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7,800 | Accuracy-Reliability Cost Function for Empirical Variance Estimation | stat.ML | In this paper we focus on the problem of assigning uncertainties to
single-point predictions. We introduce a cost function that encodes the
trade-off between accuracy and reliability in probabilistic forecast. We derive
analytic formula for the case of forecasts of continuous scalar variables
expressed in terms of Gaus... | computer science |
7,801 | Probabilistic and Regularized Graph Convolutional Networks | cs.LG | This paper explores the recently proposed Graph Convolutional Network
architecture proposed in (Kipf & Welling, 2016) The key points of their work is
summarized and their results are reproduced. Graph regularization and
alternative graph convolution approaches are explored. I find that explicit
graph regularization was... | computer science |
7,802 | COPA: Constrained PARAFAC2 for Sparse & Large Datasets | cs.LG | PARAFAC2 has demonstrated success in modeling irregular tensors, where the
tensor dimensions vary across one of the modes. An example scenario is jointly
modeling treatments across a set of patients with varying number of medical
encounters, where the alignment of events in time bears no clinical meaning,
and it may al... | computer science |
7,803 | Binary Matrix Completion Using Unobserved Entries | stat.ML | A matrix completion problem, which aims to recover a complete matrix from its
partial observations, is one of the important problems in the machine learning
field and has been studied actively. However, there is a discrepancy between
the mainstream problem setting, which assumes continuous-valued observations,
and some... | computer science |
7,804 | Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence | stat.ML | We consider the problem of near-optimal arm identification in the fixed
confidence setting of the infinitely armed bandit problem when nothing is known
about the arm reservoir distribution. We (1) introduce a PAC-like framework
within which to derive and cast results; (2) derive a sample complexity lower
bound for near... | computer science |
7,805 | Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust
Deep Learning | cs.LG | Deep neural networks (DNNs) enable innovative applications of machine
learning like image recognition, machine translation, or malware detection.
However, deep learning is often criticized for its lack of robustness in
adversarial settings (e.g., vulnerability to adversarial inputs) and general
inability to rationalize... | computer science |
7,806 | Active Reinforcement Learning with Monte-Carlo Tree Search | cs.LG | Active Reinforcement Learning (ARL) is a twist on RL where the agent observes
reward information only if it pays a cost. This subtle change makes exploration
substantially more challenging. Powerful principles in RL like optimism,
Thompson sampling, and random exploration do not help with ARL. We relate ARL
in tabular ... | computer science |
7,807 | Analysis of Nonautonomous Adversarial Systems | stat.ML | Generative adversarial networks are used to generate images but still their
convergence properties are not well understood. There have been a few studies
who intended to investigate the stability properties of GANs as a dynamical
system. This short writing can be seen in that direction. Among the proposed
methods for s... | computer science |
7,808 | Ranking with Adaptive Neighbors | cs.LG | Retrieving the most similar objects in a large-scale database for a given
query is a fundamental building block in many application domains, ranging from
web searches, visual, cross media, and document retrievals. State-of-the-art
approaches have mainly focused on capturing the underlying geometry of the data
manifolds... | computer science |
7,809 | Domain Adaptation on Graphs by Learning Aligned Graph Bases | stat.ML | We propose a method for domain adaptation on graphs. Given sufficiently many
observations of the label function on a source graph, we study the problem of
transferring the label information from the source graph to a target graph for
estimating the target label function. Our assumption about the relation between
the tw... | computer science |
7,810 | Predicting Oral Disintegrating Tablet Formulations by Neural Network
Techniques | stat.ML | Oral Disintegrating Tablets (ODTs) is a novel dosage form that can be
dissolved on the tongue within 3min or less especially for geriatric and
pediatric patients. Current ODT formulation studies usually rely on the
personal experience of pharmaceutical experts and trial-and-error in the
laboratory, which is inefficient... | computer science |
7,811 | On the Universal Approximation Property and Equivalence of Stochastic
Computing-based Neural Networks and Binary Neural Networks | cs.LG | Large-scale deep neural networks are both memory intensive and
computation-intensive, thereby posing stringent requirements on the computing
platforms. Hardware accelerations of deep neural networks have been extensively
investigated in both industry and academia. Specific forms of binary neural
networks (BNNs) and sto... | computer science |
7,812 | Generalised Structural CNNs (SCNNs) for time series data with arbitrary
graph-toplogies | stat.ML | Deep Learning methods, specifically convolutional neural networks (CNNs),
have seen a lot of success in the domain of image-based data, where the data
offers a clearly structured topology in the regular lattice of pixels. This
4-neighbourhood topological simplicity makes the application of convolutional
masks straightf... | computer science |
7,813 | Improving GANs Using Optimal Transport | cs.LG | We present Optimal Transport GAN (OT-GAN), a variant of generative
adversarial nets minimizing a new metric measuring the distance between the
generator distribution and the data distribution. This metric, which we call
mini-batch energy distance, combines optimal transport in primal form with an
energy distance define... | computer science |
7,814 | Large Margin Deep Networks for Classification | stat.ML | We present a formulation of deep learning that aims at producing a large
margin classifier. The notion of margin, minimum distance to a decision
boundary, has served as the foundation of several theoretically profound and
empirically successful results for both classification and regression tasks.
