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1,000 | A Theory of Local Learning, the Learning Channel, and the Optimality of
Backpropagation | cs.LG | In a physical neural system, where storage and processing are intimately
intertwined, the rules for adjusting the synaptic weights can only depend on
variables that are available locally, such as the activity of the pre- and
post-synaptic neurons, resulting in local learning rules. A systematic
framework for studying t... | computer science |
1,001 | Beating the Perils of Non-Convexity: Guaranteed Training of Neural
Networks using Tensor Methods | cs.LG | Training neural networks is a challenging non-convex optimization problem,
and backpropagation or gradient descent can get stuck in spurious local optima.
We propose a novel algorithm based on tensor decomposition for guaranteed
training of two-layer neural networks. We provide risk bounds for our proposed
method, with... | computer science |
1,002 | Natural Neural Networks | stat.ML | We introduce Natural Neural Networks, a novel family of algorithms that speed
up convergence by adapting their internal representation during training to
improve conditioning of the Fisher matrix. In particular, we show a specific
example that employs a simple and efficient reparametrization of the neural
network weigh... | computer science |
1,003 | Semi-Supervised Learning with Ladder Networks | cs.NE | We combine supervised learning with unsupervised learning in deep neural
networks. The proposed model is trained to simultaneously minimize the sum of
supervised and unsupervised cost functions by backpropagation, avoiding the
need for layer-wise pre-training. Our work builds on the Ladder network
proposed by Valpola (... | computer science |
1,004 | OCReP: An Optimally Conditioned Regularization for Pseudoinversion Based
Neural Training | cs.NE | In this paper we consider the training of single hidden layer neural networks
by pseudoinversion, which, in spite of its popularity, is sometimes affected by
numerical instability issues. Regularization is known to be effective in such
cases, so that we introduce, in the framework of Tikhonov regularization, a
matricia... | computer science |
1,005 | Partitioning Large Scale Deep Belief Networks Using Dropout | stat.ML | Deep learning methods have shown great promise in many practical
applications, ranging from speech recognition, visual object recognition, to
text processing. However, most of the current deep learning methods suffer from
scalability problems for large-scale applications, forcing researchers or users
to focus on small-... | computer science |
1,006 | Hessian-free Optimization for Learning Deep Multidimensional Recurrent
Neural Networks | cs.LG | Multidimensional recurrent neural networks (MDRNNs) have shown a remarkable
performance in the area of speech and handwriting recognition. The performance
of an MDRNN is improved by further increasing its depth, and the difficulty of
learning the deeper network is overcome by using Hessian-free (HF)
optimization. Given... | computer science |
1,007 | On the Expressive Power of Deep Learning: A Tensor Analysis | cs.NE | It has long been conjectured that hypotheses spaces suitable for data that is
compositional in nature, such as text or images, may be more efficiently
represented with deep hierarchical networks than with shallow ones. Despite the
vast empirical evidence supporting this belief, theoretical justifications to
date are li... | computer science |
1,008 | A review of learning vector quantization classifiers | cs.LG | In this work we present a review of the state of the art of Learning Vector
Quantization (LVQ) classifiers. A taxonomy is proposed which integrates the
most relevant LVQ approaches to date. The main concepts associated with modern
LVQ approaches are defined. A comparison is made among eleven LVQ classifiers
using one r... | computer science |
1,009 | Provable approximation properties for deep neural networks | stat.ML | We discuss approximation of functions using deep neural nets. Given a
function $f$ on a $d$-dimensional manifold $\Gamma \subset \mathbb{R}^m$, we
construct a sparsely-connected depth-4 neural network and bound its error in
approximating $f$. The size of the network depends on dimension and curvature
of the manifold $\... | computer science |
1,010 | Semantics, Representations and Grammars for Deep Learning | cs.LG | Deep learning is currently the subject of intensive study. However,
fundamental concepts such as representations are not formally defined --
researchers "know them when they see them" -- and there is no common language
for describing and analyzing algorithms. This essay proposes an abstract
framework that identifies th... | computer science |
1,011 | Generalizing Pooling Functions in Convolutional Neural Networks: Mixed,
Gated, and Tree | stat.ML | We seek to improve deep neural networks by generalizing the pooling
operations that play a central role in current architectures. We pursue a
careful exploration of approaches to allow pooling to learn and to adapt to
complex and variable patterns. The two primary directions lie in (1) learning a
pooling function via (... | computer science |
1,012 | Batch Normalized Recurrent Neural Networks | stat.ML | Recurrent Neural Networks (RNNs) are powerful models for sequential data that
have the potential to learn long-term dependencies. However, they are
computationally expensive to train and difficult to parallelize. Recent work
has shown that normalizing intermediate representations of neural networks can
significantly im... | computer science |
1,013 | Optimizing and Contrasting Recurrent Neural Network Architectures | stat.ML | Recurrent Neural Networks (RNNs) have long been recognized for their
potential to model complex time series. However, it remains to be determined
what optimization techniques and recurrent architectures can be used to best
realize this potential. The experiments presented take a deep look into Hessian
free optimization... | computer science |
1,014 | An Exploration of Softmax Alternatives Belonging to the Spherical Loss
Family | cs.NE | In a multi-class classification problem, it is standard to model the output
of a neural network as a categorical distribution conditioned on the inputs.
