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32,902 | Learn on Source, Refine on Target:A Model Transfer Learning Framework
with Random Forests | cs.LG | We propose novel model transfer-learning methods that refine a decision
forest model M learned within a "source" domain using a training set sampled
from a "target" domain, assumed to be a variation of the source. We present two
random forest transfer algorithms. The first algorithm searches greedily for
locally optima... | computer science |
32,903 | Stochastic Proximal Gradient Descent for Nuclear Norm Regularization | cs.LG | In this paper, we utilize stochastic optimization to reduce the space
complexity of convex composite optimization with a nuclear norm regularizer,
where the variable is a matrix of size $m \times n$. By constructing a low-rank
estimate of the gradient, we propose an iterative algorithm based on stochastic
proximal grad... | computer science |
32,904 | Discrete Rényi Classifiers | cs.LG | Consider the binary classification problem of predicting a target variable
$Y$ from a discrete feature vector $X = (X_1,...,X_d)$. When the probability
distribution $\mathbb{P}(X,Y)$ is known, the optimal classifier, leading to the
minimum misclassification rate, is given by the Maximum A-posteriori
Probability decisio... | computer science |
32,905 | Diffusion-Convolutional Neural Networks | cs.LG | We present diffusion-convolutional neural networks (DCNNs), a new model for
graph-structured data. Through the introduction of a diffusion-convolution
operation, we show how diffusion-based representations can be learned from
graph-structured data and used as an effective basis for node classification.
DCNNs have sever... | computer science |
32,906 | Evaluating Protein-protein Interaction Predictors with a Novel
3-Dimensional Metric | cs.LG | In order for the predicted interactions to be directly adopted by biologists,
the ma- chine learning predictions have to be of high precision, regardless of
recall. This aspect cannot be evaluated or numerically represented well by
traditional metrics like accuracy, ROC, or precision-recall curve. In this
work, we star... | computer science |
32,907 | Performance Analysis of Multiclass Support Vector Machine Classification
for Diagnosis of Coronary Heart Diseases | cs.LG | Automatic diagnosis of coronary heart disease helps the doctor to support in
decision making a diagnosis. Coronary heart disease have some types or levels.
Referring to the UCI Repository dataset, it divided into 4 types or levels that
are labeled numbers 1-4 (low, medium, high and serious). The diagnosis models
can be... | computer science |
32,908 | Max-Sum Diversification, Monotone Submodular Functions and Semi-metric
Spaces | cs.LG | In many applications such as web-based search, document summarization,
facility location and other applications, the results are preferable to be both
representative and diversified subsets of documents. The goal of this study is
to select a good "quality", bounded-size subset of a given set of items, while
maintaining... | computer science |
32,909 | Neighbourhood NILM: A Big-data Approach to Household Energy
Disaggregation | cs.LG | In this paper, we investigate whether "big-data" is more valuable than
"precise" data for the problem of energy disaggregation: the process of
breaking down aggregate energy usage on a per-appliance basis. Existing
techniques for disaggregation rely on energy metering at a resolution of 1
minute or higher, but most pow... | computer science |
32,910 | Efficient Construction of Local Parametric Reduced Order Models Using
Machine Learning Techniques | cs.LG | Reduced order models are computationally inexpensive approximations that
capture the important dynamical characteristics of large, high-fidelity
computer models of physical systems. This paper applies machine learning
techniques to improve the design of parametric reduced order models.
Specifically, machine learning is... | computer science |
32,911 | Learning with a Strong Adversary | cs.LG | The robustness of neural networks to intended perturbations has recently
attracted significant attention. In this paper, we propose a new method,
\emph{learning with a strong adversary}, that learns robust classifiers from
supervised data. The proposed method takes finding adversarial examples as an
intermediate step. ... | computer science |
32,912 | Label Efficient Learning by Exploiting Multi-class Output Codes | cs.LG | We present a new perspective on the popular multi-class algorithmic
techniques of one-vs-all and error correcting output codes. Rather than
studying the behavior of these techniques for supervised learning, we establish
a connection between the success of these methods and the existence of
label-efficient learning proc... | computer science |
32,913 | Learning to Diagnose with LSTM Recurrent Neural Networks | cs.LG | Clinical medical data, especially in the intensive care unit (ICU), consist
of multivariate time series of observations. For each patient visit (or
episode), sensor data and lab test results are recorded in the patient's
Electronic Health Record (EHR). While potentially containing a wealth of
insights, the data is diff... | computer science |
32,914 | Universum Prescription: Regularization using Unlabeled Data | cs.LG | This paper shows that simply prescribing "none of the above" labels to
unlabeled data has a beneficial regularization effect to supervised learning.