However, most large m... | computer science |
7,815 | Proximal SCOPE for Distributed Sparse Learning: Better Data Partition
Implies Faster Convergence Rate | stat.ML | Distributed sparse learning with a cluster of multiple machines has attracted
much attention in machine learning, especially for large-scale applications
with high-dimensional data. One popular way to implement sparse learning is to
use $L_1$ regularization. In this paper, we propose a novel method, called
proximal \mb... | computer science |
7,816 | Capturing Structure Implicitly from Time-Series having Limited Data | stat.ML | Scientific fields such as insider-threat detection and highway-safety
planning often lack sufficient amounts of time-series data to estimate
statistical models for the purpose of scientific discovery. Moreover, the
available limited data are quite noisy. This presents a major challenge when
estimating time-series model... | computer science |
7,817 | Deep Choice Model Using Pointer Networks for Airline Itinerary
Prediction | stat.ML | Travel providers such as airlines and on-line travel agents are becoming more
and more interested in understanding how passengers choose among alternative
itineraries when searching for flights. This knowledge helps them better
display and adapt their offer, taking into account market conditions and
customer needs. Som... | computer science |
7,818 | Constant-Time Predictive Distributions for Gaussian Processes | cs.LG | One of the most compelling features of Gaussian process (GP) regression is
its ability to provide well calibrated posterior distributions. Recent advances
in inducing point methods have drastically sped up marginal likelihood and
posterior mean computations, leaving posterior covariance estimation and
sampling as the r... | computer science |
7,819 | A Kernel Theory of Modern Data Augmentation | cs.LG | Data augmentation, a technique in which a training set is expanded with
class-preserving transformations, is ubiquitous in modern machine learning
pipelines. In this paper, we seek to establish a theoretical framework for
understanding modern data augmentation techniques. We start by showing that for
kernel classifiers... | computer science |
7,820 | Graph Partition Neural Networks for Semi-Supervised Classification | cs.LG | We present graph partition neural networks (GPNN), an extension of graph
neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate
between locally propagating information between nodes in small subgraphs and
globally propagating information between the subgraphs. To efficiently
partition graphs, we ... | computer science |
7,821 | Adversarial Logit Pairing | cs.LG | In this paper, we develop improved techniques for defending against
adversarial examples at scale. First, we implement the state of the art version
of adversarial training at unprecedented scale on ImageNet and investigate
whether it remains effective in this setting - an important open scientific
question (Athalye et ... | computer science |
7,822 | Forecasting Economics and Financial Time Series: ARIMA vs. LSTM | cs.LG | Forecasting time series data is an important subject in economics, business,
and finance. Traditionally, there are several techniques to effectively
forecast the next lag of time series data such as univariate Autoregressive
(AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and
more notably Auto... | computer science |
7,823 | Reviving and Improving Recurrent Back-Propagation | cs.LG | In this paper, we revisit the recurrent back-propagation (RBP) algorithm,
discuss the conditions under which it applies as well as how to satisfy them in
deep neural networks. We show that RBP can be unstable and propose two variants
based on conjugate gradient on the normal equations (CG-RBP) and Neumann series
(Neuma... | computer science |
7,824 | A Dual Approach to Scalable Verification of Deep Networks | cs.LG | This paper addresses the problem of formally verifying desirable properties
of neural networks, i.e., obtaining provable guarantees that the outputs of the
neural network will always behave in a certain way for a given class of inputs.
