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7,500 | Deeper Insights into Graph Convolutional Networks for Semi-Supervised
Learning | cs.LG | Many interesting problems in machine learning are being revisited with new
deep learning tools. For graph-based semisupervised learning, a recent
important development is graph convolutional networks (GCNs), which nicely
integrate local vertex features and graph topology in the convolutional layers.
Although the GCN mo... | computer science |
7,501 | Drug Selection via Joint Push and Learning to Rank | cs.LG | Selecting the right drugs for the right patients is a primary goal of
precision medicine. In this manuscript, we consider the problem of cancer drug
selection in a learning-to-rank framework. We have formulated the cancer drug
selection problem as to accurately predicting 1). the ranking positions of
sensitive drugs an... | computer science |
7,502 | Deep Learning for Electromyographic Hand Gesture Signal Classification
by Leveraging Transfer Learning | cs.LG | In recent years, the use of deep learning algorithms has become increasingly
more prominent. Within the field of electromyography-based gesture recognition
however, deep learning algorithms are seldom employed. This is due in part to
the large quantity of data required for the network to train on. The data
sparsity ari... | computer science |
7,503 | Semi-Supervised Convolutional Neural Networks for Human Activity
Recognition | cs.LG | Labeled data used for training activity recognition classifiers are usually
limited in terms of size and diversity. Thus, the learned model may not
generalize well when used in real-world use cases. Semi-supervised learning
augments labeled examples with unlabeled examples, often resulting in improved
performance. Howe... | computer science |
7,504 | A Theoretical Investigation of Graph Degree as an Unsupervised Normality
Measure | cs.LG | For a graph representation of a dataset, a straightforward normality measure
for a sample can be its graph degree. Considering a weighted graph, degree of a
sample is the sum of the corresponding row's values in a similarity matrix. The
measure is intuitive given the abnormal samples are usually rare and they are
dissi... | computer science |
7,505 | Training Set Debugging Using Trusted Items | cs.LG | Training set bugs are flaws in the data that adversely affect machine
learning. The training set is usually too large for man- ual inspection, but
one may have the resources to verify a few trusted items. The set of trusted
items may not by itself be adequate for learning, so we propose an algorithm
that uses these ite... | computer science |
7,506 | DKN: Deep Knowledge-Aware Network for News Recommendation | stat.ML | Online news recommender systems aim to address the information explosion of
news and make personalized recommendation for users. In general, news language
is highly condensed, full of knowledge entities and common sense. However,
existing methods are unaware of such external knowledge and cannot fully
discover latent k... | computer science |
7,507 | Transparent Model Distillation | stat.ML | Model distillation was originally designed to distill knowledge from a large,
complex teacher model to a faster, simpler student model without significant
loss in prediction accuracy. We investigate model distillation for another goal
-- transparency -- investigating if fully-connected neural networks can be
distilled ... | computer science |
7,508 | PDNet: Semantic Segmentation integrated with a Primal-Dual Network for
Document binarization | stat.ML | Binarization of digital documents is the task of classifying each pixel in an
image of the document as belonging to the background (parchment/paper) or
foreground (text/ink). Historical documents are often subject to degradations,
that make the task challenging. In the current work a deep neural network
architecture is... | computer science |
7,509 | Improving Bi-directional Generation between Different Modalities with
Variational Autoencoders | stat.ML | We investigate deep generative models that can exchange multiple modalities
bi-directionally, e.g., generating images from corresponding texts and vice
versa. A major approach to achieve this objective is to train a model that
integrates all the information of different modalities into a joint
representation and then t... | computer science |
7,510 | Classification of sparsely labeled spatio-temporal data through
semi-supervised adversarial learning | stat.ML | In recent years, Generative Adversarial Networks (GAN) have emerged as a
powerful method for learning the mapping from noisy latent spaces to realistic
data samples in high-dimensional space. So far, the development and application
of GANs have been predominantly focused on spatial data such as images. In this
project,... | computer science |
7,511 | Multivariate normal mixture modeling, clustering and classification with
the rebmix package | stat.ML | The rebmix package provides R functions for random univariate and
multivariate finite mixture model generation, estimation, clustering and
classification. The paper is focused on multivariate normal mixture models with
unrestricted variance-covariance matrices. The objective is to show how to
generate datasets for a kn... | computer science |
7,512 | Correlated Components Analysis --- Extracting Reliable Dimensions in
Multivariate Data | stat.ML | How does one find data dimensions that are reliably expressed across
repetitions? For example, in neuroscience one may want to identify combinations
of brain signals that are reliably activated across multiple trials or
subjects. For a clinical assessment with multiple ratings, one may want to
identify an aggregate sco... | computer science |
7,513 | Nonlinear Dimensionality Reduction on Graphs | cs.LG | In this era of data deluge, many signal processing and machine learning tasks
are faced with high-dimensional datasets, including images, videos, as well as
time series generated from social, commercial and brain network interactions.
