AI News, Bayesian Reasoning and Deep Learning 4

Bayesian Reasoning and Deep Learning 4

Deep learning provides a powerful class of models and an easy framework for learning that now provides state-of-the-art methods for applications ranging from image classification to speech recognition.

Bayesian reasoning provides a powerful approach for information integration, inference and decision making that has established it as the key tool for data-efficient learning, uncertainty quantification and robust model composition that is widely used in applications ranging from information retrieval to large-scale ranking.

Machine learning

progressively improve performance on a specific task) with data, without being explicitly programmed.[1] The name Machine learning was coined in 1959 by Arthur Samuel.[2] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[3] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[4] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,[5]:2 through building a model from sample inputs.

Machine learning is sometimes conflated with data mining,[8] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[5]:vii[9] Machine learning can also be unsupervised[10] and be used to learn and establish baseline behavioral profiles for various entities[11] and then used to find meaningful anomalies.

These analytical models allow researchers, data scientists, engineers, and analysts to 'produce reliable, repeatable decisions and results' and uncover 'hidden insights' through learning from historical relationships and trends in the data.[12] Effective machine learning is difficult because finding patterns is hard and often not enough training data are available;

Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: 'A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.'[15] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms.

Machine learning tasks are typically classified into two broad categories, depending on whether there is a learning 'signal' or 'feedback' available to a learning system: Another categorization of machine learning tasks arises when one considers the desired output of a machine-learned system:[5]:3 Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience.

Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.

Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.[19]:488 By 1980, expert systems had come to dominate AI, and statistics was out of favor.[20] Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.[19]:708–710;

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge.

Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.[22] He also suggested the term data science as a placeholder to call the overall field.[22] Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model,[23] wherein 'algorithmic model' means more or less the machine learning algorithms like Random forest.

Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into (high-dimensional) vectors.[29] Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features.

In machine learning, genetic algorithms found some uses in the 1980s and 1990s.[33][34] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[35] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves `rules’ to store, manipulate or apply, knowledge.

They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[37] Applications for machine learning include: In 2006, the online movie company Netflix held the first 'Netflix Prize' competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%.

A joint team made up of researchers from ATT Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[43] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ('everything is a recommendation') and they changed their recommendation engine accordingly.[44] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of Machine Learning to predict the financial crisis.

[45] In 2012, co-founder of Sun Microsystems Vinod Khosla predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[46] In 2014, it has been reported that a machine learning algorithm has been applied in Art History to study fine art paintings, and that it may have revealed previously unrecognized influences between artists.[47] Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set.

Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[49] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[50][51] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.

Deep Learning Is Not Good Enough, We Need Bayesian Deep Learning for Safe AI

Understanding what a model does not know is a critical part of many machine learning systems. Unfortunately,

both these algorithms were able to assign a high level of uncertainty to their erroneous predictions, then each system may have been able to make better decisions and likely avoid disaster.

main issue is that traditional machine learning approaches to understanding uncertainty, such as Gaussian processes, do not scale to high dimensional inputs like images and videos.

In this post I’m going to introduce a resurging field known as Bayesian deep learning (BDL), which provides a deep learning framework which can also model uncertainty. BDL

example, aleatoric uncertainty in images can be attributed to occlusions (because cameras can’t see through objects) or lack of visual features or over-exposed regions of an image, etc. It

uncertainty is very important to model for: We can actually divide aleatoric into two further sub-categories: Next, I’m going to show how to form models to capture this uncertainty using Bayesian deep learning.

deep architectures can model complex tasks by leveraging the hierarchical representation power of deep learning, while also being able to infer complex multi-modal posterior distributions. Bayesian

deep learning models typically form uncertainty estimates by either placing distributions over model weights, or by learning a direct mapping to probabilistic outputs. In

this section I’m going to briefly discuss how we can model both epistemic and aleatoric uncertainty using Bayesian deep learning models.

To learn a Heteroscedastic uncertainty model, we simply can replace the loss function with the following: where the model predicts a mean \hat{y} and variance \sigma^2. As

Homoscedastic aleatoric uncertainty can be modelled in a similar way, however the uncertainty parameter will no longer be a model output, but a free parameter we optimise.

a quick summary of some results of a monocular depth regression model on two datasets: These results show that when we train on less data, or test on data which is significantly different from the training set, then our epistemic uncertainty increases drastically. However,

forms an interesting multi-task learning problem because scene understanding involves joint learning of various regression and classification tasks with different units and scales. Perhaps

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