The output must therefore be positive and sum to one, which is traditionally
enforced by a softmax. This probabilistic mapping allows to use the maximum
likelihood pri... | computer science |
1,015 | Understanding Adversarial Training: Increasing Local Stability of Neural
Nets through Robust Optimization | stat.ML | We propose a general framework for increasing local stability of Artificial
Neural Nets (ANNs) using Robust Optimization (RO). We achieve this through an
alternating minimization-maximization procedure, in which the loss of the
network is minimized over perturbed examples that are generated at each
parameter update. We... | computer science |
1,016 | Unitary Evolution Recurrent Neural Networks | cs.LG | Recurrent neural networks (RNNs) are notoriously difficult to train. When the
eigenvalues of the hidden to hidden weight matrix deviate from absolute value
1, optimization becomes difficult due to the well studied issue of vanishing
and exploding gradients, especially when trying to learn long-term
dependencies. To cir... | computer science |
1,017 | The Limitations of Deep Learning in Adversarial Settings | cs.CR | Deep learning takes advantage of large datasets and computationally efficient
training algorithms to outperform other approaches at various machine learning
tasks. However, imperfections in the training phase of deep neural networks
make them vulnerable to adversarial samples: inputs crafted by adversaries with
the int... | computer science |
1,018 | The Power of Depth for Feedforward Neural Networks | cs.LG | We show that there is a simple (approximately radial) function on $\reals^d$,
expressible by a small 3-layer feedforward neural networks, which cannot be
approximated by any 2-layer network, to more than a certain constant accuracy,
unless its width is exponential in the dimension. The result holds for
virtually all kn... | computer science |
1,019 | Structured Pruning of Deep Convolutional Neural Networks | cs.NE | Real time application of deep learning algorithms is often hindered by high
computational complexity and frequent memory accesses. Network pruning is a
promising technique to solve this problem. However, pruning usually results in
irregular network connections that not only demand extra representation efforts
but also ... | computer science |
1,020 | Common Variable Learning and Invariant Representation Learning using
Siamese Neural Networks | stat.ML | We consider the statistical problem of learning common source of variability
in data which are synchronously captured by multiple sensors, and demonstrate
that Siamese neural networks can be naturally applied to this problem. This
approach is useful in particular in exploratory, data-driven applications,
where neither ... | computer science |
1,021 | Stochastic Neural Networks with Monotonic Activation Functions | stat.ML | We propose a Laplace approximation that creates a stochastic unit from any
smooth monotonic activation function, using only Gaussian noise. This paper
investigates the application of this stochastic approximation in training a
family of Restricted Boltzmann Machines (RBM) that are closely linked to
Bregman divergences.... | computer science |
1,022 | Single-Solution Hypervolume Maximization and its use for Improving
Generalization of Neural Networks | cs.LG | This paper introduces the hypervolume maximization with a single solution as
an alternative to the mean loss minimization. The relationship between the two
problems is proved through bounds on the cost function when an optimal solution
to one of the problems is evaluated on the other, with a hyperparameter to
control t... | computer science |
1,023 | Poor starting points in machine learning | cs.LG | Poor (even random) starting points for learning/training/optimization are
common in machine learning. In many settings, the method of Robbins and Monro
(online stochastic gradient descent) is known to be optimal for good starting
points, but may not be optimal for poor starting points -- indeed, for poor
starting point... | computer science |
1,024 | Benefits of depth in neural networks | cs.LG | For any positive integer $k$, there exist neural networks with $\Theta(k^3)$
layers, $\Theta(1)$ nodes per layer, and $\Theta(1)$ distinct parameters which
can not be approximated by networks with $\mathcal{O}(k)$ layers unless they
are exponentially large --- they must possess $\Omega(2^k)$ nodes. This result
is prove... | computer science |
1,025 | Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample
Guarantees for Oja's Algorithm | cs.LG | This work provides improved guarantees for streaming principle component
analysis (PCA). Given $A_1, \ldots, A_n\in \mathbb{R}^{d\times d}$ sampled
independently from distributions satisfying $\mathbb{E}[A_i] = \Sigma$ for
$\Sigma \succeq \mathbf{0}$, this work provides an $O(d)$-space linear-time
single-pass streaming... | computer science |
1,026 | Practical Riemannian Neural Networks | cs.NE | We provide the first experimental results on non-synthetic datasets for the
quasi-diagonal Riemannian gradient descents for neural networks introduced in
[Ollivier, 2015]. These include the MNIST, SVHN, and FACE datasets as well as a
previously unpublished electroencephalogram dataset. The quasi-diagonal