Their efficient processing calls for dimensionality reduction techniques
capable of p... | computer science |
7,514 | Discrete Autoencoders for Sequence Models | cs.LG | Recurrent models for sequences have been recently successful at many tasks,
especially for language modeling and machine translation. Nevertheless, it
remains challenging to extract good representations from these models. For
instance, even though language has a clear hierarchical structure going from
characters throug... | computer science |
7,515 | Robustness of classification ability of spiking neural networks | stat.ML | It is well-known that the robustness of artificial neural networks (ANNs) is
important for their wide ranges of applications. In this paper, we focus on the
robustness of the classification ability of a spiking neural network which
receives perturbed inputs. Actually, the perturbation is allowed to be
arbitrary styles.... | computer science |
7,516 | Fast Power system security analysis with Guided Dropout | stat.ML | We propose a new method to efficiently compute load-flows (the steady-state
of the power-grid for given productions, consumptions and grid topology),
substituting conventional simulators based on differential equation solvers. We
use a deep feed-forward neural network trained with load-flows precomputed by
simulation. ... | computer science |
7,517 | Links: A High-Dimensional Online Clustering Method | stat.ML | We present a novel algorithm, called Links, designed to perform online
clustering on unit vectors in a high-dimensional Euclidean space. The algorithm
is appropriate when it is necessary to cluster data efficiently as it streams
in, and is to be contrasted with traditional batch clustering algorithms that
have access t... | computer science |
7,518 | Spherical CNNs | cs.LG | Convolutional Neural Networks (CNNs) have become the method of choice for
learning problems involving 2D planar images. However, a number of problems of
recent interest have created a demand for models that can analyze spherical
images. Examples include omnidirectional vision for drones, robots, and
autonomous cars, mo... | computer science |
7,519 | DeepDTA: Deep Drug-Target Binding Affinity Prediction | stat.ML | The identification of novel drug-target (DT) interactions is a substantial
part of the drug discovery process. Most of the computational methods that have
been proposed to predict DT interactions have focused on binary classification,
where the goal is to determine whether a DT pair interacts or not. However,
protein-l... | computer science |
7,520 | Low-rank Bandit Methods for High-dimensional Dynamic Pricing | cs.LG | We consider high dimensional dynamic multi-product pricing with an evolving
but low-dimensional linear demand model. Assuming the temporal variation in
cross-elasticities exhibits low-rank structure based on fixed (latent) features
of the products, we show that the revenue maximization problem reduces to an
online band... | computer science |
7,521 | Kernel Distillation for Gaussian Processes | stat.ML | Gaussian processes (GPs) are flexible models that can capture complex
structure in large-scale dataset due to their non-parametric nature. However,
the usage of GPs in real-world application is limited due to their high
computational cost at inference time. In this paper, we introduce a new
framework, \textit{kernel di... | computer science |
7,522 | Deep Multi-view Learning to Rank | cs.LG | We study the problem of learning to rank from multiple sources. Though
multi-view learning and learning to rank have been studied extensively leading
to a wide range of applications, multi-view learning to rank as a synergy of
both topics has received little attention. The aim of the paper is to propose a
composite ran... | computer science |
7,523 | DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic
Congestion | cs.LG | Non-recurring traffic congestion is caused by temporary disruptions, such as
accidents, sports games, adverse weather, etc. We use data related to real-time
traffic speed, jam factors (a traffic congestion indicator), and events
collected over a year from Nashville, TN to train a multi-layered deep neural
network. The ... | computer science |
7,524 | Fusarium Damaged Kernels Detection Using Transfer Learning on Deep
Neural Network Architecture | cs.LG | The present work shows the application of transfer learning for a pre-trained
deep neural network (DNN), using a small image dataset ($\approx$ 12,000) on a
single workstation with enabled NVIDIA GPU card that takes up to 1 hour to
complete the training task and archive an overall average accuracy of $94.7\%$.