Most previous work on this topic was limited in its applicability by the size
of the... | computer science |
7,825 | Learning Long Term Dependencies via Fourier Recurrent Units | cs.LG | It is a known fact that training recurrent neural networks for tasks that
have long term dependencies is challenging. One of the main reasons is the
vanishing or exploding gradient problem, which prevents gradient information
from propagating to early layers. In this paper we propose a simple recurrent
architecture, th... | computer science |
7,826 | Structural query-by-committee | cs.LG | In this work, we describe a framework that unifies many different interactive
learning tasks. We present a generalization of the {\it query-by-committee}
active learning algorithm for this setting, and we study its consistency and
rate of convergence, both theoretically and empirically, with and without
noise. | computer science |
7,827 | Early Hospital Mortality Prediction using Vital Signals | cs.LG | Early hospital mortality prediction is critical as intensivists strive to
make efficient medical decisions about the severely ill patients staying in
intensive care units. As a result, various methods have been developed to
address this problem based on clinical records. However, some of the laboratory
test results are... | computer science |
7,828 | A Robust AUC Maximization Framework with Simultaneous Outlier Detection
and Feature Selection for Positive-Unlabeled Classification | cs.LG | The positive-unlabeled (PU) classification is a common scenario in real-world
applications such as healthcare, text classification, and bioinformatics, in
which we only observe a few samples labeled as "positive" together with a large
volume of "unlabeled" samples that may contain both positive and negative
samples. Bu... | computer science |
7,829 | Confounder Detection in High Dimensional Linear Models using First
Moments of Spectral Measures | stat.ML | In this paper, we study the confounder detection problem in the linear model,
where the target variable $Y$ is predicted using its $n$ potential causes
$X_n=(x_1,...,x_n)^T$. Based on an assumption of rotation invariant generating
process of the model, recent study shows that the spectral measure induced by
the regress... | computer science |
7,830 | TBD: Benchmarking and Analyzing Deep Neural Network Training | cs.LG | The recent popularity of deep neural networks (DNNs) has generated a lot of
research interest in performing DNN-related computation efficiently. However,
the primary focus is usually very narrow and limited to (i) inference -- i.e.
how to efficiently execute already trained models and (ii) image classification
networks... | computer science |
7,831 | Estimating the intrinsic dimension of datasets by a minimal neighborhood
information | stat.ML | Analyzing large volumes of high-dimensional data is an issue of fundamental
importance in data science, molecular simulations and beyond. Several
approaches work on the assumption that the important content of a dataset
belongs to a manifold whose Intrinsic Dimension (ID) is much lower than the
crude large number of co... | computer science |
7,832 | Learning non-Gaussian Time Series using the Box-Cox Gaussian Process | stat.ML | Gaussian processes (GPs) are Bayesian nonparametric generative models that
provide interpretability of hyperparameters, admit closed-form expressions for
training and inference, and are able to accurately represent uncertainty. To
model general non-Gaussian data with complex correlation structure, GPs can be
paired wit... | computer science |
7,833 | Monte Carlo Information Geometry: The dually flat case | cs.LG | Exponential families and mixture families are parametric probability models
that can be geometrically studied as smooth statistical manifolds with respect
to any statistical divergence like the Kullback-Leibler (KL) divergence or the
Hellinger divergence. When equipping a statistical manifold with the KL
divergence, th... | computer science |
7,834 | Fair Deep Learning Prediction for Healthcare Applications with
Confounder Filtering | stat.ML | The rapid development of deep learning methods has permitted the fast and
accurate medical decision making from complex structured data, like CT images
or MRI. However, some problems still exist in such applications that may lead
to imperfect predictions. Previous observations have shown that, confounding
factors, if h... | computer science |
7,835 | MLtuner: System Support for Automatic Machine Learning Tuning | cs.LG | MLtuner automatically tunes settings for training tunables (such as the
learning rate, the momentum, the mini-batch size, and the data staleness bound)
that have a significant impact on large-scale machine learning (ML)
performance. Traditionally, these tunables are set manually, which is
unsurprisingly error-prone and... | computer science |
7,836 | Stacked Neural Networks for end-to-end ciliary motion analysis | cs.LG | Cilia are hairlike structures protruding from nearly every cell in the body.