Riemannian alg... | computer science |
1,027 | Scalable and Sustainable Deep Learning via Randomized Hashing | stat.ML | Current deep learning architectures are growing larger in order to learn from
complex datasets. These architectures require giant matrix multiplication
operations to train millions of parameters. Conversely, there is another
growing trend to bring deep learning to low-power, embedded devices. The matrix
operations, ass... | computer science |
1,028 | Noisy Activation Functions | cs.LG | Common nonlinear activation functions used in neural networks can cause
training difficulties due to the saturation behavior of the activation
function, which may hide dependencies that are not visible to vanilla-SGD
(using first order gradients only). Gating mechanisms that use softly
saturating activation functions t... | computer science |
1,029 | Training Input-Output Recurrent Neural Networks through Spectral Methods | cs.LG | We consider the problem of training input-output recurrent neural networks
(RNN) for sequence labeling tasks. We propose a novel spectral approach for
learning the network parameters. It is based on decomposition of the
cross-moment tensor between the output and a non-linear transformation of the
input, based on score ... | computer science |
1,030 | Turing learning: a metric-free approach to inferring behavior and its
application to swarms | stat.ML | We propose Turing Learning, a novel system identification method for
inferring the behavior of natural or artificial systems. Turing Learning
simultaneously optimizes two populations of computer programs, one representing
models of the behavior of the system under investigation, and the other
representing classifiers. ... | computer science |
1,031 | Stochastic Variance Reduction for Nonconvex Optimization | math.OC | We study nonconvex finite-sum problems and analyze stochastic variance
reduced gradient (SVRG) methods for them. SVRG and related methods have
recently surged into prominence for convex optimization given their edge over
stochastic gradient descent (SGD); but their theoretical analysis almost
exclusively assumes convex... | computer science |
1,032 | Variational Autoencoders for Feature Detection of Magnetic Resonance
Imaging Data | cs.LG | Independent component analysis (ICA), as an approach to the blind
source-separation (BSS) problem, has become the de-facto standard in many
medical imaging settings. Despite successes and a large ongoing research
effort, the limitation of ICA to square linear transformations have not been
overcome, so that general INFO... | computer science |
1,033 | A guide to convolution arithmetic for deep learning | stat.ML | We introduce a guide to help deep learning practitioners understand and
manipulate convolutional neural network architectures. The guide clarifies the
relationship between various properties (input shape, kernel shape, zero
padding, strides and output shape) of convolutional, pooling and transposed
convolutional layers... | computer science |
1,034 | Acceleration of Deep Neural Network Training with Resistive Cross-Point
Devices | cs.LG | In recent years, deep neural networks (DNN) have demonstrated significant
business impact in large scale analysis and classification tasks such as speech
recognition, visual object detection, pattern extraction, etc. Training of
large DNNs, however, is universally considered as time consuming and
computationally intens... | computer science |
1,035 | Biologically Inspired Radio Signal Feature Extraction with Sparse
Denoising Autoencoders | stat.ML | Automatic modulation classification (AMC) is an important task for modern
communication systems; however, it is a challenging problem when signal
features and precise models for generating each modulation may be unknown. We
present a new biologically-inspired AMC method without the need for models or
manually specified... | computer science |
1,036 | Learning activation functions from data using cubic spline interpolation | stat.ML | Neural networks require a careful design in order to perform properly on a
given task. In particular, selecting a good activation function (possibly in a
data-dependent fashion) is a crucial step, which remains an open problem in the
research community. Despite a large amount of investigations, most current
implementat... | computer science |
1,037 | On Optimality Conditions for Auto-Encoder Signal Recovery | stat.ML | Auto-Encoders are unsupervised models that aim to learn patterns from
observed data by minimizing a reconstruction cost. The useful representations
learned are often found to be sparse and distributed. On the other hand,
compressed sensing and sparse coding assume a data generating process, where
the observed data is g... | computer science |
1,038 | Recurrent Neural Networks for Multivariate Time Series with Missing
Values | cs.LG | Multivariate time series data in practical applications, such as health care,
geoscience, and biology, are characterized by a variety of missing values. In
time series prediction and other related tasks, it has been noted that missing
values and their missing patterns are often correlated with the target labels,
a.k.a.... | computer science |
1,039 | Modeling Missing Data in Clinical Time Series with RNNs | cs.LG | We demonstrate a simple strategy to cope with missing data in sequential