The DNN ... | computer science |
7,525 | Incremental kernel PCA and the Nyström method | stat.ML | Incremental versions of batch algorithms are often desired, for increased
time efficiency in the streaming data setting, or increased memory efficiency
in general. In this paper we present a novel algorithm for incremental kernel
PCA, based on rank one updates to the eigendecomposition of the kernel matrix,
which is mo... | computer science |
7,526 | Optimizing Non-decomposable Measures with Deep Networks | stat.ML | We present a class of algorithms capable of directly training deep neural
networks with respect to large families of task-specific performance measures
such as the F-measure and the Kullback-Leibler divergence that are structured
and non-decomposable. This presents a departure from standard deep learning
techniques tha... | computer science |
7,527 | A Modified Sigma-Pi-Sigma Neural Network with Adaptive Choice of
Multinomials | cs.LG | Sigma-Pi-Sigma neural networks (SPSNNs) as a kind of high-order neural
networks can provide more powerful mapping capability than the traditional
feedforward neural networks (Sigma-Sigma neural networks). In the existing
literature, in order to reduce the number of the Pi nodes in the Pi layer, a
special multinomial P_... | computer science |
7,528 | Alternating Multi-bit Quantization for Recurrent Neural Networks | cs.LG | Recurrent neural networks have achieved excellent performance in many
applications. However, on portable devices with limited resources, the models
are often too large to deploy. For applications on the server with large scale
concurrent requests, the latency during inference can also be very critical for
costly comput... | computer science |
7,529 | One-class Collective Anomaly Detection based on Long Short-Term Memory
Recurrent Neural Networks | cs.LG | Intrusion detection for computer network systems has been becoming one of the
most critical tasks for network administrators today. It has an important role
for organizations, governments and our society due to the valuable resources
hosted on computer networks. Traditional misuse detection strategies are unable
to det... | computer science |
7,530 | Dual Memory Neural Computer for Asynchronous Two-view Sequential
Learning | cs.LG | One of the core tasks in multi-view learning is to capture relations among
views. For sequential data, the relations not only span across views, but also
extend throughout the view length to form long-term intra-view and inter-view
interactions. In this paper, we present a new memory augmented neural network
model that... | computer science |
7,531 | Joint Binary Neural Network for Multi-label Learning with Applications
to Emotion Classification | cs.LG | Recently the deep learning techniques have achieved success in multi-label
classification due to its automatic representation learning ability and the
end-to-end learning framework. Existing deep neural networks in multi-label
classification can be divided into two kinds: binary relevance neural network
(BRNN) and thre... | computer science |
7,532 | Multi-task Learning for Continuous Control | cs.LG | Reliable and effective multi-task learning is a prerequisite for the
development of robotic agents that can quickly learn to accomplish related,
everyday tasks. However, in the reinforcement learning domain, multi-task
learning has not exhibited the same level of success as in other domains, such
as computer vision. In... | computer science |
7,533 | Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain
Features | cs.LG | Unsupervised learning of time series data, also known as temporal clustering,
is a challenging problem in machine learning. Here we propose a novel
algorithm, Deep Temporal Clustering (DTC), to naturally integrate
dimensionality reduction and temporal clustering into a single end-to-end
learning framework, fully unsupe... | computer science |
7,534 | Hierarchical Adversarially Learned Inference | stat.ML | We propose a novel hierarchical generative model with a simple Markovian
structure and a corresponding inference model. Both the generative and
inference model are trained using the adversarial learning paradigm. We
demonstrate that the hierarchical structure supports the learning of
progressively more abstract represe... | computer science |
7,535 | Task-Aware Compressed Sensing with Generative Adversarial Networks | cs.LG | In recent years, neural network approaches have been widely adopted for
machine learning tasks, with applications in computer vision. More recently,
unsupervised generative models based on neural networks have been successfully
applied to model data distributions via low-dimensional latent spaces. In this
paper, we use... | computer science |
7,536 | To understand deep learning we need to understand kernel learning | stat.ML | Generalization performance of classifiers in deep learning has recently
become a subject of intense study. Heavily over-parametrized deep models tend
to fit training data exactly. Despite overfitting, they perform well on test
data, a phenomenon not yet fully understood.
