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900 | Learning Structured Sparsity in Deep Neural Networks | cs.NE | High demand for computation resources severely hinders deployment of
large-scale Deep Neural Networks (DNN) in resource constrained devices. In this
work, we propose a Structured Sparsity Learning (SSL) method to regularize the
structures (i.e., filters, channels, filter shapes, and layer depth) of DNNs.
SSL can: (1) l... | computer science |
901 | Depth-Width Tradeoffs in Approximating Natural Functions with Neural
Networks | cs.LG | We provide several new depth-based separation results for feed-forward neural
networks, proving that various types of simple and natural functions can be
better approximated using deeper networks than shallower ones, even if the
shallower networks are much larger. This includes indicators of balls and
ellipses; non-lin... | computer science |
902 | Tensor Switching Networks | cs.NE | We present a novel neural network algorithm, the Tensor Switching (TS)
network, which generalizes the Rectified Linear Unit (ReLU) nonlinearity to
tensor-valued hidden units. The TS network copies its entire input vector to
different locations in an expanded representation, with the location determined
by its hidden un... | computer science |
903 | Survey of Expressivity in Deep Neural Networks | stat.ML | We survey results on neural network expressivity described in "On the
Expressive Power of Deep Neural Networks". The paper motivates and develops
three natural measures of expressiveness, which all display an exponential
dependence on the depth of the network. In fact, all of these measures are
related to a fourth quan... | computer science |
904 | Precise Recovery of Latent Vectors from Generative Adversarial Networks | cs.LG | Generative adversarial networks (GANs) transform latent vectors into visually
plausible images. It is generally thought that the original GAN formulation
gives no out-of-the-box method to reverse the mapping, projecting images back
into latent space. We introduce a simple, gradient-based technique called
stochastic cli... | computer science |
905 | Predicting Surgery Duration with Neural Heteroscedastic Regression | stat.ML | Scheduling surgeries is a challenging task due to the fundamental uncertainty
of the clinical environment, as well as the risks and costs associated with
under- and over-booking. We investigate neural regression algorithms to
estimate the parameters of surgery case durations, focusing on the issue of
heteroscedasticity... | computer science |
906 | Depth Creates No Bad Local Minima | cs.LG | In deep learning, \textit{depth}, as well as \textit{nonlinearity}, create
non-convex loss surfaces. Then, does depth alone create bad local minima? In
this paper, we prove that without nonlinearity, depth alone does not create bad
local minima, although it induces non-convex loss surface. Using this insight,
we greatl... | computer science |
907 | Deep Semi-Random Features for Nonlinear Function Approximation | cs.LG | We propose semi-random features for nonlinear function approximation. The
flexibility of semi-random feature lies between the fully adjustable units in
deep learning and the random features used in kernel methods. For one hidden
layer models with semi-random features, we prove with no unrealistic
assumptions that the m... | computer science |
908 | Curriculum Dropout | cs.NE | Dropout is a very effective way of regularizing neural networks.
Stochastically "dropping out" units with a certain probability discourages
over-specific co-adaptations of feature detectors, preventing overfitting and
improving network generalization. Besides, Dropout can be interpreted as an
approximate model aggregat... | computer science |
909 | The power of deeper networks for expressing natural functions | cs.LG | It is well-known that neural networks are universal approximators, but that
deeper networks tend to be much more efficient than shallow ones. We shed light
on this by proving that the total number of neurons $m$ required to approximate
natural classes of multivariate polynomials of $n$ variables grows only
linearly wit... | computer science |
910 | Gradient Descent for Spiking Neural Networks | cs.LG | Much of studies on neural computation are based on network models of static
neurons that produce analog output, despite the fact that information
processing in the brain is predominantly carried out by dynamic neurons that
produce discrete pulses called spikes. Research in spike-based computation has
been impeded by th... | computer science |
911 | Unsure When to Stop? Ask Your Semantic Neighbors | cs.NE | In iterative supervised learning algorithms it is common to reach a point in
the search where no further induction seems to be possible with the available
data. If the search is continued beyond this point, the risk of overfitting
increases significantly. Following the recent developments in inductive
semantic stochast... | computer science |
912 | Anomaly Detection on Graph Time Series | cs.LG | In this paper, we use variational recurrent neural network to investigate the
anomaly detection problem on graph time series. The temporal correlation is
modeled by the combination of recurrent neural network (RNN) and variational