inputs, addressing the task of multilabel classification of diagnoses given
clinical time series. Collected from the pediatric intensive care unit (PICU)
at Children's Hospital Los Angeles, our data consists of multivariate time
series of observat... | computer science |
1,040 | Improving Power Generation Efficiency using Deep Neural Networks | stat.ML | Recently there has been significant research on power generation,
distribution and transmission efficiency especially in the case of renewable
resources. The main objective is reduction of energy losses and this requires
improvements on data acquisition and analysis. In this paper we address these
concerns by using con... | computer science |
1,041 | Faster Training of Very Deep Networks Via p-Norm Gates | stat.ML | A major contributing factor to the recent advances in deep neural networks is
structural units that let sensory information and gradients to propagate
easily. Gating is one such structure that acts as a flow control. Gates are
employed in many recent state-of-the-art recurrent models such as LSTM and GRU,
and feedforwa... | computer science |
1,042 | BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for
Task-Oriented Dialogue Systems | cs.LG | We present a new algorithm that significantly improves the efficiency of
exploration for deep Q-learning agents in dialogue systems. Our agents explore
via Thompson sampling, drawing Monte Carlo samples from a Bayes-by-Backprop
neural network. Our algorithm learns much faster than common exploration
strategies such as ... | computer science |
1,043 | Why does deep and cheap learning work so well? | cs.LG | We show how the success of deep learning could depend not only on mathematics
but also on physics: although well-known mathematical theorems guarantee that
neural networks can approximate arbitrary functions well, the class of
functions of practical interest can frequently be approximated through "cheap
learning" with ... | computer science |
1,044 | Distribution-Specific Hardness of Learning Neural Networks | cs.LG | Although neural networks are routinely and successfully trained in practice
using simple gradient-based methods, most existing theoretical results are
negative, showing that learning such networks is difficult, in a worst-case
sense over all data distributions. In this paper, we take a more nuanced view,
and consider w... | computer science |
1,045 | Sampling Generative Networks | cs.NE | We introduce several techniques for sampling and visualizing the latent
spaces of generative models. Replacing linear interpolation with spherical
linear interpolation prevents diverging from a model's prior distribution and
produces sharper samples. J-Diagrams and MINE grids are introduced as
visualizations of manifol... | computer science |
1,046 | A Cheap Linear Attention Mechanism with Fast Lookups and Fixed-Size
Representations | cs.LG | The softmax content-based attention mechanism has proven to be very
beneficial in many applications of recurrent neural networks. Nevertheless it
suffers from two major computational limitations. First, its computations for
an attention lookup scale linearly in the size of the attended sequence.
Second, it does not enc... | computer science |
1,047 | Regularized Dynamic Boltzmann Machine with Delay Pruning for
Unsupervised Learning of Temporal Sequences | cs.LG | We introduce Delay Pruning, a simple yet powerful technique to regularize
dynamic Boltzmann machines (DyBM). The recently introduced DyBM provides a
particularly structured Boltzmann machine, as a generative model of a
multi-dimensional time-series. This Boltzmann machine can have infinitely many
layers of units but al... | computer science |
1,048 | Tractable Generative Convolutional Arithmetic Circuits | cs.LG | Casting neural networks in generative frameworks is a highly sought-after
endeavor these days. Existing methods, such as Generative Adversarial Networks,
capture some of the generative capabilities, but not all. To truly leverage the
power of generative models, tractable marginalization is needed, a feature
outside the... | computer science |
1,049 | Using Fast Weights to Attend to the Recent Past | stat.ML | Until recently, research on artificial neural networks was largely restricted
to systems with only two types of variable: Neural activities that represent
the current or recent input and weights that learn to capture regularities
among inputs, outputs and payoffs. There is no good reason for this
restriction. Synapses ... | computer science |
1,050 | Hybrid clustering-classification neural network in the medical
diagnostics of reactive arthritis | cs.LG | The hybrid clustering-classification neural network is proposed. This network
allows increasing a quality of information processing under the condition of
overlapping classes due to the rational choice of a learning rate parameter and
introducing a special procedure of fuzzy reasoning in the clustering process,
which o... | computer science |
1,051 | Full-Capacity Unitary Recurrent Neural Networks | stat.ML | Recurrent neural networks are powerful models for processing sequential data,
but they are generally plagued by vanishing and exploding gradient problems.