Diseases known as ciliopathies, where cilia function is disrupted, can result
in a wide spectrum of disorders. However, most techniques for assessing ciliary
motion rely on manual identification and tracking of cilia; this process is
laborious... | computer science |
7,837 | Meta Reinforcement Learning with Latent Variable Gaussian Processes | stat.ML | Data efficiency, i.e., learning from small data sets, is critical in many
practical applications where data collection is time consuming or expensive,
e.g., robotics, animal experiments or drug design. Meta learning is one way to
increase the data efficiency of learning algorithms by generalizing learned
concepts from ... | computer science |
7,838 | Generative Multi-Agent Behavioral Cloning | cs.LG | We propose and study the problem of generative multi-agent behavioral
cloning, where the goal is to learn a generative multi-agent policy from
pre-collected demonstration data. Building upon advances in deep generative
models, we present a hierarchical policy framework that can tractably learn
complex mappings from inp... | computer science |
7,839 | Domain Adaptation with Randomized Expectation Maximization | stat.ML | Domain adaptation (DA) is the task of classifying an unlabeled dataset
(target) using a labeled dataset (source) from a related domain. The majority
of successful DA methods try to directly match the distributions of the source
and target data by transforming the feature space. Despite their success, state
of the art m... | computer science |
7,840 | Graph-based regularization for regression problems with
highly-correlated designs | stat.ML | Sparse models for high-dimensional linear regression and machine learning
have received substantial attention over the past two decades. Model selection,
or determining which features or covariates are the best explanatory variables,
is critical to the interpretability of a learned model. Much of the current
literature... | computer science |
7,841 | Efficient Recurrent Neural Networks using Structured Matrices in FPGAs | cs.LG | Recurrent Neural Networks (RNNs) are becoming increasingly important for time
series-related applications which require efficient and real-time
implementations. The recent pruning based work ESE suffers from degradation of
performance/energy efficiency due to the irregular network structure after
pruning. We propose bl... | computer science |
7,842 | Some Theoretical Properties of GANs | stat.ML | Generative Adversarial Networks (GANs) are a class of generative algorithms
that have been shown to produce state-of-the art samples, especially in the
domain of image creation. The fundamental principle of GANs is to approximate
the unknown distribution of a given data set by optimizing an objective
function through a... | computer science |
7,843 | Multi-view Metric Learning in Vector-valued Kernel Spaces | cs.LG | We consider the problem of metric learning for multi-view data and present a
novel method for learning within-view as well as between-view metrics in
vector-valued kernel spaces, as a way to capture multi-modal structure of the
data. We formulate two convex optimization problems to jointly learn the metric
and the clas... | computer science |
7,844 | Scalable Generalized Dynamic Topic Models | stat.ML | Dynamic topic models (DTMs) model the evolution of prevalent themes in
literature, online media, and other forms of text over time. DTMs assume that
word co-occurrence statistics change continuously and therefore impose
continuous stochastic process priors on their model parameters. These dynamical
priors make inferenc... | computer science |
7,845 | An Unsupervised Multivariate Time Series Kernel Approach for Identifying
Patients with Surgical Site Infection from Blood Samples | stat.ML | A large fraction of the electronic health records consists of clinical
measurements collected over time, such as blood tests, which provide important
information about the health status of a patient. These sequences of clinical
measurements are naturally represented as time series, characterized by
multiple variables a... | computer science |
7,846 | An Exercise Fatigue Detection Model Based on Machine Learning Methods | stat.ML | This study proposes an exercise fatigue detection model based on real-time
clinical data which includes time domain analysis, frequency domain analysis,
detrended fluctuation analysis, approximate entropy, and sample entropy.
Furthermore, this study proposed a feature extraction method which is combined
with an analyti... | computer science |
7,847 | Incremental Learning-to-Learn with Statistical Guarantees | stat.ML | In learning-to-learn the goal is to infer a learning algorithm that works
well on a class of tasks sampled from an unknown meta distribution. In contrast
to previous work on batch learning-to-learn, we consider a scenario where tasks
are presented sequentially and the algorithm needs to adapt incrementally to
improve i... | computer science |
7,848 | Clustering to Reduce Spatial Data Set Size | cs.LG | Traditionally it had been a problem that researchers did not have access to
enough spatial data to answer pressing research questions or build compelling
visualizations. Today, however, the problem is often that we have too much
data. Spatially redundant or approximately redundant points may refer to a
single feature (... | computer science |
7,849 | Seglearn: A Python Package for Learning Sequences and Time Series | stat.ML | Seglearn is an open-source python package for machine learning time series or
sequences using a sliding window segmentation approach. The implementation
provides a flexible pipeline for tackling classification, regression, and
forecasting problems with multivariate sequence and contextual data. This
package is compatib... | computer science |
7,850 | Speaker Clustering With Neural Networks And Audio Processing | cs.SD | Speaker clustering is the task of differentiating speakers in a recording. In
a way, the aim is to answer "who spoke when" in audio recordings. A common
method used in industry is feature extraction directly from the recording
thanks to MFCC features, and by using well-known techniques such as Gaussian
Mixture Models (... | computer science |
7,851 | Gradient Descent Quantizes ReLU Network Features | stat.ML | Deep neural networks are often trained in the over-parametrized regime (i.e.
with far more parameters than training examples), and understanding why the
training converges to solutions that generalize remains an open problem.