inference (VI), while the spatial information is captured by the graph
convolutional netw... | computer science |
913 | A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and
Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data | cs.NE | Gated Recurrent Unit (GRU) is a recently-developed variation of the long
short-term memory (LSTM) unit, both of which are types of recurrent neural
network (RNN). Through empirical evidence, both models have been proven to be
effective in a wide variety of machine learning tasks such as natural language
processing (Wen... | computer science |
914 | DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in
Neural Networks | cs.NE | Deep neural networks have become widely used, obtaining remarkable results in
domains such as computer vision, speech recognition, natural language
processing, audio recognition, social network filtering, machine translation,
and bio-informatics, where they have produced results comparable to human
experts. However, th... | computer science |
915 | A Method of Generating Random Weights and Biases in Feedforward Neural
Networks with Random Hidden Nodes | cs.NE | Neural networks with random hidden nodes have gained increasing interest from
researchers and practical applications. This is due to their unique features
such as very fast training and universal approximation property. In these
networks the weights and biases of hidden nodes determining the nonlinear
feature mapping a... | computer science |
916 | Rotational Unit of Memory | cs.LG | The concepts of unitary evolution matrices and associative memory have
boosted the field of Recurrent Neural Networks (RNN) to state-of-the-art
performance in a variety of sequential tasks. However, RNN still have a limited
capacity to manipulate long-term memory. To bypass this weakness the most
successful application... | computer science |
917 | Progressive Growing of GANs for Improved Quality, Stability, and
Variation | cs.NE | We describe a new training methodology for generative adversarial networks.
The key idea is to grow both the generator and discriminator progressively:
starting from a low resolution, we add new layers that model increasingly fine
details as training progresses. This both speeds the training up and greatly
stabilizes i... | computer science |
918 | Generative Adversarial Source Separation | cs.SD | Generative source separation methods such as non-negative matrix
factorization (NMF) or auto-encoders, rely on the assumption of an output
probability density. Generative Adversarial Networks (GANs) can learn data
distributions without needing a parametric assumption on the output density. We
show on a speech source se... | computer science |
919 | A Supervised STDP-based Training Algorithm for Living Neural Networks | cs.NE | Neural networks have shown great potential in many applications like speech
recognition, drug discovery, image classification, and object detection. Neural
network models are inspired by biological neural networks, but they are
optimized to perform machine learning tasks on digital computers. The proposed
work explores... | computer science |
920 | Improving Factor-Based Quantitative Investing by Forecasting Company
Fundamentals | stat.ML | On a periodic basis, publicly traded companies are required to report
fundamentals: financial data such as revenue, operating income, debt, among
others. These data points provide some insight into the financial health of a
company. Academic research has identified some factors, i.e. computed features
of the reported d... | computer science |
921 | Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization | cs.NE | In this paper we demonstrate how genetic algorithms can be used to reverse
engineer an evaluation function's parameters for computer chess. Our results
show that using an appropriate mentor, we can evolve a program that is on par
with top tournament-playing chess programs, outperforming a two-time World
Computer Chess ... | computer science |
922 | Block Neural Network Avoids Catastrophic Forgetting When Learning
Multiple Task | cs.NE | In the present work we propose a Deep Feed Forward network architecture which
can be trained according to a sequential learning paradigm, where tasks of
increasing difficulty are learned sequentially, yet avoiding catastrophic
forgetting. The proposed architecture can re-use the features learned on
previous tasks in a ... | computer science |
923 | A Scalable Deep Neural Network Architecture for Multi-Building and
Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting | cs.NI | One of the key technologies for future large-scale location-aware services
covering a complex of multi-story buildings --- e.g., a big shopping mall and a
university campus --- is a scalable indoor localization technique. In this
paper, we report the current status of our investigation on the use of deep
neural network... | computer science |
924 | Dynamic Boltzmann Machines for Second Order Moments and Generalized
Gaussian Distributions | stat.ML | Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predict
time-series data. Gaussian DyBM is a DyBM that assumes the predicted data is
generated by a Gaussian distribution whose first-order moment (mean)
dynamically changes over time but its second-order moment (variance) is fixed.
