We propose a simple and general variant of the standard reparameterized gradient estimator for the variational evidence lower bound. Specifically, we remove a part of the total derivative with respect to the variational parameters that corresponds to the score function. Removing this term produces an unbiased gradient estimator whose variance approaches zero as the approximate posterior approaches the exact posterior. We analyze the behavior of this gradient estimator theoretically and empirically, and generalize it to more complex variational distributions such as mixtures and importance-weighted posteriors.

State-of-the-art techniques in Generative Adversarial Networks (GANs) such as cycleGAN is able to learn the mapping of one image domain $X$ to another image domain $Y$ using unpaired image data. We extend the cycleGAN to ${\it Conditional}$ cycleGAN such that the mapping from $X$ to $Y$ is subjected to attribute condition $Z$. Using face image generation as an application example, where $X$ is a low resolution face image, $Y$ is a high resolution face image, and $Z$ is a set of attributes related to facial appearance (e.g. gender, hair color, smile), we present our method to incorporate $Z$ into the network, such that the hallucinated high resolution face image $Y'$ not only satisfies the low resolution constrain inherent in $X$, but also the attribute condition prescribed by $Z$. Using face feature vector extracted from face verification network as $Z$, we demonstrate the efficacy of our approach on identity-preserving face image super-resolution. Our approach is general and applicable to high-quality face image generation where specific facial attributes can be controlled easily in the automatically generated results.

We are aware of the devastating effect HIV can have on the immune system, but a new studysuggests the virus may also adversely affect brain development. In groundbreaking research, scientists have found young HIV positive patients with limited or no treatment for their condition had significant disruptions in brain structure, poorer strength and efficiency of connections, and lessened cognitive performance, when compared to their peers without the virus. A new study published online in Brain Connectivity found that limited-treated or untreated HIV infections in young adults may affect their brain connections, which in turn compromises their cognitive abilities. The study found that, in addition to having visible structural and connectivity differences from uninfected peers, HIV positive young adults performed worse on cognitive tests involving tasks such as learning and recalling information. "The most interesting aspect of this study was that it was performed in young adults with limited or no history of HIV treatment."

The simplest approach to identifying irregularities in data is to flag the data points that deviate from common statistical properties of a distribution, including mean, median, mode, and quantiles. Let's say the definition of an anomalous data point is one that deviates by a certain standard deviation from the mean. Traversing mean over time-series data isn't exactly trivial, as it's not static. You would need a rolling window to compute the average across the data points. Technically, this is called a rolling average or a moving average, and it's intended to smooth short-term fluctuations and highlight long-term ones.

With more than 40% market share in mobile games, billion monthly active users and 2.6 billion unique devices, Unity plays a profoundly important role in the booming gaming and VR markets. Introducing machine learning and AI into game development will curb one of the greatest expenses in time and money that go into content creation, but will also change the role of programmers. With immense access to data and computing power, developers will use historical data to model extensive virtual realities of games, taking programming out of the equation. I spoke with Dr Danny Lange, VP of AI and machine learning at Unity about this, the present and future of game development, machine learning, and how developers and startups can get most out of it. Dr Danny Lange is VP of AI and machine learning at Unity Technologies.

I recommend using the "pip" Python package manager, which will allow you to simply run "pip3 install packagename " to install each of the dependencies: For actually writing and running the code I recommend using IPython, which will allow you to run modular blocks of code and immediately the view output values and data visualizations, along with the Jupyter Notebook as a graphical interface. With all of the dependencies installed, simply run "jupyter notebook" on the command line, from the same directory as the titanic3.xls The Data At First Glance: Who Survived The Titanic, And Why? Before we can feed our dataset into a machine learning algorithm, we have to remove missing values and split it into training and test sets. Interestingly, after splitting by class, the main deciding factor determining the survival of women is the ticket fare that they paid, while the deciding factor for men is their age(with children being much more likely to survive).

A disembodied voice sounded over a loudspeaker. Take cover," it warned to anyone within earshot. Then, the sirens began to wail. Erin Delaney assumed it was a drill. She peeked down the hallway to see how other people were responding. Then she hit the deck. The NATO base in Kabul where Delaney had been working for weeks was being attacked. Delaney, 24, had never had any military training. She grew up in San Diego, traveled up the coast for college at UC Berkeley, and spent the next two years nestled in the safe, Tesla-filled San Francisco bubble, working in the compliance department at Dropbox. Now, with her nose to the ground, she was getting a taste--however brief--of life in a war zone. She flipped over the visitor's badge she'd received when she first arrived at the base. In case of attack, it said, she should stay on the ground for two minutes. Assuming nothing dire happened, she was to shelter in place until the shelling stopped. So, for about an hour, that's what she did.

With more than 40% market share in mobile games, billion monthly active users and 2.6 billion unique devices, Unity plays a profoundly important role in the booming gaming and VR markets. Introducing machine learning and AI into game development will curb one of the greatest expenses in time and money that go into content creation, but will also change the role of programmers. With immense access to data and computing power, developers will use historical data to model extensive virtual realities of games, taking programming out of the equation. I spoke with Dr. Danny Lange, VP of AI and machine learning at Unity about this, the present and future of game development, machine learning, and how developers and startups can get most out of it. Dr. Danny Lange is VP of AI and machine learning at Unity Technologies.

The world's No.1 weiqi (Go) player Ke Jie lost the contest against his artificial intelligence (AI) rival, AlphaGo, in the third and also final match of the summit on Saturday. This match began at 10:30 BJT in Wuzhen, east China's Zhejiang Province, with AlphaGo playing the black and Ke white. Ke showed his brilliant weiqi skills as he said he will "fight till the end," though he lost his previous two matches against AlphaGo on Tuesday and Thursday. AlphaGo made the first "impolite" move as it did on Thursday – to put the black stone on the bottom-right corner of the weiqi board. It is a Chinese tradition that the first stone is usually placed around the top-right corner and this is what weiqi coaches always teach beginners.

How much maths do I need to learn to be a data scientist? Even though the question sounds simple, there is no simple answer to the the question. Usually, we say that you need to know basic descriptive and inferential statistics to start. That is good to start. But, once you have covered the basic concepts in machine learning, you will need to learn some more math. You need it to understand how these algorithms work. What are their limitations and in case they make any underlying assumptions. Now, there could be a lot of areas to study including algebra, calculus, statistics, 3-D geometry etc. If you get confused (like I did) and ask experts what should you learn at this stage, most of them would suggest / agree that you go ahead with Linear Algebra. But, the problem does not stop there. The next challenge is to figure out how to learn Linear Algebra. You can get lost in the detailed mathematics and derivation and learning them would not help as much! I went through that journey myself and hence decided to write this comprehensive guide. If you have faced this question about how to learn & what to learn in Linear Algebra – you are at the right place. I would like to present 4 scenarios to showcase why learning Linear Algebra is important, if you are learning Data Science and Machine Learning. What do you see when you look at the image above? You most likely said flower, leaves -not too difficult. But, if I ask you to write that logic so that a computer can do the same for you – it will be a very difficult task (to say the least).