Several studies have highlighted the fact that the training procedure, i.e.
mini-batch Stochas... | computer science |
7,852 | Learning through deterministic assignment of hidden parameters | cs.LG | Supervised learning frequently boils down to determining hidden and bright
parameters in a parameterized hypothesis space based on finite input-output
samples. The hidden parameters determine the attributions of hidden predictors
or the nonlinear mechanism of an estimator, while the bright parameters
characterize how h... | computer science |
7,853 | Demystifying Deep Learning: A Geometric Approach to Iterative
Projections | cs.LG | Parametric approaches to Learning, such as deep learning (DL), are highly
popular in nonlinear regression, in spite of their extremely difficult training
with their increasing complexity (e.g. number of layers in DL). In this paper,
we present an alternative semi-parametric framework which foregoes the
ordinarily requi... | computer science |
7,854 | Attention Solves Your TSP | stat.ML | We propose a framework for solving combinatorial optimization problems of
which the output can be represented as a sequence of input elements. As an
alternative to the Pointer Network, we parameterize a policy by a model based
entirely on (graph) attention layers, and train it efficiently using REINFORCE
with a simple ... | computer science |
7,855 | Ensembles of Radial Basis Function Networks for Spectroscopic Detection
of Cervical Pre-Cancer | cs.NE | The mortality related to cervical cancer can be substantially reduced through
early detection and treatment. However, current detection techniques, such as
Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and
specificity.
In vivo fluorescence spectroscopy is a technique which quickly,
non-inv... | computer science |
7,856 | Non-convex cost functionals in boosting algorithms and methods for panel
selection | cs.NE | In this document we propose a new improvement for boosting techniques as
proposed in Friedman '99 by the use of non-convex cost functional. The idea is
to introduce a correlation term to better deal with forecasting of additive
time series. The problem is discussed in a theoretical way to prove the
existence of minimiz... | computer science |
7,857 | Evolving controllers for simulated car racing | cs.NE | This paper describes the evolution of controllers for racing a simulated
radio-controlled car around a track, modelled on a real physical track. Five
different controller architectures were compared, based on neural networks,
force fields and action sequences. The controllers use either egocentric (first
person), Newto... | computer science |
7,858 | Mutual information for the selection of relevant variables in
spectrometric nonlinear modelling | cs.LG | Data from spectrophotometers form vectors of a large number of exploitable
variables. Building quantitative models using these variables most often
requires using a smaller set of variables than the initial one. Indeed, a too
large number of input variables to a model results in a too large number of
parameters, leadin... | computer science |
7,859 | Uncovering delayed patterns in noisy and irregularly sampled time
series: an astronomy application | cs.LG | We study the problem of estimating the time delay between two signals
representing delayed, irregularly sampled and noisy versions of the same
underlying pattern. We propose and demonstrate an evolutionary algorithm for
the (hyper)parameter estimation of a kernel-based technique in the context of
an astronomical proble... | computer science |
7,860 | Intrusion Detection In Mobile Ad Hoc Networks Using GA Based Feature
Selection | cs.NE | Mobile ad hoc networking (MANET) has become an exciting and important
technology in recent years because of the rapid proliferation of wireless
devices. MANETs are highly vulnerable to attacks due to the open medium,
dynamically changing network topology and lack of centralized monitoring point.