The first point of our paper is that strong pe... | computer science |
7,537 | The Matrix Calculus You Need For Deep Learning | cs.LG | This paper is an attempt to explain all the matrix calculus you need in order
to understand the training of deep neural networks. We assume no math knowledge
beyond what you learned in calculus 1, and provide links to help you refresh
the necessary math where needed. Note that you do not need to understand this
materia... | computer science |
7,538 | Deep Learning with a Rethinking Structure for Multi-label Classification | cs.LG | Multi-label classification (MLC) is an important learning problem that
expects the learning algorithm to take the hidden correlation of the labels
into account. Extracting the hidden correlation is generally a challenging
task. In this work, we propose a novel deep learning framework to better
extract the hidden correl... | computer science |
7,539 | Training Generative Adversarial Networks via Primal-Dual Subgradient
Methods: A Lagrangian Perspective on GAN | cs.LG | We relate the minimax game of generative adversarial networks (GANs) to
finding the saddle points of the Lagrangian function for a convex optimization
problem, where the discriminator outputs and the distribution of generator
outputs play the roles of primal variables and dual variables, respectively.
This formulation ... | computer science |
7,540 | Spectral Learning of Binomial HMMs for DNA Methylation Data | cs.LG | We consider learning parameters of Binomial Hidden Markov Models, which may
be used to model DNA methylation data. The standard algorithm for the problem
is EM, which is computationally expensive for sequences of the scale of the
mammalian genome. Recently developed spectral algorithms can learn parameters
of latent va... | computer science |
7,541 | Cadre Modeling: Simultaneously Discovering Subpopulations and Predictive
Models | stat.ML | We consider the problem in regression analysis of identifying subpopulations
that exhibit different patterns of response, where each subpopulation requires
a different underlying model. Unlike statistical cohorts, these subpopulations
are not known a priori; thus, we refer to them as cadres. When the cadres and
their a... | computer science |
7,542 | Transductive Adversarial Networks (TAN) | stat.ML | Transductive Adversarial Networks (TAN) is a novel domain-adaptation machine
learning framework that is designed for learning a conditional probability
distribution on unlabelled input data in a target domain, while also only
having access to: (1) easily obtained labelled data from a related source
domain, which may ha... | computer science |
7,543 | A Game-Theoretic Approach to Design Secure and Resilient Distributed
Support Vector Machines | stat.ML | Distributed Support Vector Machines (DSVM) have been developed to solve
large-scale classification problems in networked systems with a large number of
sensors and control units. However, the systems become more vulnerable as
detection and defense are increasingly difficult and expensive. This work aims
to develop secu... | computer science |
7,544 | Learning Sparse Wavelet Representations | cs.LG | In this work we propose a method for learning wavelet filters directly from
data. We accomplish this by framing the discrete wavelet transform as a
modified convolutional neural network. We introduce an autoencoder wavelet
transform network that is trained using gradient descent. We show that the
model is capable of le... | computer science |
7,545 | Statistical Learnability of Generalized Additive Models based on Total
Variation Regularization | stat.ML | A generalized additive model (GAM, Hastie and Tibshirani (1987)) is a
nonparametric model by the sum of univariate functions with respect to each
explanatory variable, i.e., $f({\mathbf x}) = \sum f_j(x_j)$, where
$x_j\in\mathbb{R}$ is $j$-th component of a sample ${\mathbf x}\in
\mathbb{R}^p$. In this paper, we introd... | computer science |
7,546 | Thompson Sampling for Dynamic Pricing | stat.ML | In this paper we apply active learning algorithms for dynamic pricing in a
prominent e-commerce website. Dynamic pricing involves changing the price of
items on a regular basis, and uses the feedback from the pricing decisions to
update prices of the items. Most popular approaches to dynamic pricing use a
passive learn... | computer science |
7,547 | Learning Latent Representations in Neural Networks for Clustering
through Pseudo Supervision and Graph-based Activity Regularization | cs.LG | In this paper, we propose a novel unsupervised clustering approach exploiting
the hidden information that is indirectly introduced through a pseudo
classification objective. Specifically, we randomly assign a pseudo
parent-class label to each observation which is then modified by applying the
domain specific transforma... | computer science |
7,548 | Relational Autoencoder for Feature Extraction | cs.LG | Feature extraction becomes increasingly important as data grows high
dimensional. Autoencoder as a neural network based feature extraction method
achieves great success in generating abstract features of high dimensional
data. However, it fails to consider the relationships of data samples which may
affect experimental... | computer science |
7,549 | Adversarial Metric Learning | cs.LG | In the past decades, intensive efforts have been put to design various loss
functions and metric forms for metric learning problem. These improvements have
shown promising results when the test data is similar to the training data.