Unitary recurrent neural networks (uRNNs), which use unitary recurrence
matrices, have recently been proposed as a means to avoid these issues.
However, in previous ... | computer science |
1,052 | Demystifying ResNet | cs.NE | The Residual Network (ResNet), proposed in He et al. (2015), utilized
shortcut connections to significantly reduce the difficulty of training, which
resulted in great performance boosts in terms of both training and
generalization error.
It was empirically observed in He et al. (2015) that stacking more layers of
res... | computer science |
1,053 | Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear | cs.LG | Many practical environments contain catastrophic states that an optimal agent
would visit infrequently or never. Even on toy problems, Deep Reinforcement
Learning (DRL) agents tend to periodically revisit these states upon forgetting
their existence under a new policy. We introduce intrinsic fear (IF), a learned
reward... | computer science |
1,054 | Adversarial Ladder Networks | cs.NE | The use of unsupervised data in addition to supervised data in training
discriminative neural networks has improved the performance of this clas-
sification scheme. However, the best results were achieved with a training
process that is divided in two parts: first an unsupervised pre-training step
is done for initializ... | computer science |
1,055 | Neural Taylor Approximations: Convergence and Exploration in Rectifier
Networks | cs.LG | Modern convolutional networks, incorporating rectifiers and max-pooling, are
neither smooth nor convex. Standard guarantees therefore do not apply.
Nevertheless, methods from convex optimization such as gradient descent and
Adam are widely used as building blocks for deep learning algorithms. This
paper provides the fi... | computer science |
1,056 | Deep Unsupervised Clustering with Gaussian Mixture Variational
Autoencoders | cs.LG | We study a variant of the variational autoencoder model (VAE) with a Gaussian
mixture as a prior distribution, with the goal of performing unsupervised
clustering through deep generative models. We observe that the known problem of
over-regularisation that has been shown to arise in regular VAEs also manifests
itself i... | computer science |
1,057 | Identity Matters in Deep Learning | cs.LG | An emerging design principle in deep learning is that each layer of a deep
artificial neural network should be able to easily express the identity
transformation. This idea not only motivated various normalization techniques,
such as \emph{batch normalization}, but was also key to the immense success of
\emph{residual ... | computer science |
1,058 | Deep Learning with Sets and Point Clouds | stat.ML | We introduce a simple permutation equivariant layer for deep learning with
set structure.This type of layer, obtained by parameter-sharing, has a simple
implementation and linear-time complexity in the size of each set. We use deep
permutation-invariant networks to perform point-could classification and
MNIST-digit sum... | computer science |
1,059 | Compacting Neural Network Classifiers via Dropout Training | stat.ML | We introduce dropout compaction, a novel method for training feed-forward
neural networks which realizes the performance gains of training a large model
with dropout regularization, yet extracts a compact neural network for run-time
efficiency. In the proposed method, we introduce a sparsity-inducing prior on
the per u... | computer science |
1,060 | Spikes as regularizers | cs.NE | We present a confidence-based single-layer feed-forward learning algorithm
SPIRAL (Spike Regularized Adaptive Learning) relying on an encoding of
activation spikes. We adaptively update a weight vector relying on confidence
estimates and activation offsets relative to previous activity. We regularize
updates proportion... | computer science |
1,061 | Local minima in training of neural networks | stat.ML | There has been a lot of recent interest in trying to characterize the error
surface of deep models. This stems from a long standing question. Given that
deep networks are highly nonlinear systems optimized by local gradient methods,
why do they not seem to be affected by bad local minima? It is widely believed
that tra... | computer science |
1,062 | Time Series Classification from Scratch with Deep Neural Networks: A
Strong Baseline | cs.LG | We propose a simple but strong baseline for time series classification from
scratch with deep neural networks. Our proposed baseline models are pure
end-to-end without any heavy preprocessing on the raw data or feature crafting.