We call it universum prescription by the fact that the prescribed labels cannot
be one of the supervised labels. In spite of its simplicity, universum
prescription obtaine... | computer science |
32,915 | Sparse Learning for Large-scale and High-dimensional Data: A Randomized
Convex-concave Optimization Approach | cs.LG | In this paper, we develop a randomized algorithm and theory for learning a
sparse model from large-scale and high-dimensional data, which is usually
formulated as an empirical risk minimization problem with a sparsity-inducing
regularizer. Under the assumption that there exists a (approximately) sparse
solution with hi... | computer science |
32,916 | Deep Linear Discriminant Analysis | cs.LG | We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns
linearly separable latent representations in an end-to-end fashion. Classic LDA
extracts features which preserve class separability and is used for
dimensionality reduction for many classification problems. The central idea of
this paper is to put LD... | computer science |
32,917 | Large-Scale Approximate Kernel Canonical Correlation Analysis | cs.LG | Kernel canonical correlation analysis (KCCA) is a nonlinear multi-view
representation learning technique with broad applicability in statistics and
machine learning. Although there is a closed-form solution for the KCCA
objective, it involves solving an $N\times N$ eigenvalue system where $N$ is
the training set size, ... | computer science |
32,918 | Budget Online Multiple Kernel Learning | cs.LG | Online learning with multiple kernels has gained increasing interests in
recent years and found many applications. For classification tasks, Online
Multiple Kernel Classification (OMKC), which learns a kernel based classifier
by seeking the optimal linear combination of a pool of single kernel
classifiers in an online ... | computer science |
32,919 | Topic Modeling of Behavioral Modes Using Sensor Data | cs.LG | The field of Movement Ecology, like so many other fields, is experiencing a
period of rapid growth in availability of data. As the volume rises,
traditional methods are giving way to machine learning and data science, which
are playing an increasingly large part it turning this data into
science-driving insights. One r... | computer science |
32,920 | MuProp: Unbiased Backpropagation for Stochastic Neural Networks | cs.LG | Deep neural networks are powerful parametric models that can be trained
efficiently using the backpropagation algorithm. Stochastic neural networks
combine the power of large parametric functions with that of graphical models,
which makes it possible to learn very complex distributions. However, as
backpropagation is n... | computer science |
32,921 | Binary embeddings with structured hashed projections | cs.LG | We consider the hashing mechanism for constructing binary embeddings, that
involves pseudo-random projections followed by nonlinear (sign function)
mappings. The pseudo-random projection is described by a matrix, where not all
entries are independent random variables but instead a fixed "budget of
randomness" is distri... | computer science |
32,922 | Constant Time EXPected Similarity Estimation using Stochastic
Optimization | cs.LG | A new algorithm named EXPected Similarity Estimation (EXPoSE) was recently
proposed to solve the problem of large-scale anomaly detection. It is a
non-parametric and distribution free kernel method based on the Hilbert space
embedding of probability measures. Given a dataset of $n$ samples, EXPoSE needs
only $\mathcal{... | computer science |
32,923 | Net2Net: Accelerating Learning via Knowledge Transfer | cs.LG | We introduce techniques for rapidly transferring the information stored in
one neural net into another neural net. The main purpose is to accelerate the
training of a significantly larger neural net. During real-world workflows, one
often trains very many different neural networks during the experimentation and
design ... | computer science |
32,924 | Adversarial Autoencoders | cs.LG | In this paper, we propose the "adversarial autoencoder" (AAE), which is a
probabilistic autoencoder that uses the recently proposed generative
adversarial networks (GAN) to perform variational inference by matching the
aggregated posterior of the hidden code vector of the autoencoder with an
arbitrary prior distributio... | computer science |
32,925 | Why are deep nets reversible: A simple theory, with implications for
training | cs.LG | Generative models for deep learning are promising both to improve
understanding of the model, and yield training methods requiring fewer labeled
samples.