However, in many fi... | computer science |
925 | Multi-timescale memory dynamics in a reinforcement learning network with
attention-gated memory | cs.LG | Learning and memory are intertwined in our brain and their relationship is at
the core of several recent neural network models. In particular, the
Attention-Gated MEmory Tagging model (AuGMEnT) is a reinforcement learning
network with an emphasis on biological plausibility of memory dynamics and
learning. We find that ... | computer science |
926 | Weighted Contrastive Divergence | cs.LG | Learning algorithms for energy based Boltzmann architectures that rely on
gradient descent are in general computationally prohibitive, typically due to
the exponential number of terms involved in computing the partition function.
In this way one has to resort to approximation schemes for the evaluation of
the gradient.... | computer science |
927 | Dynamic Optimization of Neural Network Structures Using Probabilistic
Modeling | cs.NE | Deep neural networks (DNNs) are powerful machine learning models and have
succeeded in various artificial intelligence tasks. Although various
architectures and modules for the DNNs have been proposed, selecting and
designing the appropriate network structure for a target problem is a
challenging task. In this paper, w... | computer science |
928 | Pruning Techniques for Mixed Ensembles of Genetic Programming Models | cs.NE | The objective of this paper is to define an effective strategy for building
an ensemble of Genetic Programming (GP) models. Ensemble methods are widely
used in machine learning due to their features: they average out biases, they
reduce the variance and they usually generalize better than single models.
Despite these a... | computer science |
929 | Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines | cs.LG | This paper introduces the Metric-Free Natural Gradient (MFNG) algorithm for
training Boltzmann Machines. Similar in spirit to the Hessian-Free method of
Martens [8], our algorithm belongs to the family of truncated Newton methods
and exploits an efficient matrix-vector product to avoid explicitely storing
the natural g... | computer science |
930 | Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | cs.LG | We introduce a simple and effective method for regularizing large
convolutional neural networks. We replace the conventional deterministic
pooling operations with a stochastic procedure, randomly picking the activation
within each pooling region according to a multinomial distribution, given by
the activities within th... | computer science |
931 | Training Neural Networks with Stochastic Hessian-Free Optimization | cs.LG | Hessian-free (HF) optimization has been successfully used for training deep
autoencoders and recurrent networks. HF uses the conjugate gradient algorithm
to construct update directions through curvature-vector products that can be
computed on the same order of time as gradients. In this paper we exploit this
property a... | computer science |
932 | Reversible Jump MCMC Simulated Annealing for Neural Networks | cs.LG | We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated
annealing algorithm to optimize radial basis function (RBF) networks. This
algorithm enables us to maximize the joint posterior distribution of the
network parameters and the number of basis functions. It performs a global
search in the joint ... | computer science |
933 | Predicting Parameters in Deep Learning | cs.LG | We demonstrate that there is significant redundancy in the parameterization
of several deep learning models. Given only a few weight values for each
feature it is possible to accurately predict the remaining values. Moreover, we
show that not only can the parameter values be predicted, but many of them need
not be lear... | computer science |
934 | Disentangling Factors of Variation via Generative Entangling | stat.ML | Here we propose a novel model family with the objective of learning to
disentangle the factors of variation in data. Our approach is based on the
spike-and-slab restricted Boltzmann machine which we generalize to include
higher-order interactions among multiple latent variables. Seen from a
generative perspective, the ... | computer science |
935 | Neural Networks for Complex Data | cs.NE | Artificial neural networks are simple and efficient machine learning tools.
Defined originally in the traditional setting of simple vector data, neural
network models have evolved to address more and more difficulties of complex
real world problems, ranging from time evolving data to sophisticated data
structures such ... | computer science |
936 | Multi-task Neural Networks for QSAR Predictions | stat.ML | Although artificial neural networks have occasionally been used for
Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) studies in
the past, the literature has of late been dominated by other machine learning
techniques such as random forests. However, a variety of new neural net
techniques along with suc... | computer science |
937 | A Hybrid Latent Variable Neural Network Model for Item Recommendation | cs.LG | Collaborative filtering is used to recommend items to a user without
requiring a knowledge of the item itself and tends to outperform other
techniques. However, collaborative filtering suffers from the cold-start
problem, which occurs when an item has not yet been rated or a user has not
rated any items. Incorporating ... | computer science |
938 | Techniques for Learning Binary Stochastic Feedforward Neural Networks | stat.ML | Stochastic binary hidden units in a multi-layer perceptron (MLP) network give
at least three potential benefits when compared to deterministic MLP networks.
