Detecting and segmenting salient objects in natural scenes, often referred to as salient object detection, has attracted a lot of interest in computer vision. While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics in salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance and suggest future research directions.

In this article, we look at two machine learning (ML) techniques, Naive Bayes classifier and neural networks, and demystify how they work. With all the hype surrounding self-driving cars and video-game-playing AI robots, it's worth taking a step back and reminding ourselves how machine learning programs actually "learn". In this article, we look at two machine learning (ML) techniques–spam filters and neural networks–and demystify how they work. And if you're not sure what machine learning even is, read about the difference between artificial intelligence, machine learning, and deep learning. One common machine learning algorithm is the Naive Bayes classifier, which is used for filtering spam emails.

The "one-time-only" boxing match between a 40-year-old who retired two years ago and an Irishman making his pro debut in the sport is positioned to become the greatest-selling pay-per-view fight of all time Friday. Showtime Executive Vice President Stephen Espinoza said "it's too early to declare a hard number" but Saturday's Floyd Mayweather Jr.-Conor McGregor fight is "tracking in the mid-to-high 4 million pay-per view buys." "If we don't reach the record, we're going to be very, very close," and "we consider it a massive success." "It was an exciting, entertaining fight and there was massive interest," in it, Espinoza told The Times, crediting strong digital sales to boost the overall domestic sales. The bout is also expected to surpass the $600 million generated in total revenue by Mayweather's less-entertaining unanimous-decision triumph over seven-division champion Manny Pacquiao, with final pay-per-view numbers expected by next week.

This is great, in the sense that you don't have to worry about accidently forgetting to carry the 1 or remember how each rule in calculus operates. It is still great to have a general understanding of some of the equations you can utilize, distributions you can model and general statistics rules that can help clean up your data! We need to quickly lay out some definitions. In this post we will talk about discrete variables. If you have not heard the term before this references variables that are of a limited set. It actually could include numbers that are decimals pending on the set of variables you are using. However, these rules need to be established. For instance, you can't have 3.5783123 medical procedures in real life.

This paper presents a constructive algorithm that achieves successful one-shot learning of hidden spike-patterns in a competitive detection task. It has previously been shown (Masquelier et al., 2008) that spike-timing-dependent plasticity (STDP) and lateral inhibition can result in neurons competitively tuned to repeating spike-patterns concealed in high rates of overall presynaptic activity. One-shot construction of neurons with synapse weights calculated as estimates of converged STDP outcomes results in immediate selective detection of hidden spike-patterns. The capability of continual learning is demonstrated through the successful one-shot detection of new sets of spike-patterns introduced after long intervals in the simulation time. Simulation expansion (Lightheart et al., 2013) has been proposed as an approach to the development of constructive algorithms that are compatible with simulations of biological neural networks. A simulation of a biological neural network may have orders of magnitude fewer neurons and connections than the related biological neural systems; therefore, simulated neural networks can be assumed to be a subset of a larger neural system. The constructive algorithm is developed using simulation expansion concepts to perform an operation equivalent to the exchange of neurons between the simulation and the larger hypothetical neural system. The dynamic selection of neurons to simulate within a larger neural system (hypothetical or stored in memory) may be a starting point for a wide range of developments and applications in machine learning and the simulation of biology.

Did you pay for an expensive pay-per-view or streaming pass to watch the hyped-up boxing match between Floyd Mayweather and Conor McGregor, only to boil with rage as your access went down? Numerous reports have revealed that servers across the US crashed or buckled under demand for the fight, creating outages serious enough that organizers delayed the fight to make sure people could tune in. Mayweather himself said that pay-per-view servers in California and Florida crashed, while Showtime and UFC failed to load, ran into login trouble and otherwise couldn't keep up with interest. The pay-per-view issues at a minimum are known to have affected TV providers like Comcast, Atlantic Broadband and Frontier, although it's not clear how large the scope of the failures was at this stage. Problems like this aren't completely unprecedented -- Mayweather's fight against Manny Pacquiao created hiccups of its own.

Microsoft's speech recognition efforts have hit a significant milestone. It can now transcribe human speech with a 5.1% error rate, Microsoft technical fellow Xuedong Huang wrote in a blog post -- the same error rate as humans. Microsoft actually thought it hit this point last year, when it reached 5.9%, the word error rate it had measured for humans. But then other researchers carried out separate studies and pegged the human error level as slightly lower, 5.1%. But it has now achieved this -- reducing its error rate by 12%, and using AI techniques like "neural-net based acoustic and language models."

The biggest fight of 2017 and possibly the No.1 pay-per-view of all time is finally almost here. Floyd Mayweather and Conor McGregor will go head-to-head Saturday night at T-Mobile Arena in Las Vegas, pitting the greatest boxer of his generation against the biggest star in UFC history. It all starts at 9 p.m. EDT on Showtime PPV with three undercard bouts preceding the main event. Watching the fight on TV will cost fans $99.99, making for a potential record-setting night. Preliminary bouts are scheduled to start on FOX at 7 p.m. EDT, and those fights can be seen with a free live stream online with FOX Sports GO.

The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its applications to relatively simple phenomena. In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space. Both the neural parameters and Bayesian priors are learned jointly using tailored variational inference. The resulting model induces a hierarchical structure of latent semantic concepts underlying the data corpus, and infers accurate representations of data instances. We apply our model in video representation learning. Our method is able to discover highly interpretable activity hierarchies, and obtain improved clustering accuracy and generalization capacity based on the learned rich representations.

After Kubiiki Pride's 13-year-old daughter disappeared, it took 270 days for her mother to find her. When she did, it was as an escort available to be rented out on an online classified web site. Her daughter had been drugged and beaten into compliance by a sex trafficker. To find her, Pride had to trawl through hundreds of advertisements on Backpage.com, When it comes to identifying signs of human trafficking in online sex adverts, the task for police is often no easier.