However, the trained models often fail to produce reliable distances on the
ambiguous te... | computer science |
7,550 | Self-Bounded Prediction Suffix Tree via Approximate String Matching | cs.LG | Prediction suffix trees (PST) provide an effective tool for sequence
modelling and prediction. Current prediction techniques for PSTs rely on exact
matching between the suffix of the current sequence and the previously observed
sequence. We present a provably correct algorithm for learning a PST with
approximate suffix... | computer science |
7,551 | Curve Registered Coupled Low Rank Factorization | stat.ML | We propose an extension of the canonical polyadic (CP) tensor model where one
of the latent factors is allowed to vary through data slices in a constrained
way. The components of the latent factors, which we want to retrieve from data,
can vary from one slice to another up to a diffeomorphism. We suppose that the
diffe... | computer science |
7,552 | Deep clustering of longitudinal data | stat.ML | Deep neural networks are a family of computational models that have led to a
dramatical improvement of the state of the art in several domains such as
image, voice or text analysis. These methods provide a framework to model
complex, non-linear interactions in large datasets, and are naturally suited to
the analysis of... | computer science |
7,553 | Bayesian inference for bivariate ranks | stat.ML | A recommender system based on ranks is proposed, where an expert's ranking of
a set of objects and a user's ranking of a subset of those objects are combined
to make a prediction of the user's ranking of all objects. The rankings are
assumed to be induced by latent continuous variables corresponding to the
grades assig... | computer science |
7,554 | Learning Localized Spatio-Temporal Models From Streaming Data | stat.ML | We address the problem of predicting spatio-temporal processes with temporal
patterns that vary across spatial regions, when data is obtained as a stream.
That is, when the training dataset is augmented sequentially. Specifically, we
develop a localized spatio-temporal covariance model of the process that can
capture s... | computer science |
7,555 | Information Planning for Text Data | stat.ML | Information planning enables faster learning with fewer training examples. It
is particularly applicable when training examples are costly to obtain. This
work examines the advantages of information planning for text data by focusing
on three supervised models: Naive Bayes, supervised LDA and deep neural
networks. We s... | computer science |
7,556 | Predicting University Students' Academic Success and Choice of Major
using Random Forests | stat.ML | In this paper, a large data set containing every course taken by every
undergraduate student in a major university in Canada over 10 years is
analyzed. Modern machine learning algorithms can use large data sets to build
useful tools for the data provider, in this case, the university. In this
article, two classifiers a... | computer science |
7,557 | Generalization of an Upper Bound on the Number of Nodes Needed to
Achieve Linear Separability | stat.ML | An important issue in neural network research is how to choose the number of
nodes and layers such as to solve a classification problem. We provide new
intuitions based on earlier results by An et al. (2015) by deriving an upper
bound on the number of nodes in networks with two hidden layers such that
linear separabili... | computer science |
7,558 | Modeling Dynamics with Deep Transition-Learning Networks | cs.LG | Markov processes, both classical and higher order, are often used to model
dynamic processes, such as stock prices, molecular dynamics, and Monte Carlo
methods. Previous works have shown that an autoencoder can be formulated as a
specific type of Markov chain. Here, we propose a generative neural network
known as a tra... | computer science |
7,559 | Enhanced version of AdaBoostM1 with J48 Tree learning method | stat.ML | Machine Learning focuses on the construction and study of systems that can
learn from data. This is connected with the classification problem, which
usually is what Machine Learning algorithms are designed to solve. When a
machine learning method is used by people with no special expertise in machine
learning, it is im... | computer science |
7,560 | Bayesian Optimization Using Monotonicity Information and Its Application
in Machine Learning Hyperparameter | cs.LG | We propose an algorithm for a family of optimization problems where the
objective can be decomposed as a sum of functions with monotonicity properties.