It is important to sear... | computer science |
7,861 | An optimized recursive learning algorithm for three-layer feedforward
neural networks for mimo nonlinear system identifications | cs.NE | Back-propagation with gradient method is the most popular learning algorithm
for feed-forward neural networks. However, it is critical to determine a proper
fixed learning rate for the algorithm. In this paper, an optimized recursive
algorithm is presented for online learning based on matrix operation and
optimization ... | computer science |
7,862 | A hybrid learning algorithm for text classification | cs.NE | Text classification is the process of classifying documents into predefined
categories based on their content. Existing supervised learning algorithms to
automatically classify text need sufficient documents to learn accurately. This
paper presents a new algorithm for text classification that requires fewer
documents f... | computer science |
7,863 | Distribution-Independent Evolvability of Linear Threshold Functions | cs.LG | Valiant's (2007) model of evolvability models the evolutionary process of
acquiring useful functionality as a restricted form of learning from random
examples. Linear threshold functions and their various subclasses, such as
conjunctions and decision lists, play a fundamental role in learning theory and
hence their evo... | computer science |
7,864 | A Spiking Neural Learning Classifier System | cs.NE | Learning Classifier Systems (LCS) are population-based reinforcement learners
used in a wide variety of applications. This paper presents a LCS where each
traditional rule is represented by a spiking neural network, a type of network
with dynamic internal state. We employ a constructivist model of growth of both
neuron... | computer science |
7,865 | PID Parameters Optimization by Using Genetic Algorithm | cs.SY | Time delays are components that make time-lag in systems response. They arise
in physical, chemical, biological and economic systems, as well as in the
process of measurement and computation. In this work, we implement Genetic
Algorithm (GA) in determining PID controller parameters to compensate the delay
in First Orde... | computer science |
7,866 | Autoregressive short-term prediction of turning points using support
vector regression | cs.LG | This work is concerned with autoregressive prediction of turning points in
financial price sequences. Such turning points are critical local extrema
points along a series, which mark the start of new swings. Predicting the
future time of such turning points or even their early or late identification
slightly before or ... | computer science |
7,867 | Analog readout for optical reservoir computers | cs.ET | Reservoir computing is a new, powerful and flexible machine learning
technique that is easily implemented in hardware. Recently, by using a
time-multiplexed architecture, hardware reservoir computers have reached
performance comparable to digital implementations. Operating speeds allowing
for real time information oper... | computer science |
7,868 | Coupled Neural Associative Memories | cs.NE | We propose a novel architecture to design a neural associative memory that is
capable of learning a large number of patterns and recalling them later in
presence of noise. It is based on dividing the neurons into local clusters and
parallel plains, very similar to the architecture of the visual cortex of
macaque brain.... | computer science |
7,869 | Eignets for function approximation on manifolds | cs.LG | Let $\XX$ be a compact, smooth, connected, Riemannian manifold without
boundary, $G:\XX\times\XX\to \RR$ be a kernel. Analogous to a radial basis
function network, an eignet is an expression of the form $\sum_{j=1}^M
a_jG(\circ,y_j)$, where $a_j\in\RR$, $y_j\in\XX$, $1\le j\le M$. We describe a
deterministic, universal... | computer science |
7,870 | A Generalized Markov-Chain Modelling Approach to $(1,λ)$-ES Linear
Optimization: Technical Report | cs.NA | Several recent publications investigated Markov-chain modelling of linear
optimization by a $(1,\lambda)$-ES, considering both unconstrained and linearly
constrained optimization, and both constant and varying step size. All of them
assume normality of the involved random steps, and while this is consistent
with a blac... | computer science |
7,871 | Brain-like associative learning using a nanoscale non-volatile phase
change synaptic device array | cs.NE | Recent advances in neuroscience together with nanoscale electronic device
technology have resulted in huge interests in realizing brain-like computing
hardwares using emerging nanoscale memory devices as synaptic elements.