The proposed Fully Convolutional Network (FCN) achieves premium performance to
other state-... | computer science |
1,063 | Tunable Sensitivity to Large Errors in Neural Network Training | stat.ML | When humans learn a new concept, they might ignore examples that they cannot
make sense of at first, and only later focus on such examples, when they are
more useful for learning. We propose incorporating this idea of tunable
sensitivity for hard examples in neural network learning, using a new
generalization of the cr... | computer science |
1,064 | Efficient Convolutional Auto-Encoding via Random Convexification and
Frequency-Domain Minimization | stat.ML | The omnipresence of deep learning architectures such as deep convolutional
neural networks (CNN)s is fueled by the synergistic combination of
ever-increasing labeled datasets and specialized hardware. Despite the
indisputable success, the reliance on huge amounts of labeled data and
specialized hardware can be a limiti... | computer science |
1,065 | Memory Augmented Neural Networks with Wormhole Connections | cs.LG | Recent empirical results on long-term dependency tasks have shown that neural
networks augmented with an external memory can learn the long-term dependency
tasks more easily and achieve better generalization than vanilla recurrent
neural networks (RNN). We suggest that memory augmented neural networks can
reduce the ef... | computer science |
1,066 | Toward the automated analysis of complex diseases in genome-wide
association studies using genetic programming | cs.NE | Machine learning has been gaining traction in recent years to meet the demand
for tools that can efficiently analyze and make sense of the ever-growing
databases of biomedical data in health care systems around the world. However,
effectively using machine learning methods requires considerable domain
expertise, which ... | computer science |
1,067 | Deep Learning with Dynamic Computation Graphs | cs.NE | Neural networks that compute over graph structures are a natural fit for
problems in a variety of domains, including natural language (parse trees) and
cheminformatics (molecular graphs). However, since the computation graph has a
different shape and size for every input, such networks do not directly support
batched t... | computer science |
1,068 | Deep Kernelized Autoencoders | stat.ML | In this paper we introduce the deep kernelized autoencoder, a neural network
model that allows an explicit approximation of (i) the mapping from an input
space to an arbitrary, user-specified kernel space and (ii) the back-projection
from such a kernel space to input space. The proposed method is based on
traditional a... | computer science |
1,069 | A Deterministic and Generalized Framework for Unsupervised Learning with
Restricted Boltzmann Machines | cs.LG | Restricted Boltzmann machines (RBMs) are energy-based neural-networks which
are commonly used as the building blocks for deep architectures neural
architectures. In this work, we derive a deterministic framework for the
training, evaluation, and use of RBMs based upon the Thouless-Anderson-Palmer
(TAP) mean-field appro... | computer science |
1,070 | Robust Stochastic Configuration Networks with Kernel Density Estimation | cs.NE | Neural networks have been widely used as predictive models to fit data
distribution, and they could be implemented through learning a collection of
samples. In many applications, however, the given dataset may contain noisy
samples or outliers which may result in a poor learner model in terms of
generalization. This pa... | computer science |
1,071 | Generative Temporal Models with Memory | cs.LG | We consider the general problem of modeling temporal data with long-range
dependencies, wherein new observations are fully or partially predictable based
on temporally-distant, past observations. A sufficiently powerful temporal
model should separate predictable elements of the sequence from unpredictable
elements, exp... | computer science |
1,072 | On the Origin of Deep Learning | cs.LG | This paper is a review of the evolutionary history of deep learning models.
It covers from the genesis of neural networks when associationism modeling of
the brain is studied, to the models that dominate the last decade of research
in deep learning like convolutional neural networks, deep belief networks, and
recurrent... | computer science |
1,073 | The Shattered Gradients Problem: If resnets are the answer, then what is
the question? | cs.NE | A long-standing obstacle to progress in deep learning is the problem of
vanishing and exploding gradients. The problem has largely been overcome
through the introduction of carefully constructed initializations and batch
normalization. Nevertheless, architectures incorporating skip-connections such
as resnets perform m... | computer science |
1,074 | Generalization and Equilibrium in Generative Adversarial Nets (GANs) | cs.LG | We show that training of generative adversarial network (GAN) may not have
good generalization properties; e.g., training may appear successful but the
trained distribution may be far from target distribution in standard metrics.