(1) They allow to learn one-to-many type of mappings. (2) They can be used in
structured prediction problems, where modeling the internal structure of the
output i... | computer science |
939 | Learning ELM network weights using linear discriminant analysis | cs.NE | We present an alternative to the pseudo-inverse method for determining the
hidden to output weight values for Extreme Learning Machines performing
classification tasks. The method is based on linear discriminant analysis and
provides Bayes optimal single point estimates for the weight values. | computer science |
940 | Exponentially Increasing the Capacity-to-Computation Ratio for
Conditional Computation in Deep Learning | stat.ML | Many state-of-the-art results obtained with deep networks are achieved with
the largest models that could be trained, and if more computation power was
available, we might be able to exploit much larger datasets in order to improve
generalization ability. Whereas in learning algorithms such as decision trees
the ratio ... | computer science |
941 | Soft-Deep Boltzmann Machines | cs.NE | We present a layered Boltzmann machine (BM) that can better exploit the
advantages of a distributed representation. It is widely believed that deep BMs
(DBMs) have far greater representational power than its shallow counterpart,
restricted Boltzmann machines (RBMs). However, this expectation on the
supremacy of DBMs ov... | computer science |
942 | Domain-Adversarial Training of Neural Networks | stat.ML | We introduce a new representation learning approach for domain adaptation, in
which data at training and test time come from similar but different
distributions. Our approach is directly inspired by the theory on domain
adaptation suggesting that, for effective domain transfer to be achieved,
predictions must be made b... | computer science |
943 | Deep Online Convex Optimization with Gated Games | cs.LG | Methods from convex optimization are widely used as building blocks for deep
learning algorithms. However, the reasons for their empirical success are
unclear, since modern convolutional networks (convnets), incorporating
rectifier units and max-pooling, are neither smooth nor convex. Standard
guarantees therefore do n... | computer science |
944 | Churn analysis using deep convolutional neural networks and autoencoders | stat.ML | Customer temporal behavioral data was represented as images in order to
perform churn prediction by leveraging deep learning architectures prominent in
image classification. Supervised learning was performed on labeled data of over
6 million customers using deep convolutional neural networks, which achieved an
AUC of 0... | computer science |
945 | Developing an ICU scoring system with interaction terms using a genetic
algorithm | cs.NE | ICU mortality scoring systems attempt to predict patient mortality using
predictive models with various clinical predictors. Examples of such systems
are APACHE, SAPS and MPM. However, most such scoring systems do not actively
look for and include interaction terms, despite physicians intuitively taking
such interactio... | computer science |
946 | Scale Normalization | cs.NE | One of the difficulties of training deep neural networks is caused by
improper scaling between layers. Scaling issues introduce exploding / gradient
problems, and have typically been addressed by careful scale-preserving
initialization. We investigate the value of preserving scale, or isometry,
beyond the initial weigh... | computer science |
947 | Layer-wise learning of deep generative models | cs.NE | When using deep, multi-layered architectures to build generative models of
data, it is difficult to train all layers at once. We propose a layer-wise
training procedure admitting a performance guarantee compared to the global
optimum. It is based on an optimistic proxy of future performance, the best
latent marginal. W... | computer science |
948 | Distributed optimization of deeply nested systems | cs.LG | In science and engineering, intelligent processing of complex signals such as
images, sound or language is often performed by a parameterized hierarchy of
nonlinear processing layers, sometimes biologically inspired. Hierarchical
systems (or, more generally, nested systems) offer a way to generate complex
mappings usin... | computer science |
949 | Understanding Boltzmann Machine and Deep Learning via A Confident
Information First Principle | cs.NE | Typical dimensionality reduction methods focus on directly reducing the
number of random variables while retaining maximal variations in the data. In
this paper, we consider the dimensionality reduction in parameter spaces of
binary multivariate distributions. We propose a general
Confident-Information-First (CIF) prin... | computer science |
950 | Canonical dual solutions to nonconvex radial basis neural network
optimization problem | cs.NE | Radial Basis Functions Neural Networks (RBFNNs) are tools widely used in
regression problems. One of their principal drawbacks is that the formulation
corresponding to the training with the supervision of both the centers and the