Although there has been experimental work that demonstrated the operation of
nanoscale synaptic e... | computer science |
7,872 | Weighted Patterns as a Tool for Improving the Hopfield Model | cs.LG | We generalize the standard Hopfield model to the case when a weight is
assigned to each input pattern. The weight can be interpreted as the frequency
of the pattern occurrence at the input of the network. In the framework of the
statistical physics approach we obtain the saddle-point equation allowing us to
examine the... | computer science |
7,873 | Genetic Programming for Multibiometrics | cs.NE | Biometric systems suffer from some drawbacks: a biometric system can provide
in general good performances except with some individuals as its performance
depends highly on the quality of the capture. One solution to solve some of
these problems is to use multibiometrics where different biometric systems are
combined to... | computer science |
7,874 | Should I Stay or Should I Go: Coordinating Biological Needs with
Continuously-updated Assessments of the Environment | cs.NE | This paper presents Wanderer, a model of how autonomous adaptive systems
coordinate internal biological needs with moment-by-moment assessments of the
probabilities of events in the external world. The extent to which Wanderer
moves about or explores its environment reflects the relative activations of
two competing mo... | computer science |
7,875 | Hybrid data clustering approach using K-Means and Flower Pollination
Algorithm | cs.LG | Data clustering is a technique for clustering set of objects into known
number of groups. Several approaches are widely applied to data clustering so
that objects within the clusters are similar and objects in different clusters
are far away from each other. K-Means, is one of the familiar center based
clustering algor... | computer science |
7,876 | Neural Network with Unbounded Activation Functions is Universal
Approximator | cs.NE | This paper presents an investigation of the approximation property of neural
networks with unbounded activation functions, such as the rectified linear unit
(ReLU), which is the new de-facto standard of deep learning. The ReLU network
can be analyzed by the ridgelet transform with respect to Lizorkin
distributions. By ... | computer science |
7,877 | A Max-Sum algorithm for training discrete neural networks | cs.LG | We present an efficient learning algorithm for the problem of training neural
networks with discrete synapses, a well-known hard (NP-complete) discrete
optimization problem. The algorithm is a variant of the so-called Max-Sum (MS)
algorithm. In particular, we show how, for bounded integer weights with $q$
distinct stat... | computer science |
7,878 | Learning Program Embeddings to Propagate Feedback on Student Code | cs.LG | Providing feedback, both assessing final work and giving hints to stuck
students, is difficult for open-ended assignments in massive online classes
which can range from thousands to millions of students. We introduce a neural
network method to encode programs as a linear mapping from an embedded
precondition space to a... | computer science |
7,879 | Using Recurrent Neural Networks to Optimize Dynamical Decoupling for
Quantum Memory | cs.LG | We utilize machine learning models which are based on recurrent neural
networks to optimize dynamical decoupling (DD) sequences. DD is a relatively
simple technique for suppressing the errors in quantum memory for certain noise
models. In numerical simulations, we show that with minimum use of prior
knowledge and start... | computer science |
7,880 | Recurrent Neural Networks for Polyphonic Sound Event Detection in Real
Life Recordings | cs.SD | In this paper we present an approach to polyphonic sound event detection in
real life recordings based on bi-directional long short term memory (BLSTM)
recurrent neural networks (RNNs). A single multilabel BLSTM RNN is trained to
map acoustic features of a mixture signal consisting of sounds from multiple
classes, to b... | computer science |
7,881 | Revisiting Distributed Synchronous SGD | cs.LG | Distributed training of deep learning models on large-scale training data is
typically conducted with asynchronous stochastic optimization to maximize the
rate of updates, at the cost of additional noise introduced from asynchrony. In
contrast, the synchronous approach is often thought to be impractical due to
idle tim... | computer science |
7,882 | Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural
Networks | cs.NE | We present in this paper a simple, yet efficient convolutional neural network
(CNN) architecture for robust audio event recognition. Opposing to deep CNN
architectures with multiple convolutional and pooling layers topped up with
multiple fully connected layers, the proposed network consists of only three
layers: convo... | computer science |
7,883 | Crafting Adversarial Input Sequences for Recurrent Neural Networks | cs.CR | Machine learning models are frequently used to solve complex security
problems, as well as to make decisions in sensitive situations like guiding
autonomous vehicles or predicting financial market behaviors. Previous efforts
have shown that numerous machine learning models were vulnerable to adversarial
manipulations o... | computer science |
7,884 | Biologically Inspired Spiking Neurons : Piecewise Linear Models and
Digital Implementation | cs.LG | There has been a strong push recently to examine biological scale simulations
of neuromorphic algorithms to achieve stronger inference capabilities. This
paper presents a set of piecewise linear spiking neuron models, which can
reproduce different behaviors, similar to the biological neuron, both for a
single neuron as... | computer science |
7,885 | Auto-encoders: reconstruction versus compression | cs.NE | We discuss the similarities and differences between training an auto-encoder
to minimize the reconstruction error, and training the same auto-encoder to
compress the data via a generative model. Minimizing a codelength for the data
using an auto-encoder is equivalent to minimizing the reconstruction error plus
some cor... | computer science |
7,886 | ANN Model to Predict Stock Prices at Stock Exchange Markets | cs.CE | Stock exchanges are considered major players in financial sectors of many
countries. Most Stockbrokers, who execute stock trade, use technical,
fundamental or time series analysis in trying to predict stock prices, so as to
advise clients. However, these strategies do not usually guarantee good returns
because they gui... | computer science |
7,887 | Neural Network-Based Active Learning in Multivariate Calibration | cs.NE | In chemometrics, data from infrared or near-infrared (NIR) spectroscopy are
often used to identify a compound or to analyze the composition of amaterial.