However, generalization does occur for a weaker metric called neural net
distance. It is a... | computer science |
1,075 | Mean teachers are better role models: Weight-averaged consistency
targets improve semi-supervised deep learning results | cs.NE | The recently proposed Temporal Ensembling has achieved state-of-the-art
results in several semi-supervised learning benchmarks. It maintains an
exponential moving average of label predictions on each training example, and
penalizes predictions that are inconsistent with this target. However, because
the targets change ... | computer science |
1,076 | On the Expressive Power of Overlapping Architectures of Deep Learning | cs.LG | Expressive efficiency refers to the relation between two architectures A and
B, whereby any function realized by B could be replicated by A, but there
exists functions realized by A, which cannot be replicated by B unless its size
grows significantly larger. For example, it is known that deep networks are
exponentially... | computer science |
1,077 | Learned Optimizers that Scale and Generalize | cs.LG | Learning to learn has emerged as an important direction for achieving
artificial intelligence. Two of the primary barriers to its adoption are an
inability to scale to larger problems and a limited ability to generalize to
new tasks. We introduce a learned gradient descent optimizer that generalizes
well to new tasks, ... | computer science |
1,078 | An Automated Auto-encoder Correlation-based Health-Monitoring and
Prognostic Method for Machine Bearings | cs.LG | This paper studies an intelligent ultimate technique for health-monitoring
and prognostic of common rotary machine components, particularly bearings.
During a run-to-failure experiment, rich unsupervised features from vibration
sensory data are extracted by a trained sparse auto-encoder. Then, the
correlation of the ex... | computer science |
1,079 | Ensemble representation learning: an analysis of fitness and survival
for wrapper-based genetic programming methods | cs.NE | Recently we proposed a general, ensemble-based feature engineering wrapper
(FEW) that was paired with a number of machine learning methods to solve
regression problems. Here, we adapt FEW for supervised classification and
perform a thorough analysis of fitness and survival methods within this
framework. Our tests demon... | computer science |
1,080 | Failures of Gradient-Based Deep Learning | cs.LG | In recent years, Deep Learning has become the go-to solution for a broad
range of applications, often outperforming state-of-the-art. However, it is
important, for both theoreticians and practitioners, to gain a deeper
understanding of the difficulties and limitations associated with common
approaches and algorithms. W... | computer science |
1,081 | A Neural Representation of Sketch Drawings | cs.NE | We present sketch-rnn, a recurrent neural network (RNN) able to construct
stroke-based drawings of common objects. The model is trained on thousands of
crude human-drawn images representing hundreds of classes. We outline a
framework for conditional and unconditional sketch generation, and describe new
robust training ... | computer science |
1,082 | Diagonal RNNs in Symbolic Music Modeling | cs.NE | In this paper, we propose a new Recurrent Neural Network (RNN) architecture.
The novelty is simple: We use diagonal recurrent matrices instead of full. This
results in better test likelihood and faster convergence compared to regular
full RNNs in most of our experiments. We show the benefits of using diagonal
recurrent... | computer science |
1,083 | DropIn: Making Reservoir Computing Neural Networks Robust to Missing
Inputs by Dropout | cs.LG | The paper presents a novel, principled approach to train recurrent neural
networks from the Reservoir Computing family that are robust to missing part of
the input features at prediction time. By building on the ensembling properties
of Dropout regularization, we propose a methodology, named DropIn, which
efficiently t... | computer science |
1,084 | Training Deep Convolutional Neural Networks with Resistive Cross-Point
Devices | cs.LG | In a previous work we have detailed the requirements to obtain a maximal
performance benefit by implementing fully connected deep neural networks (DNN)
in form of arrays of resistive devices for deep learning. This concept of
Resistive Processing Unit (RPU) devices we extend here towards convolutional
neural networks (... | computer science |
1,085 | SuperSpike: Supervised learning in multi-layer spiking neural networks | cs.LG | A vast majority of computation in the brain is performed by spiking neural
networks. Despite the ubiquity of such spiking, we currently lack an
understanding of how biological spiking neural circuits learn and compute
in-vivo, as well as how we can instantiate such capabilities in artificial
spiking circuits in-silico.... | computer science |
1,086 | Are Saddles Good Enough for Deep Learning? | stat.ML | Recent years have seen a growing interest in understanding deep neural
networks from an optimization perspective. It is understood now that converging
to low-cost local minima is sufficient for such models to become effective in
practice. However, in this work, we propose a new hypothesis based on recent
theoretical fi... | computer science |
1,087 | Gated Orthogonal Recurrent Units: On Learning to Forget | cs.LG | We present a novel recurrent neural network (RNN) based model that combines