weights is a highly non-convex optimization problem, which leads to some
fundamentally dif... | computer science |
951 | On the Number of Linear Regions of Deep Neural Networks | stat.ML | We study the complexity of functions computable by deep feedforward neural
networks with piecewise linear activations in terms of the symmetries and the
number of linear regions that they have. Deep networks are able to sequentially
map portions of each layer's input-space to the same output. In this way, deep
models c... | computer science |
952 | Geometry and Expressive Power of Conditional Restricted Boltzmann
Machines | cs.NE | Conditional restricted Boltzmann machines are undirected stochastic neural
networks with a layer of input and output units connected bipartitely to a
layer of hidden units. These networks define models of conditional probability
distributions on the states of the output units given the states of the input
units, parame... | computer science |
953 | Is Joint Training Better for Deep Auto-Encoders? | stat.ML | Traditionally, when generative models of data are developed via deep
architectures, greedy layer-wise pre-training is employed. In a well-trained
model, the lower layer of the architecture models the data distribution
conditional upon the hidden variables, while the higher layers model the hidden
distribution prior. Bu... | computer science |
954 | Massively Multitask Networks for Drug Discovery | stat.ML | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | computer science |
955 | Gated Feedback Recurrent Neural Networks | cs.NE | In this work, we propose a novel recurrent neural network (RNN) architecture.
The proposed RNN, gated-feedback RNN (GF-RNN), extends the existing approach of
stacking multiple recurrent layers by allowing and controlling signals flowing
from upper recurrent layers to lower layers using a global gating unit for each
pai... | computer science |
956 | Deep Learning with Limited Numerical Precision | cs.LG | Training of large-scale deep neural networks is often constrained by the
available computational resources. We study the effect of limited precision
data representation and computation on neural network training. Within the
context of low-precision fixed-point computations, we observe the rounding
scheme to play a cruc... | computer science |
957 | MADE: Masked Autoencoder for Distribution Estimation | cs.LG | There has been a lot of recent interest in designing neural network models to
estimate a distribution from a set of examples. We introduce a simple
modification for autoencoder neural networks that yields powerful generative
models. Our method masks the autoencoder's parameters to respect autoregressive
constraints: ea... | computer science |
958 | Simple, Efficient, and Neural Algorithms for Sparse Coding | cs.LG | Sparse coding is a basic task in many fields including signal processing,
neuroscience and machine learning where the goal is to learn a basis that
enables a sparse representation of a given set of data, if one exists. Its
standard formulation is as a non-convex optimization problem which is solved in
practice by heuri... | computer science |
959 | Toxicity Prediction using Deep Learning | stat.ML | Everyday we are exposed to various chemicals via food additives, cleaning and
cosmetic products and medicines -- and some of them might be toxic. However
testing the toxicity of all existing compounds by biological experiments is
neither financially nor logistically feasible. Therefore the government
agencies NIH, EPA ... | computer science |
960 | To Drop or Not to Drop: Robustness, Consistency and Differential Privacy
Properties of Dropout | cs.LG | Training deep belief networks (DBNs) requires optimizing a non-convex
function with an extremely large number of parameters. Naturally, existing
gradient descent (GD) based methods are prone to arbitrarily poor local minima.
In this paper, we rigorously show that such local minima can be avoided (upto
an approximation ... | computer science |
961 | Distilling the Knowledge in a Neural Network | stat.ML | A very simple way to improve the performance of almost any machine learning
algorithm is to train many different models on the same data and then to
average their predictions. Unfortunately, making predictions using a whole
ensemble of models is cumbersome and may be too computationally expensive to
allow deployment to... | computer science |
962 | A mathematical motivation for complex-valued convolutional networks | cs.LG | A complex-valued convolutional network (convnet) implements the repeated
application of the following composition of three operations, recursively
applying the composition to an input vector of nonnegative real numbers: (1)
convolution with complex-valued vectors followed by (2) taking the absolute
value of every entry... | computer science |
963 | Optimizing Neural Networks with Kronecker-factored Approximate Curvature | cs.LG | We propose an efficient method for approximating natural gradient descent in
neural networks which we call Kronecker-Factored Approximate Curvature (K-FAC).