The motivating problem is optimization of hyperparameters of machine learning
algorithms, where we argue that the objective, validation error, can be
decomposed as mono... | computer science |
7,561 | Critères de qualité d'un classifieur généraliste | stat.ML | This paper considers the problem of choosing a good classifier. For each
problem there exist an optimal classifier, but none are optimal, regarding the
error rate, in all cases. Because there exists a large number of classifiers, a
user would rather prefer an all-purpose classifier that is easy to adjust, in
the hope t... | computer science |
7,562 | Distributed One-class Learning | cs.LG | We propose a cloud-based filter trained to block third parties from uploading
privacy-sensitive images of others to online social media. The proposed filter
uses Distributed One-Class Learning, which decomposes the cloud-based filter
into multiple one-class classifiers. Each one-class classifier captures the
properties... | computer science |
7,563 | Learning Correlation Space for Time Series | cs.LG | We propose an approximation algorithm for efficient correlation search in
time series data. In our method, we use Fourier transform and neural network to
embed time series into a low-dimensional Euclidean space. The given space is
learned such that time series correlation can be effectively approximated from
Euclidean ... | computer science |
7,564 | Learning to Recommend via Inverse Optimal Matching | stat.ML | We consider recommendation in the context of optimal matching, i.e., we need
to pair or match a user with an item in an optimal way. The framework is
particularly relevant when the supply of an individual item is limited and it
can only satisfy a small number of users even though it may be preferred by
many. We leverag... | computer science |
7,565 | Deep learning with t-exponential Bayesian kitchen sinks | cs.LG | Bayesian learning has been recently considered as an effective means of
accounting for uncertainty in trained deep network parameters. This is of
crucial importance when dealing with small or sparse training datasets. On the
other hand, shallow models that compute weighted sums of their inputs, after
passing them throu... | computer science |
7,566 | Differentiable Dynamic Programming for Structured Prediction and
Attention | stat.ML | Dynamic programming (DP) solves a variety of structured combinatorial
problems by iteratively breaking them down into smaller subproblems. In spite
of their versatility, DP algorithms are usually non-differentiable, which
hampers their use as a layer in neural networks trained by backpropagation. To
address this issue,... | computer science |
7,567 | On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes
Optimal Classifier | stat.ML | We study the rates of convergence from empirical surrogate risk minimizers to
the Bayes optimal classifier. Specifically, we introduce the notion of
\emph{consistency intensity} to characterize a surrogate loss function and
exploit this notion to obtain the rate of convergence from an empirical
surrogate risk minimizer... | computer science |
7,568 | Dual Control Memory Augmented Neural Networks for Treatment
Recommendations | cs.LG | Machine-assisted treatment recommendations hold a promise to reduce physician
time and decision errors. We formulate the task as a sequence-to-sequence
prediction model that takes the entire time-ordered medical history as input,
and predicts a sequence of future clinical procedures and medications. It is
built on the ... | computer science |
7,569 | On the Generalization of Equivariance and Convolution in Neural Networks
to the Action of Compact Groups | stat.ML | Convolutional neural networks have been extremely successful in the image
recognition domain because they ensure equivariance to translations. There have
been many recent attempts to generalize this framework to other domains,
including graphs and data lying on manifolds. In this paper we give a rigorous,
theoretical t... | computer science |
7,570 | Nearly Optimal Adaptive Procedure for Piecewise-Stationary Bandit: a
Change-Point Detection Approach | stat.ML | Multi-armed bandit (MAB) is a class of online learning problems where a
learning agent aims to maximize its expected cumulative reward while repeatedly
selecting to pull arms with unknown reward distributions. In this paper, we
consider a scenario in which the arms' reward distributions may change in a
piecewise-statio... | computer science |
7,571 | PCA-Based Missing Information Imputation for Real-Time Crash Likelihood
Prediction Under Imbalanced Data | cs.LG | The real-time crash likelihood prediction has been an important research
topic. Various classifiers, such as support vector machine (SVM) and tree-based
boosting algorithms, have been proposed in traffic safety studies. However, few
research focuses on the missing data imputation in real-time crash likelihood
predictio... | computer science |
7,572 | Optimizing Neural Networks in the Equivalent Class Space | stat.ML | It has been widely observed that many activation functions and pooling
methods of neural network models have (positive-) rescaling-invariant property,
including ReLU, PReLU, max-pooling, and average pooling, which makes
fully-connected neural networks (FNNs) and convolutional neural networks (CNNs)
invariant to (positi... | computer science |
7,573 | On the Latent Space of Wasserstein Auto-Encoders | stat.ML | We study the role of latent space dimensionality in Wasserstein auto-encoders
(WAEs). Through experimentation on synthetic and real datasets, we argue that
random encoders should be preferred over deterministic encoders. We highlight
