I was recently reading a paper on fairness that was all math equations, and even as someone with a math PhD, I was having trouble mapping what this meant into the real world.

I have a model that's trained to detect various concepts on biomedical papers. I'm using the last dense layer before the output to use as vector embeddings for these medical papers. I then use k-means on the embedding vectors to cluster the papers together. I prefer this over a pure unsupervised approach as these embeddings will be forced to focus on biological similarity over biological & semantic. I have about 10M papers and each cluster needs to have 50 elements.

Anomaly Detection has several important applications. In this paper, our focus is on detecting anomalies in seller-reviewer data using tensor decomposition. While tensor-decomposition is mostly unsupervised, we formulate Bayesian semi-supervised tensor decomposition to take advantage of sparse labeled data. In addition, we use Polya-Gamma data augmentation for the semi-supervised Bayesian tensor decomposition. Finally, we show that the Polya-Gamma formulation simplifies calculation of the Fisher information matrix for partial natural gradient learning. Our experimental results show that our semi-supervised approach outperforms state of the art unsupervised baselines. And that the partial natural gradient learning outperforms stochastic gradient learning and Online-EM with sufficient statistics.

The topic modeling discovers the latent topic probability of the given text documents. To generate the more meaningful topic that better represents the given document, we proposed a new feature extraction technique which can be used in the data preprocessing stage. The method consists of three steps. First, it generates the word/word-pair from every single document. Second, it applies a two-way TF-IDF algorithm to word/word-pair for semantic filtering. Third, it uses the K-means algorithm to merge the word pairs that have the similar semantic meaning. Experiments are carried out on the Open Movie Database (OMDb), Reuters Dataset and 20NewsGroup Dataset. The mean Average Precision score is used as the evaluation metric. Comparing our results with other state-of-the-art topic models, such as Latent Dirichlet allocation and traditional Restricted Boltzmann Machines. Our proposed data preprocessing can improve the generated topic accuracy by up to 12.99\%.

Skip-gram with negative sampling, a popular variant of Word2vec originally designed and tuned to create word embeddings for Natural Language Processing, has been used to create item embeddings with successful applications in recommendation. While these fields do not share the same type of data, neither evaluate on the same tasks, recommendation applications tend to use the same already tuned hyperparameters values, even if optimal hyperparameters values are often known to be data and task dependent. We thus investigate the marginal importance of each hyperparameter in a recommendation setting, with an extensive joint hyperparameter optimization on various datasets. Results reveal that optimizing neglected hyperparameters, namely negative sampling distribution, number of epochs, subsampling parameter and window-size, significantly improves performance on a recommendation task, and can increase it up to a factor of $10$.

Recently, The Spectator, in association with NatWest, brought together leading entrepreneurs, MPs and technology writers to discuss where Artificial Intelligence (AI) – or the fourth industrial revolution as it is often termed – is taking us.

In the last 20 years, flatscreen TVs have gone from high-priced novelty item to household mainstay. Not even counting Black Friday, you can get perfectly acceptable TVs for just a couple hundred bucks, and really astounding ones for not much more. It's no secret that along the way, the flatscreen market has become absolutely saturated. This has lead to what I would call "feature jousting." Almost every year, flatscreen TVs ship with some new "must-have" feature: The ability to play 3D movies (including dork-tastic 3D glasses), or built-in web browsers and streaming apps, or remotes with built-in voice search.

Want to make films and videos that really catch attention and praise? This online Easy Filmmaking Course will teach you how to make great films and videos using proven techniques and approaches. This course is designed to teach you the ins and outs of easy film and video making by showing you the art and craft of making films and videos by showing you how to plan, design and put them together. While there are plenty of courses about making films and videos, it's hard to find a course that gives you a step-by-step insight into making films easily that really punch through the noise. This is the course for you, taught by a professional filmmaker who has personally make more than 40 productions.

TORONTO, April 10, 2018 /CNW/ – Canada's trust levels in government, leaders and most information sources remain stable and strong, but social media networks and the artificial intelligence (AI) sector are both at a crossroads, according to the 2018 Proof Inc. (formerly Environics Communications Inc.) …