K-FAC is based on an efficiently invertible approximation of a neural network's
Fisher information matrix which is neither diagonal nor low-rank, and in some
cases... | computer science |
964 | Unsupervised model compression for multilayer bootstrap networks | cs.LG | Recently, multilayer bootstrap network (MBN) has demonstrated promising
performance in unsupervised dimensionality reduction. It can learn compact
representations in standard data sets, i.e. MNIST and RCV1. However, as a
bootstrap method, the prediction complexity of MBN is high. In this paper, we
propose an unsupervis... | computer science |
965 | Positive blood culture detection in time series data using a BiLSTM
network | cs.LG | The presence of bacteria or fungi in the bloodstream of patients is abnormal
and can lead to life-threatening conditions. A computational model based on a
bidirectional long short-term memory artificial neural network, is explored to
assist doctors in the intensive care unit to predict whether examination of
blood cult... | computer science |
966 | Known Unknowns: Uncertainty Quality in Bayesian Neural Networks | stat.ML | We evaluate the uncertainty quality in neural networks using anomaly
detection. We extract uncertainty measures (e.g. entropy) from the predictions
of candidate models, use those measures as features for an anomaly detector,
and gauge how well the detector differentiates known from unknown classes. We
assign higher unc... | computer science |
967 | Semi-Supervised Learning with the Deep Rendering Mixture Model | stat.ML | Semi-supervised learning algorithms reduce the high cost of acquiring labeled
training data by using both labeled and unlabeled data during learning. Deep
Convolutional Networks (DCNs) have achieved great success in supervised tasks
and as such have been widely employed in the semi-supervised learning. In this
paper we... | computer science |
968 | Self-calibrating Neural Networks for Dimensionality Reduction | cs.LG | Recently, a novel family of biologically plausible online algorithms for
reducing the dimensionality of streaming data has been derived from the
similarity matching principle. In these algorithms, the number of output
dimensions can be determined adaptively by thresholding the singular values of
the input data matrix. ... | computer science |
969 | Tunable Efficient Unitary Neural Networks (EUNN) and their application
to RNNs | cs.LG | Using unitary (instead of general) matrices in artificial neural networks
(ANNs) is a promising way to solve the gradient explosion/vanishing problem, as
well as to enable ANNs to learn long-term correlations in the data. This
approach appears particularly promising for Recurrent Neural Networks (RNNs).
In this work, w... | computer science |
970 | Sequence Transduction with Recurrent Neural Networks | cs.NE | Many machine learning tasks can be expressed as the transformation---or
\emph{transduction}---of input sequences into output sequences: speech
recognition, machine translation, protein secondary structure prediction and
text-to-speech to name but a few. One of the key challenges in sequence
transduction is learning to ... | computer science |
971 | On Fast Dropout and its Applicability to Recurrent Networks | stat.ML | Recurrent Neural Networks (RNNs) are rich models for the processing of
sequential data. Recent work on advancing the state of the art has been focused
on the optimization or modelling of RNNs, mostly motivated by adressing the
problems of the vanishing and exploding gradients. The control of overfitting
has seen consid... | computer science |
972 | Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks | cs.NE | In this paper we propose and investigate a novel nonlinear unit, called $L_p$
unit, for deep neural networks. The proposed $L_p$ unit receives signals from
several projections of a subset of units in the layer below and computes a
normalized $L_p$ norm. We notice two interesting interpretations of the $L_p$
unit. First... | computer science |
973 | Missing Value Imputation With Unsupervised Backpropagation | cs.NE | Many data mining and data analysis techniques operate on dense matrices or
complete tables of data. Real-world data sets, however, often contain unknown
values. Even many classification algorithms that are designed to operate with
missing values still exhibit deteriorated accuracy. One approach to handling
missing valu... | computer science |
974 | Stochastic Gradient Estimate Variance in Contrastive Divergence and
Persistent Contrastive Divergence | cs.NE | Contrastive Divergence (CD) and Persistent Contrastive Divergence (PCD) are
popular methods for training the weights of Restricted Boltzmann Machines.
