Rendering is explicitly set to 30 frames per second plus the poor performance of the Javascript memory allocator so expect small lag depending on your CPU/Browser configuration. ASCII art is a related (and older) graphic design technique for producing images from printable characters. Divide the input image into rectangular grid of equal size. The grid size correspond to the height and width of a single tile (i.e. For each cell, a font glyph is selected from the codebook to replace the raw pixels in this cell.

Medical and public understanding of bipolar disorder has come a long way since the days when the mental illness was called "manic depression." It is much better defined and more easily treated than it used to be, and while mental illnesses still have a certain stigma attached to them, more and more people are opening up about having bipolar disorder, hoping to break down negative perceptions. For those who suffer from the condition, diagnosis is a crucial first step. The National Institute of Mental Health notes the hurdles these people face, saying doctors may not properly identify bipolar disorder if the person appears to have psychotic symptoms like hallucinations or delusions as a result of a severe mood swing, the hallmark of the illness. That could include thinking they have special powers during a manic episode, or believing they have committed a crime during a depressive one.

A new endoscopic system powered by artificial intelligence (AI) has today been shown to automatically identify colorectal adenomas during colonoscopy. The system, developed in Japan, has recently been tested in one of the first prospective trials of AI-assisted endoscopy in a clinical setting, with the results presented today at the 25th UEG Week in Barcelona, Spain. The new computer-aided diagnostic system uses an endocytoscopic* image – a 500-fold magnified view of a colorectal polyp – to analyse approximately 300 features of the polyp after applying narrow-band imaging (NBI) mode or staining with methylene blue. The system compares the features of each polyp against more than 30,000 endocytoscopic images that were used for machine learning, allowing it to predict the lesion pathology in less than a second. Preliminary studies demonstrated the feasibility of using such a system to classify colorectal polyps, however, until today, no prospective studies have been reported.

Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factors cause major human diseases. In this work, we focus on two challenges in particular: How do we build richer causal models, which can capture highly nonlinear relationships and interactions between multiple causes? How do we adjust for latent confounders, which are variables influencing both cause and effect and which prevent learning of causal relationships? To address these challenges, we synthesize ideas from causality and modern probabilistic modeling. For the first, we describe implicit causal models, a class of causal models that leverages neural architectures with an implicit density. For the second, we describe an implicit causal model that adjusts for confounders by sharing strength across examples. In experiments, we scale Bayesian inference on up to a billion genetic measurements. We achieve state of the art accuracy for identifying causal factors: we significantly outperform existing genetics methods by an absolute difference of 15-45.3%.

Summary: Unless you're involved in anomaly detection you may never have heard of Unsupervised Decision Trees. It's a very interesting approach to decision trees that on the surface doesn't sound possible but in practice is the backbone of modern intrusion detection. I was at a presentation recently that focused on stream processing but the use case presented was about anomaly detection. When they started talking about unsupervised decision trees my antenna went up. What do you mean unsupervised decision trees?

We all use Decision Tree technique on daily basis to plan our life, we just don't give a fancy name to those decision-making process. Businesses use these supervised machine learning techniques like Decision trees to make better decisions and make more profit. Decision trees have been around for a long time and also known to suffer from bias and variance. You will have a large bias with simple trees and a large variance with complex trees. Ensemble methods, which combines several decision trees to produce better predictive performance than utilizing a single decision tree.

A decision tree is a tree-shaped diagram that shows statistical probability or determines a course of action. It shows the steps to take and why one choice may lead to another. Therefore, it is a suitable decision-making tool for research analysis or for planning the strategy to reach a goal. A decision tree has three main parts: a root node, leaf nodes, and branches. The root node is the target value that we are seeking to reach.

Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We developed a novel data-driven geological feature detection approach based on pre-stack seismic measurements. Our detection method employs an efficient and accurate machine-learning detection approach to extract useful subsurface geologic features automatically. Specifically, our method is based on kernel ridge regression model. The conventional kernel ridge regression can be computationally prohibited because of the large volume of seismic measurements. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage. In particular, we utilize a randomized numerical linear algebra technique, named Nystr\"om method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate detection. We provide thorough computational cost analysis to show efficiency of our new geological feature detection methods. We further validate the performance of our new subsurface geologic feature detection method using synthetic surface seismic data for 2D acoustic and elastic velocity models. Our numerical examples demonstrate that our new detection method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we show that our method yields a speed-up ratio on the order of $\sim10^2$ to $\sim 10^3$ in a multi-core computational environment.

As the data that is fed becomes larger, the decision tree tends to become longer. In such cases, noise and corrupt/incorrect data can have a detrimental impact on the decision tree. This results in the decision tree overfitting the dataset, that is decision tree performs satisfactory for the training data, but fails to produce an appropriate approximation of the target concept when it encounters actual data. Overfitting can also occur when insufficent data is provided to build the decision tree (like perhaps, our previous with only 6 rows.)

Decision Tree algorithm belongs to the family of supervised learning algorithms. Unlike other supervised learning algorithms, decision tree algorithm can be used for solving regression and classification problems too. The general motive of using Decision Tree is to create a training model which can use to predict class or value of target variables by learning decision rules inferred from prior data(training data). The understanding level of Decision Trees algorithm is so easy compared with other classification algorithms. The decision tree algorithm tries to solve the problem, by using tree representation.