the potential of WAEs for representation learning with promising results on a
benchmar... | computer science |
7,574 | Distributed Stochastic Multi-Task Learning with Graph Regularization | stat.ML | We propose methods for distributed graph-based multi-task learning that are
based on weighted averaging of messages from other machines. Uniform averaging
or diminishing stepsize in these methods would yield consensus (single task)
learning. We show how simply skewing the averaging weights or controlling the
stepsize a... | computer science |
7,575 | Quadrature-based features for kernel approximation | cs.LG | We consider the problem of improving kernel approximation via randomized
feature maps. These maps arise as Monte Carlo approximation to integral
representations of kernel functions and scale up kernel methods for larger
datasets. We propose to use more efficient numerical integration technique to
obtain better estimate... | computer science |
7,576 | Random Hinge Forest for Differentiable Learning | stat.ML | We propose random hinge forests, a simple, efficient, and novel variant of
decision forests. Importantly, random hinge forests can be readily incorporated
as a general component within arbitrary computation graphs that are optimized
end-to-end with stochastic gradient descent or variants thereof. We derive
random hinge... | computer science |
7,577 | Consistent Individualized Feature Attribution for Tree Ensembles | cs.LG | Interpreting predictions from tree ensemble methods such as gradient boosting
machines and random forests is important, yet feature attribution for trees is
often heuristic and not individualized for each prediction. Here we show that
popular feature attribution methods are inconsistent, meaning they can lower a
featur... | computer science |
7,578 | Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal
KPIs in Web Applications | cs.LG | To ensure undisrupted business, large Internet companies need to closely
monitor various KPIs (e.g., Page Views, number of online users, and number of
orders) of its Web applications, to accurately detect anomalies and trigger
timely troubleshooting/mitigation. However, anomaly detection for these
seasonal KPIs with va... | computer science |
7,579 | Assessing the Utility of Weather Data for Photovoltaic Power Prediction | stat.ML | Photovoltaic systems have been widely deployed in recent times to meet the
increased electricity demand as an environmental-friendly energy source. The
major challenge for integrating photovoltaic systems in power systems is the
unpredictability of the solar power generated. In this paper, we analyze the
impact of havi... | computer science |
7,580 | Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor
Method for Extreme Multi-Label Classification | stat.ML | Machine learning has played an important role in information retrieval (IR)
in recent times. In search engines, for example, query keywords are accepted
and documents are returned in order of relevance to the given query; this can
be cast as a multi-label ranking problem in machine learning. Generally, the
number of ca... | computer science |
7,581 | Latent variable time-varying network inference | stat.ML | In many applications of finance, biology and sociology, complex systems
involve entities interacting with each other. These processes have the
peculiarity of evolving over time and of comprising latent factors, which
influence the system without being explicitly measured. In this work we present
latent variable time-va... | computer science |
7,582 | Efficient Bias-Span-Constrained Exploration-Exploitation in
Reinforcement Learning | cs.LG | We introduce SCAL, an algorithm designed to perform efficient
exploration-exploitation in any unknown weakly-communicating Markov Decision
Process (MDP) for which an upper bound c on the span of the optimal bias
function is known. For an MDP with S states, A actions and Gamma <= S possible
next states, we prove a regre... | computer science |
7,583 | Practical Evaluation and Optimization of Contextual Bandit Algorithms | stat.ML | We study and empirically optimize contextual bandit learning, exploration,
and problem encodings across 500+ datasets, creating a reference for
practitioners and discovering or reinforcing a number of natural open problems
for researchers. Across these experiments we show that minimizing the amount of
exploration is a ... | computer science |
7,584 | Predicting short-term Bitcoin price fluctuations from buy and sell
orders | stat.ML | Bitcoin is the first decentralized digital cryptocurrency, which has showed
significant market capitalization growth in last few years. It is important to
understand what drives the fluctuations of the Bitcoin exchange price and to
what extent they are predictable. In this paper, we study the ability to make
short-term... | computer science |
7,585 | client2vec: Towards Systematic Baselines for Banking Applications | stat.ML | The workflow of data scientists normally involves potentially inefficient
processes such as data mining, feature engineering and model selection. Recent
research has focused on automating this workflow, partly or in its entirety, to
improve productivity. We choose the former approach and in this paper share our
experie... | computer science |
7,586 | Fast Interactive Image Retrieval using large-scale unlabeled data | cs.LG | An interactive image retrieval system learns which images in the database
belong to a user's query concept, by analyzing the example images and feedback
provided by the user. The challenge is to retrieve the relevant images with
minimal user interaction. In this work, we propose to solve this problem by