However, both methods use an approximate method for sampling from the model
distribution. As a side effect, these approximations yield significantly
different biases and... | computer science |
975 | How to Construct Deep Recurrent Neural Networks | cs.NE | In this paper, we explore different ways to extend a recurrent neural network
(RNN) to a \textit{deep} RNN. We start by arguing that the concept of depth in
an RNN is not as clear as it is in feedforward neural networks. By carefully
analyzing and understanding the architecture of an RNN, however, we find three
points ... | computer science |
976 | Neuronal Synchrony in Complex-Valued Deep Networks | stat.ML | Deep learning has recently led to great successes in tasks such as image
recognition (e.g Krizhevsky et al., 2012). However, deep networks are still
outmatched by the power and versatility of the brain, perhaps in part due to
the richer neuronal computations available to cortical circuits. The challenge
is to identify ... | computer science |
977 | An empirical analysis of dropout in piecewise linear networks | stat.ML | The recently introduced dropout training criterion for neural networks has
been the subject of much attention due to its simplicity and remarkable
effectiveness as a regularizer, as well as its interpretation as a training
procedure for an exponentially large ensemble of networks that share
parameters. In this work we ... | computer science |
978 | An Empirical Investigation of Catastrophic Forgetting in Gradient-Based
Neural Networks | stat.ML | Catastrophic forgetting is a problem faced by many machine learning models
and algorithms. When trained on one task, then trained on a second task, many
machine learning models "forget" how to perform the first task. This is widely
believed to be a serious problem for neural networks. Here, we investigate the
extent to... | computer science |
979 | Unsupervised Domain Adaptation by Backpropagation | stat.ML | Top-performing deep architectures are trained on massive amounts of labeled
data. In the absence of labeled data for a certain task, domain adaptation
often provides an attractive option given that labeled data of similar nature
but from a different domain (e.g. synthetic images) are available. Here, we
propose a new a... | computer science |
980 | Deep Directed Generative Autoencoders | stat.ML | For discrete data, the likelihood $P(x)$ can be rewritten exactly and
parametrized into $P(X = x) = P(X = x | H = f(x)) P(H = f(x))$ if $P(X | H)$
has enough capacity to put no probability mass on any $x'$ for which $f(x')\neq
f(x)$, where $f(\cdot)$ is a deterministic discrete function. The log of the
first factor giv... | computer science |
981 | An exact mapping between the Variational Renormalization Group and Deep
Learning | stat.ML | Deep learning is a broad set of techniques that uses multiple layers of
representation to automatically learn relevant features directly from
structured data. Recently, such techniques have yielded record-breaking results
on a diverse set of difficult machine learning tasks in computer vision, speech
recognition, and n... | computer science |
982 | Non-parametric Bayesian Learning with Deep Learning Structure and Its
Applications in Wireless Networks | cs.LG | In this paper, we present an infinite hierarchical non-parametric Bayesian
model to extract the hidden factors over observed data, where the number of
hidden factors for each layer is unknown and can be potentially infinite.
Moreover, the number of layers can also be infinite. We construct the model
structure that allo... | computer science |
983 | Parallel training of DNNs with Natural Gradient and Parameter Averaging | cs.NE | We describe the neural-network training framework used in the Kaldi speech
recognition toolkit, which is geared towards training DNNs with large amounts
of training data using multiple GPU-equipped or multi-core machines. In order
to be as hardware-agnostic as possible, we needed a way to use multiple
machines without ... | computer science |
984 | End-to-end Continuous Speech Recognition using Attention-based Recurrent
NN: First Results | cs.NE | We replace the Hidden Markov Model (HMM) which is traditionally used in in
continuous speech recognition with a bi-directional recurrent neural network
encoder coupled to a recurrent neural network decoder that directly emits a
stream of phonemes. The alignment between the input and output sequences is
established usin... | computer science |
985 | Provable Methods for Training Neural Networks with Sparse Connectivity | cs.LG | We provide novel guaranteed approaches for training feedforward neural
networks with sparse connectivity. We leverage on the techniques developed
previously for learning linear networks and show that they can also be
effectively adopted to learn non-linear networks. We operate on the moments
involving label and the sco... | computer science |
986 | Domain-Adversarial Neural Networks | stat.ML | We introduce a new representation learning algorithm suited to the context of
domain adaptation, in which data at training and test time come from similar
but different distributions. Our algorithm is directly inspired by theory on
domain adaptation suggesting that, for effective domain transfer to be
achieved, predict... | computer science |
987 | Learning with Pseudo-Ensembles | stat.ML | We formalize the notion of a pseudo-ensemble, a (possibly infinite)
collection of child models spawned from a parent model by perturbing it
according to some noise process. E.g., dropout (Hinton et. al, 2012) in a deep
neural network trains a pseudo-ensemble of child subnetworks generated by
randomly masking nodes in t... | computer science |
988 | Random Walk Initialization for Training Very Deep Feedforward Networks | cs.NE | Training very deep networks is an important open problem in machine learning.