This involves the calibration of models that predict the concentration
ofmaterial constituents from the measured NIR spectrum. An interesting aspect
of multivariate c... | computer science |
7,888 | Deep Transform: Time-Domain Audio Error Correction via Probabilistic
Re-Synthesis | cs.SD | In the process of recording, storage and transmission of time-domain audio
signals, errors may be introduced that are difficult to correct in an
unsupervised way. Here, we train a convolutional deep neural network to
re-synthesize input time-domain speech signals at its output layer. We then use
this abstract transform... | computer science |
7,889 | Deep Transform: Cocktail Party Source Separation via Probabilistic
Re-Synthesis | cs.SD | In cocktail party listening scenarios, the human brain is able to separate
competing speech signals. However, the signal processing implemented by the
brain to perform cocktail party listening is not well understood. Here, we
trained two separate convolutive autoencoder deep neural networks (DNN) to
separate monaural a... | computer science |
7,890 | Probabilistic Binary-Mask Cocktail-Party Source Separation in a
Convolutional Deep Neural Network | cs.SD | Separation of competing speech is a key challenge in signal processing and a
feat routinely performed by the human auditory brain. A long standing benchmark
of the spectrogram approach to source separation is known as the ideal binary
mask. Here, we train a convolutional deep neural network, on a two-speaker
cocktail p... | computer science |
7,891 | Towards deep learning with spiking neurons in energy based models with
contrastive Hebbian plasticity | cs.LG | In machine learning, error back-propagation in multi-layer neural networks
(deep learning) has been impressively successful in supervised and
reinforcement learning tasks. As a model for learning in the brain, however,
deep learning has long been regarded as implausible, since it relies in its
basic form on a non-local... | computer science |
7,892 | Stable Memory Allocation in the Hippocampus: Fundamental Limits and
Neural Realization | cs.NE | It is believed that hippocampus functions as a memory allocator in brain, the
mechanism of which remains unrevealed. In Valiant's neuroidal model, the
hippocampus was described as a randomly connected graph, the computation on
which maps input to a set of activated neuroids with stable size. Valiant
proposed three requ... | computer science |
7,893 | An IoT Endpoint System-on-Chip for Secure and Energy-Efficient
Near-Sensor Analytics | cs.AR | Near-sensor data analytics is a promising direction for IoT endpoints, as it
minimizes energy spent on communication and reduces network load - but it also
poses security concerns, as valuable data is stored or sent over the network at
various stages of the analytics pipeline. Using encryption to protect sensitive
data... | computer science |
7,894 | Hybrid methodology for hourly global radiation forecasting in
Mediterranean area | cs.NE | The renewable energies prediction and particularly global radiation
forecasting is a challenge studied by a growing number of research teams. This
paper proposes an original technique to model the insolation time series based
on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving
Average (ARMA) mod... | computer science |
7,895 | Learning ambiguous functions by neural networks | cs.NE | It is not, in general, possible to have access to all variables that
determine the behavior of a system. Having identified a number of variables
whose values can be accessed, there may still be hidden variables which
influence the dynamics of the system. The result is model ambiguity in the
sense that, for the same (or... | computer science |
7,896 | Modeling correlations in spontaneous activity of visual cortex with
centered Gaussian-binary deep Boltzmann machines | cs.NE | Spontaneous cortical activity -- the ongoing cortical activities in absence
of intentional sensory input -- is considered to play a vital role in many
aspects of both normal brain functions and mental dysfunctions. We present a
centered Gaussian-binary Deep Boltzmann Machine (GDBM) for modeling the
activity in early co... | computer science |
7,897 | Notes on Generalized Linear Models of Neurons | cs.NE | Experimental neuroscience increasingly requires tractable models for
analyzing and predicting the behavior of neurons and networks. The generalized
linear model (GLM) is an increasingly popular statistical framework for
analyzing neural data that is flexible, exhibits rich dynamic behavior and is
computationally tracta... | computer science |
7,898 | Sequential Click Prediction for Sponsored Search with Recurrent Neural
Networks | cs.IR | Click prediction is one of the fundamental problems in sponsored search. Most
of existing studies took advantage of machine learning approaches to predict ad
click for each event of ad view independently. However, as observed in the
real-world sponsored search system, user's behaviors on ads yield high
dependency on ho... | computer science |
7,899 | One weird trick for parallelizing convolutional neural networks | cs.NE | I present a new way to parallelize the training of convolutional neural
networks across multiple GPUs. The method scales significantly better than all
alternatives when applied to modern convolutional neural networks. | computer science |
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