posing it as a ... | computer science |
7,587 | Augment and Reduce: Stochastic Inference for Large Categorical
Distributions | stat.ML | Categorical distributions are ubiquitous in machine learning, e.g., in
classification, language models, and recommendation systems. They are also at
the core of discrete choice models. However, when the number of possible
outcomes is very large, using categorical distributions becomes computationally
expensive, as the ... | computer science |
7,588 | A Fast Proximal Point Method for Wasserstein Distance | stat.ML | Wasserstein distance plays increasingly important roles in machine learning,
stochastic programming and image processing. Major efforts have been under way
to address its high computational complexity, some leading to approximate or
regularized variations such as Sinkhorn distance. However, as we will
demonstrate, seve... | computer science |
7,589 | Stochastic quasi-Newton with adaptive step lengths for large-scale
problems | stat.ML | We provide a numerically robust and fast method capable of exploiting the
local geometry when solving large-scale stochastic optimisation problems. Our
key innovation is an auxiliary variable construction coupled with an inverse
Hessian approximation computed using a receding history of iterates and
gradients. It is th... | computer science |
7,590 | Hybrid Decision Making: When Interpretable Models Collaborate With
Black-Box Models | cs.LG | Interpretable machine learning models have received increasing interest in
recent years, especially in domains where humans are involved in the
decision-making process. However, the possible loss of the task performance for
gaining interpretability is often inevitable. This performance downgrade puts
practitioners in a... | computer science |
7,591 | On the Sample Complexity of Learning from a Sequence of Experiments | cs.LG | We analyze the sample complexity of a new problem: learning from a sequence
of experiments. In this problem, the learner should choose a hypothesis that
performs well with respect to an infinite sequence of experiments, and their
related data distributions. In practice, the learner can only perform m
experiments with a... | computer science |
7,592 | Few-Shot Learning with Metric-Agnostic Conditional Embeddings | cs.LG | Learning high quality class representations from few examples is a key
problem in metric-learning approaches to few-shot learning. To accomplish this,
we introduce a novel architecture where class representations are conditioned
for each few-shot trial based on a target image. We also deviate from
traditional metric-le... | computer science |
7,593 | Adversarially Regularized Graph Autoencoder | cs.LG | Graph embedding is an effective method to represent graph data in a low
dimensional space for graph analytics. Most existing embedding algorithms
typically focus on preserving the topological structure or minimizing the
reconstruction errors of graph data, but they have mostly ignored the data
distribution of the laten... | computer science |
7,594 | Towards Understanding the Generalization Bias of Two Layer Convolutional
Linear Classifiers with Gradient Descent | cs.LG | A major challenge in understanding the generalization of deep learning is to
explain why (stochastic) gradient descent can exploit the network architecture
to find solutions that have good generalization performance when using high
capacity models. We find simple but realistic examples showing that this
phenomenon exis... | computer science |
7,595 | Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic
Thresholding | cs.LG | As spacecraft send back increasing amounts of telemetry data, improved
anomaly detection systems are needed to lessen the monitoring burden placed on
operations engineers and reduce operational risk. Current spacecraft monitoring
systems only target a subset of anomaly types and often require costly expert
knowledge to... | computer science |
7,596 | Predicting Adversarial Examples with High Confidence | cs.LG | It has been suggested that adversarial examples cause deep learning models to
make incorrect predictions with high confidence. In this work, we take the
opposite stance: an overly confident model is more likely to be vulnerable to
adversarial examples. This work is one of the most proactive approaches taken
to date, as... | computer science |
7,597 | Legendre Tensor Decomposition | stat.ML | We present a novel nonnegative tensor decomposition method, called Legendre
decomposition, which factorizes an input tensor into a multiplicative
combination of parameters. Thanks to the well-developed theory of information
geometry, the reconstructed tensor is unique and always minimizes the KL
divergence from an inpu... | computer science |
7,598 | Tighter Variational Bounds are Not Necessarily Better | stat.ML | We provide theoretical and empirical evidence that using tighter evidence
lower bounds (ELBOs) can be detrimental to the process of learning an inference
network by reducing the signal-to-noise ratio of the gradient estimator. Our
results call into question common implicit assumptions that tighter ELBOs are
better vari... | computer science |
7,599 | First Order Generative Adversarial Networks | cs.LG | GANs excel at learning high dimensional distributions, but they can update
generator parameters in directions that do not correspond to the steepest
descent direction of the objective. Prominent examples of problematic update
directions include those used in both Goodfellow's original GAN and the
WGAN-GP. To formally d... | computer science |
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