Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.

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Representation Learning: A Review and New Perspectives

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

http://www.cognitiveneuroscience.it/wp-content/uploads/2015/10/Rossini_CliNeuro_2015.pdf

Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: Basic principles and procedures for routine clinical and research application: An updated report from an I.F.C.N. Committee

The success of hand-crafted machine learning systems in many applications raises the question of making machine learning algorithms more autonomous, i.e., to reduce the requirement of expert input to a minimum. We discuss two strategies towards this goal: (1) automated optimization of hyperparameters (including mechanisms for feature selection, preprocessing, model selection, etc) and (2) the development of algorithms with reduced sets of hyperparameters. Since many research directions (e.g., deep learning), show a tendency towards increasingly complex algorithms with more and more hyperparamters, the demand for both of these strategies continuously increases. We review recent hyperparameter optimization methods and discuss data-driven approaches to avoid the introduction of hyperparameters using unsupervised learning. We end in discussing how these complementary strategies can work hand-in-hand, representing a very promising approach towards autonomous machine learning.

Beyond Manual Tuning of Hyperparameters

Hysteresis effects on the input–output curve of motor evoked potentials

We study a generative model in which hidden causes combine competitively to produce observations. Multiple active causes combine to determine the value of an observed variable through a max function, in the place where algorithms such as sparse coding, independent component analysis, or non-negative matrix factorization would use a sum. This max rule can represent a more realistic model of non-linear interaction between basic components in many settings, including acoustic and image data. While exact maximum-likelihood learning of the parameters of this model proves to be intractable, we show that efficient approximations to expectation-maximization (EM) can be found in the case of sparsely active hidden causes. One of these approximations can be formulated as a neural network model with a generalized softmax activation function and Hebbian learning. Thus, we show that learning in recent softmax-like neural networks may be interpreted as approximate maximization of a data likelihood. We use the bars benchmark test to numerically verify our analytical results and to demonstrate the competitiveness of the resulting algorithms. Finally, we show results of learning model parameters to fit acoustic and visual data sets in which max-like component combinations arise naturally.

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Maximal Causes for Non-linear Component Extraction

We study a model of the cortical macrocolumn consisting of a collection of inhibitorily coupled minicolumns. The proposed system overcomes several severe deficits of systems based on single neurons as cerebral functional units, notably limited robustness to damage and unrealistically large computation time. Motivated by neuroanatomical and neurophysiological findings, the utilized dynamics is based on a simple model of a spiking neuron with refractory period, fixed random excitatory interconnection within minicolumns, and instantaneous inhibition within one macrocolumn. A stability analysis of the system's dynamical equations shows that minicolumns can act as monolithic functional units for purposes of critical fast decisions and learning. Oscillating inhibition (in the gamma frequency range) leads to a phase-coupled population rate code and high sensitivity to small imbalances in minicolumn inputs. Minicolumns are shown to be able to organize their collective inputs without supervision by Hebbian plasticity into selective receptive field shapes, thereby becoming classifiers for input patterns. Using the bars test, we critically compare our system's performance with that of others and demonstrate its ability for distributed neural coding.

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Rapid processing and unsupervised learning in a model of the cortical macrocolumn

We show how a preselection of hidden variables can be used to efficiently train generative models with binary hidden variables. The approach is based on Expectation Maximization (EM) and uses an efficiently computable approximation to the sufficient statistics of a given model. The computational cost to compute the sufficient statistics is strongly reduced by selecting, for each data point, the relevant hidden causes. The approximation is applicable to a wide range of generative models and provides an interpretation of the benefits of preselection in terms of a variational EM approximation. To empirically show that the method maximizes the data likelihood, it is applied to different types of generative models including: a version of non-negative matrix factorization (NMF), a model for non-linear component extraction (MCA), and a linear generative model similar to sparse coding. The derived algorithms are applied to both artificial and realistic data, and are compared to other models in the literature. We find that the training scheme can reduce computational costs by orders of magnitude and allows for a reliable extraction of hidden causes.

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Jörg Lücke

Papers

Beyond Manual Tuning of Hyperparameters

Hysteresis effects on the input–output curve of motor evoked potentials

Maximal Causes for Non-linear Component Extraction

Rapid processing and unsupervised learning in a model of the cortical macrocolumn

Expectation Truncation and the Benefits of Preselection In Training Generative Models