Neural Information Processing Systems http://nips.cc/

We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse.

We present a new approach to classification that combines data and knowledge. In this approach, data mining is used to derive association rules (possibly with negations) from data. Those rules are leveraged to increase the predictive performance of tree-based models (decision trees and random forests) used for a classification task. They are also used to improve the corresponding explanation task through the generation of abductive explanations that are more general than those derivable without taking such rules into account. Experiments show that for the two tree-based models under consideration, benefits can be offered by the approach in terms of predictive performance and in terms of explanation sizes.

Some time series in Nature have a very long memory (Robinson, 2003): fluid turbulence (Resagk et al., 2006), asset price volatility (Cont, 2001) and tick-by-tick events in financial markets (Challet and Stinchcombe, 2001; Lillo and Farmer, 2004). From a modelling point of view, this means that the current value of an observable of interest depends on the past by a convolution of itself with a long-tailed kernel. Deep learning tackles past dependence in time series with recurrent neural networks (RNNs). These networks are in essence moving averages of nonlinear functions of the inputs and learn the parameters of these averages and functions. Provided that they are sufficiently large, these networks can approximate long-tailed kernels in a satisfactory way, and are of course able to account for more complex problems than a simple linear convolution.

We show how randomly scrambling the output classes of various fractions of the training data may be used to improve predictive accuracy of a classification algorithm. We present a method for calculating the "noise sensitivity signature" of a learning algorithm which is based on scrambling the output classes. This signature can be used to indicate a good match between the complexity of the classifier and the complexity of the data. Use of noise sensitivity signatures is distinctly different from other schemes to avoid over(cid:173) training, such as cross-validation, which uses only part of the train(cid:173) ing data, or various penalty functions, which are not data-adaptive. Noise sensitivity signature methods use all of the training data and are manifestly data-adaptive and non-parametric.

Network effects have dictated the success of technologies from the telephone to shopping platforms like Etsy, and AI tools such as ChatGPT are no exception. What is different, however, is how those network effects work. Data network effects are a new form. Like the more familiar direct and indirect network effects, the value of the technology increases as it gains users. Here, however, the value comes not from the number of peers (like with the telephone) or the presence of many buyers and sellers (as on platforms like Etsy), but from feedback that helps it make better predictions. More users mean more responses, which further prediction accuracy, creating a virtuous cycle. Companies need to consider three lessons: 1) feedback is crucial, 2) routinize meticulous gathering of information, and 3) consider the data you share, intentionally or not.

You'd probably enjoy being able to make predictions about something important to you, right? In this post, I'll show you how to use regression information to analyze predictions and see if they're both unbiased and accurate. In these best data analytics courses online, you will have a better understanding of data analytics. Predictions can be made using regression equations. After fitting a model, regression equations are an important part of the statistical output.

We asked 100 senior tech executives -- CIOs, CTOs, and chief data officers -- what they need to bridge data silos, boost AI/ML projects, and open up new revenue streams. A massive 88% said the same thing: knowledge graphs. Given that these executives represent large organisations across verticals using graph technology for a wide array of use cases, something's clearly going on. So why is the knowledge graph --defined by Stanford University as "a compelling abstraction for organising world's structured knowledge over the internet, and a way to integrate information extracted from multiple data sources" -- becoming such a hot topic? Leaders know the value of their data, keenly aware that it holds the answers to their most pressing business questions.

How can I tell if my machine learning model is working for me? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. Businesses must eventually sustain themselves beyond external funding sources by turning profits. What's talked about less than ML itself is how one can leverage machine learned models to generate profits. I've got a whole sub-section of the book that shows how to account for the revenues and costs of building a machine learning system, using traditional accounting concepts. Essentially, data learning loops bring about profit and create investment opportunities for the AI-First vendor: better predictions can lead to more automation, which lowers operating costs, which in turn means more gross profit that can be invested in research and development (models and data), leading to better predictions, and so on.

Discussions: Hacker News (397 points, 97 comments), Reddit r/MachineLearning (247 points, 27 comments) Translations: German, Chinese (Simplified), Russian The tech world is abuzz with GPT3 hype. Massive language models (like GPT3) are starting to surprise us with their abilities. While not yet completely reliable for most businesses to put in front of their customers, these models are showing sparks of cleverness that are sure to accelerate the march of automation and the possibilities of intelligent computer systems. Let’s remove the aura of mystery around GPT3 and learn how it’s trained and how it works. A trained language model generates text. We can optionally pass it some text as input, which influences its output. The output is generated from what the model “learned” during its training period where it scanned vast amounts of text.