilities. We provide an effective method to train continuous CNFs for binary problems and in particular, we apply these CNFs to super-resolution and vessel segmentation tasks demonstrating competitive performance on standard benchmark datasets in terms of likelihood and conventional metrics. When the output y is high-dimensional this is a particularly challenging task, and the practitioner is left with many design choices. Do we factorize the conditional? If not, do we model correlations with, say, a conditional random field (Prince, 2012)? Do we use a unimodal distribution? How fat should the tails be? Do we use an explicit likelihood at all, or use implicit methods (Mohamed & Rezende, 2015) such as a GAN (Goodfellow et al., 2014)? Do we quantize the output?

The idea behind procedural content generation (PCG) in games is to create content automatically, using algorithms, instead of relying on user-designed content. While PCG approaches have traditionally focused on creating content for video games, they are now being applied to all kinds of virtual environments, thereby enabling training of machine learning systems that are significantly more general. For example, PCG's ability to generate never-ending streams of new levels has allowed DeepMind's Capture the Flag agent to reach beyond human-level-performance. Additionally, PCG-inspired methods such as domain randomization enabled OpenAI's robot arm to learn to manipulate objects with unprecedented dexterity. Level generation in 2D arcade games has also illuminated some shortcomings of standard deep RL methods, suggesting potential ways to train more general policies. This Review looks at key aspect of PCG approaches, including its ability to (1) enable new video games (such as No Man's Sky), (2) create open-ended learning environments, (3) combat overfitting in supervised and reinforcement learning tasks, and (4) create better benchmarks that could ultimately spur the development of better learning algorithms. We hope this article can introduce the broader machine learning community to PCG, which we believe will be a critical tool in creating a more general machine intelligence.

One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance. Aerosols provide the `seeds' on which cloud droplets form, and changes in the amount of aerosol available to a cloud can change its brightness and other physical properties such as optical thickness and spatial extent. Clouds play a critical role in moderating global temperatures and small perturbations can lead to significant amounts of cooling or warming. Uncertainty in this effect is so large it is not currently known if it is negligible, or provides a large enough cooling to largely negate present-day warming by CO2. This work uses deep convolutional neural networks to look for two particular perturbations in clouds due to anthropogenic aerosol and assess their properties and prevalence, providing valuable insights into their climatic effects.

Epilepsy is a n important public health issue. An appropriate epileptiform discharge pattern detectio n of this neurological disease is a typical problem in biomedical engineering. In this paper, a new method is proposed for spike - and - wave discharge pattern dete ction based on Kendall's Tau - b c oefficient. The proposed approach is demonstrated on a real data set containing spike - and - wave discharge signals, where our performance is evaluated in terms of high Specificity, rule in (SpPIn) with 94% for patient - specific spike - and - wave discharge detection and 83% for a general spike - and - wave discharge detection. Key words: Spike - and - wave discharge; Kendall's Tau - b c oefficient; Electroencephalography ( EEG); Epilepsy; high Specificity, rule in ( SpPIn) Introduction Electroencephalography (EEG) is widely used to record the electrical activity of the brain in neurological health centers.

By Country (United States; Canada; Mexico and Rest of North America) 6.3.

Meili Gupta is about to ask another question. A poised and eloquent rising senior at elite boarding school Phillips Exeter Academy, Gupta, 17, is anything but the introverted, soft-spoken techie stereotype. She does, however, know as much about computer science as any high school student you'd ever meet. She even grew up faithfully reading the MIT Technology Review, the university's flagship publication, which shows, because Meili is the most ubiquitous student attendee at EmTech Next, a conference the publication held on campus this past summer on AI, Machine Learning, and "the future of work." Ostensibly, the conference is an opportunity for executives and tech professionals to rub elbows while determining how next-generation technologies will shape our jobs and economy in the coming decades. For me, the gathering feels more like an opportunity to have an existential crisis; I could even say a religious crisis, though I'm not just a confirmed atheist but a professional one as well.

"The problem is not AI per se – but that this technology is developed in a biased context around gender, race and class. We need to build systems around the values we want our present and future societies to have." "A critical analysis of AI implies a close investigation of network structures and multiple layers of computational systems. It is our responsibility as researchers, activists and experts on digital rights to provoke awareness by reflecting on possible countermeasures that come from the technological, political, and artistic framework." Did you report on this topic?

Humans have been building machines to separate waste into different streams of different value requiring differing processes for decades. Until recently, we were mostly failing to do it well enough to be worth the investment. Instead, millions of people globally manually sort trash, sometimes with developed country workplace safety standards, sometimes living in developing country trash fields and scraping a living out of them. In London in the 1850s, when the population was roughly 3 million, a thousand rag and bone men plied their trade, greasy bags over their shoulders or slung on rough carts, picking through the detritus of the city to find enough items of value to allow them to pay for their lodging and food. In 1988, the World Bank estimated that 1-2% of the global population made most or all of its living picking through waste.

The performance of financial market prediction systems depends heavily on the quality of features it is using. While researchers have used various techniques for enhancing the stock specific features, less attention has been paid to extracting features that represent general mechanism of financial markets. In this paper, we investigate the importance of extracting such general features in stock market prediction domain and show how it can improve the performance of financial market prediction. We present a framework called U-CNNpred, that uses a CNN-based structure. A base model is trained in a specially designed layer-wise training procedure over a pool of historical data from many financial markets, in order to extract the common patterns from different markets. Our experiments, in which we have used hundreds of stocks in S\&P 500 as well as 14 famous indices around the world, show that this model can outperform baseline algorithms when predicting the directional movement of the markets for which it has been trained for. We also show that the base model can be fine-tuned for predicting new markets and achieve a better performance compared to the state of the art baseline algorithms that focus on constructing market-specific models from scratch.

Processes are a crucial artefact in organizations, since they coordinate the execution of activities so that products and services are provided. The use of models to analyse the underlying processes is a well-known practice. However, due to the complexity and continuous evolution of their processes, organizations need an effective way of analysing the relation between processes and models. Conformance checking techniques asses the suitability of a process model in representing an underlying process, observed through a collection of real executions. One important metric in conformance checking is to asses the precision of the model with respect to the observed executions, i.e., characterize the ability of the model to produce behavior unrelated to the one observed. In this paper we present the notion of anti-alignment as a concept to help unveiling runs in the model that may deviate significantly from the observed behavior. Using anti-alignments, a new metric for precision is proposed. In contrast to existing metrics, anti-alignment based precision metrics satisfy most of the required axioms highlighted in a recent publication. Moreover, a complexity analysis of the problem of computing anti-alignments is provided, which sheds light into the practicability of using anti-alignment to estimate precision. Experiments are provided that witness the validity of the concepts introduced in this paper.