the remembering ability of unitary RNNs with the ability of gated RNNs to
effectively forget redundant/irrelevant information in its memory. We achieve
this by extending unitary RNNs with a gating mechanism. Our model is able to
outperform LSTM... | computer science |
1,088 | Neural networks and rational functions | cs.LG | Neural networks and rational functions efficiently approximate each other. In
more detail, it is shown here that for any ReLU network, there exists a
rational function of degree $O(\text{polylog}(1/\epsilon))$ which is
$\epsilon$-close, and similarly for any rational function there exists a ReLU
network of size $O(\tex... | computer science |
1,089 | Fatiguing STDP: Learning from Spike-Timing Codes in the Presence of Rate
Codes | cs.NE | Spiking neural networks (SNNs) could play a key role in unsupervised machine
learning applications, by virtue of strengths related to learning from the fine
temporal structure of event-based signals. However, some spike-timing-related
strengths of SNNs are hindered by the sensitivity of spike-timing-dependent
plasticit... | computer science |
1,090 | Sparse Neural Networks Topologies | cs.LG | We propose Sparse Neural Network architectures that are based on random or
structured bipartite graph topologies. Sparse architectures provide compression
of the models learned and speed-ups of computations, they can also surpass
their unstructured or fully connected counterparts. As we show, even more
compact topologi... | computer science |
1,091 | Towards Deep Learning Models Resistant to Adversarial Attacks | stat.ML | Recent work has demonstrated that neural networks are vulnerable to
adversarial examples, i.e., inputs that are almost indistinguishable from
natural data and yet classified incorrectly by the network. In fact, some of
the latest findings suggest that the existence of adversarial attacks may be an
inherent weakness of ... | computer science |
1,092 | Spectrally-normalized margin bounds for neural networks | cs.LG | This paper presents a margin-based multiclass generalization bound for neural
networks that scales with their margin-normalized "spectral complexity": their
Lipschitz constant, meaning the product of the spectral norms of the weight
matrices, times a certain correction factor. This bound is empirically
investigated for... | computer science |
1,093 | Model compression as constrained optimization, with application to
neural nets. Part I: general framework | cs.LG | Compressing neural nets is an active research problem, given the large size
of state-of-the-art nets for tasks such as object recognition, and the
computational limits imposed by mobile devices. We give a general formulation
of model compression as constrained optimization. This includes many types of
compression: quan... | computer science |
1,094 | Model compression as constrained optimization, with application to
neural nets. Part II: quantization | cs.LG | We consider the problem of deep neural net compression by quantization: given
a large, reference net, we want to quantize its real-valued weights using a
codebook with $K$ entries so that the training loss of the quantized net is
minimal. The codebook can be optimally learned jointly with the net, or fixed,
as for bina... | computer science |
1,095 | Tikhonov Regularization for Long Short-Term Memory Networks | cs.LG | It is a well-known fact that adding noise to the input data often improves
network performance. While the dropout technique may be a cause of memory loss,
when it is applied to recurrent connections, Tikhonov regularization, which can
be regarded as the training with additive noise, avoids this issue naturally,
though ... | computer science |
1,096 | Neural Expectation Maximization | cs.LG | Many real world tasks such as reasoning and physical interaction require
identification and manipulation of conceptual entities. A first step towards
solving these tasks is the automated discovery of distributed symbol-like
representations. In this paper, we explicitly formalize this problem as
inference in a spatial m... | computer science |
1,097 | Classification via Tensor Decompositions of Echo State Networks | cs.LG | This work introduces a tensor-based method to perform supervised
classification on spatiotemporal data processed in an echo state network.
Typically when performing supervised classification tasks on data processed in
an echo state network, the entire collection of hidden layer node states from
the training dataset is ... | computer science |
1,098 | Deep learning with convolutional neural networks for decoding and
visualization of EEG pathology | cs.LG | We apply convolutional neural networks (ConvNets) to the task of
distinguishing pathological from normal EEG recordings in the Temple University
Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet
architectures recently shown to decode task-related information from EEG at
least as well as establish... | computer science |
1,099 | A New Learning Paradigm for Random Vector Functional-Link Network: RVFL+ | stat.ML | In school, a teacher plays an important role in various classroom teaching
patterns. Likewise to this human learning activity, the learning using
privileged information (LUPI) paradigm provides additional information
generated by the teacher to 'teach' learning algorithms during the training
stage. Therefore, this nove... | computer science |
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