One of many difficulties is that the norm of the back-propagated error gradient
can grow or decay exponentially. Here we show that training very deep
feed-forward networks (FFNs) is not as difficult as previously thought. Unlike
when back-pro... | computer science |
989 | Variational Recurrent Auto-Encoders | stat.ML | In this paper we propose a model that combines the strengths of RNNs and
SGVB: the Variational Recurrent Auto-Encoder (VRAE). Such a model can be used
for efficient, large scale unsupervised learning on time series data, mapping
the time series data to a latent vector representation. The model is
generative, such that ... | computer science |
990 | Neural Network Regularization via Robust Weight Factorization | cs.LG | Regularization is essential when training large neural networks. As deep
neural networks can be mathematically interpreted as universal function
approximators, they are effective at memorizing sampling noise in the training
data. This results in poor generalization to unseen data. Therefore, it is no
surprise that a ne... | computer science |
991 | A Bayesian encourages dropout | cs.LG | Dropout is one of the key techniques to prevent the learning from
overfitting. It is explained that dropout works as a kind of modified L2
regularization. Here, we shed light on the dropout from Bayesian standpoint.
Bayesian interpretation enables us to optimize the dropout rate, which is
beneficial for learning of wei... | computer science |
992 | Deep Fried Convnets | cs.LG | The fully connected layers of a deep convolutional neural network typically
contain over 90% of the network parameters, and consume the majority of the
memory required to store the network parameters. Reducing the number of
parameters while preserving essentially the same predictive performance is
critically important ... | computer science |
993 | Lateral Connections in Denoising Autoencoders Support Supervised
Learning | cs.LG | We show how a deep denoising autoencoder with lateral connections can be used
as an auxiliary unsupervised learning task to support supervised learning. The
proposed model is trained to minimize simultaneously the sum of supervised and
unsupervised cost functions by back-propagation, avoiding the need for
layer-wise pr... | computer science |
994 | Deep Neural Networks with Random Gaussian Weights: A Universal
Classification Strategy? | cs.NE | Three important properties of a classification machinery are: (i) the system
preserves the core information of the input data; (ii) the training examples
convey information about unseen data; and (iii) the system is able to treat
differently points from different classes. In this work we show that these
fundamental pro... | computer science |
995 | Imaging Time-Series to Improve Classification and Imputation | cs.LG | Inspired by recent successes of deep learning in computer vision, we propose
a novel framework for encoding time series as different types of images,
namely, Gramian Angular Summation/Difference Fields (GASF/GADF) and Markov
Transition Fields (MTF). This enables the use of techniques from computer
vision for time serie... | computer science |
996 | Blocks and Fuel: Frameworks for deep learning | cs.LG | We introduce two Python frameworks to train neural networks on large
datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler
with CUDA-support. It facilitates the training of complex neural network models
by providing parametrized Theano operations, attaching metadata to Theano's
symbolic comput... | computer science |
997 | Adaptive Normalized Risk-Averting Training For Deep Neural Networks | cs.LG | This paper proposes a set of new error criteria and learning approaches,
Adaptive Normalized Risk-Averting Training (ANRAT), to attack the non-convex
optimization problem in training deep neural networks (DNNs). Theoretically, we
demonstrate its effectiveness on global and local convexity lower-bounded by
the standard ... | computer science |
998 | Training Restricted Boltzmann Machines via the Thouless-Anderson-Palmer
Free Energy | cs.LG | Restricted Boltzmann machines are undirected neural networks which have been
shown to be effective in many applications, including serving as
initializations for training deep multi-layer neural networks. One of the main
reasons for their success is the existence of efficient and practical
stochastic algorithms, such a... | computer science |
999 | Pointer Networks | stat.ML | We introduce a new neural architecture to learn the conditional probability
of an output sequence with elements that are discrete tokens corresponding to
positions in an input sequence. Such problems cannot be trivially addressed by
existent approaches such as sequence-to-sequence and Neural Turing Machines,
because th... | computer science |
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