Recent works use generative model approaches to produce the deep net's input
given the value of a hidden layer several levels above. However, there is no
accompanyi... | computer science |
32,926 | Expressiveness of Rectifier Networks | cs.LG | Rectified Linear Units (ReLUs) have been shown to ameliorate the vanishing
gradient problem, allow for efficient backpropagation, and empirically promote
sparsity in the learned parameters. They have led to state-of-the-art results
in a variety of applications. However, unlike threshold and sigmoid networks,
ReLU netwo... | computer science |
32,927 | A Distribution Adaptive Framework for Prediction Interval Estimation
Using Nominal Variables | cs.LG | Proposed methods for prediction interval estimation so far focus on cases
where input variables are numerical. In datasets with solely nominal input
variables, we observe records with the exact same input $x^u$, but different
real valued outputs due to the inherent noise in the system. Existing
prediction interval esti... | computer science |
32,928 | Complex-Valued Gaussian Processes for Regression | cs.LG | In this paper we propose a novel Bayesian solution for nonlinear regression
in complex fields. Previous solutions for kernels methods usually assume a
complexification approach, where the real-valued kernel is replaced by a
complex-valued one. This approach is limited. Based on results in
complex-valued linear theory a... | computer science |
32,929 | Sparse learning of maximum likelihood model for optimization of complex
loss function | cs.LG | Traditional machine learning methods usually minimize a simple loss function
to learn a predictive model, and then use a complex performance measure to
measure the prediction performance. However, minimizing a simple loss function
cannot guarantee that an optimal performance. In this paper, we study the
problem of opti... | computer science |
32,930 | Metric learning approach for graph-based label propagation | cs.LG | The efficiency of graph-based semi-supervised algorithms depends on the graph
of instances on which they are applied. The instances are often in a vectorial
form before a graph linking them is built. The construction of the graph relies
on a metric over the vectorial space that help define the weight of the
connection ... | computer science |
32,931 | Seeding K-Means using Method of Moments | cs.LG | K-means is one of the most widely used algorithms for clustering in Data
Mining applications, which attempts to minimize the sum of the square of the
Euclidean distance of the points in the clusters from the respective means of
the clusters. However, K-means suffers from local minima problem and is not
guaranteed to co... | computer science |
32,932 | Doctor AI: Predicting Clinical Events via Recurrent Neural Networks | cs.LG | Leveraging large historical data in electronic health record (EHR), we
developed Doctor AI, a generic predictive model that covers observed medical
conditions and medication uses. Doctor AI is a temporal model using recurrent
neural networks (RNN) and was developed and applied to longitudinal time
stamped EHR data from... | computer science |
32,933 | Staleness-aware Async-SGD for Distributed Deep Learning | cs.LG | Deep neural networks have been shown to achieve state-of-the-art performance
in several machine learning tasks. Stochastic Gradient Descent (SGD) is the
preferred optimization algorithm for training these networks and asynchronous
SGD (ASGD) has been widely adopted for accelerating the training of large-scale
deep netw... | computer science |
32,934 | Prioritized Experience Replay | cs.LG | Experience replay lets online reinforcement learning agents remember and
reuse experiences from the past. In prior work, experience transitions were
uniformly sampled from a replay memory. However, this approach simply replays
transitions at the same frequency that they were originally experienced,
regardless of their ... | computer science |
32,935 | Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding
For Image & Text Retrieval | cs.LG | Joint modeling of language and vision has been drawing increasing interest. A
multimodal data representation allowing for bidirectional retrieval of images
by sentences and vice versa is a key aspect. In this paper we present three
contributions in canonical correlation analysis (CCA) based multimodal
retrieval. Firstl... | computer science |
32,936 | Policy Distillation | cs.LG | Policies for complex visual tasks have been successfully learned with deep
reinforcement learning, using an approach called deep Q-networks (DQN), but
relatively large (task-specific) networks and extensive training are needed to
achieve good performance. In this work, we present a novel method called policy
distillati... | computer science |
32,937 | Conditional Computation in Neural Networks for faster models | cs.LG | Deep learning has become the state-of-art tool in many applications, but the
evaluation and training of deep models can be time-consuming and
computationally expensive. The conditional computation approach has been
proposed to tackle this problem (Bengio et al., 2013; Davis & Arel, 2013). It
operates by selectively act... | computer science |
32,938 | Manifold Regularized Discriminative Neural Networks | cs.LG | Unregularized deep neural networks (DNNs) can be easily overfit with a
limited sample size. We argue that this is mostly due to the disriminative
nature of DNNs which directly model the conditional probability (or score) of
labels given the input. The ignorance of input distribution makes DNNs
difficult to generalize t... | computer science |
32,939 | Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its
Application to Inverse Problems | cs.LG | The sparsity of signals in a transform domain or dictionary has been
exploited in applications such as compression, denoising and inverse problems.
More recently, data-driven adaptation of synthesis dictionaries has shown
promise compared to analytical dictionary models. However, dictionary learning
problems are typica... | computer science |
32,940 | Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning | cs.LG | The ability to act in multiple environments and transfer previous knowledge
to new situations can be considered a critical aspect of any intelligent agent.
Towards this goal, we define a novel method of multitask and transfer learning
that enables an autonomous agent to learn how to behave in multiple tasks
simultaneou... | computer science |
32,941 | Fixed Point Quantization of Deep Convolutional Networks | cs.LG | In recent years increasingly complex architectures for deep convolution
networks (DCNs) have been proposed to boost the performance on image
recognition tasks. However, the gains in performance have come at a cost of
substantial increase in computation and model storage resources. Fixed point
implementation of DCNs has... | computer science |
32,942 | Denoising Criterion for Variational Auto-Encoding Framework | cs.LG | Denoising autoencoders (DAE) are trained to reconstruct their clean inputs
with noise injected at the input level, while variational autoencoders (VAE)
are trained with noise injected in their stochastic hidden layer, with a
regularizer that encourages this noise injection. In this paper, we show that
injecting noise b... | computer science |
32,943 | Training Deep Neural Networks via Direct Loss Minimization | cs.LG | Supervised training of deep neural nets typically relies on minimizing
cross-entropy. However, in many domains, we are interested in performing well
on metrics specific to the application. In this paper we propose a direct loss
minimization approach to train deep neural networks, which provably minimizes
the applicatio... | computer science |
32,944 | All you need is a good init | cs.LG | Layer-sequential unit-variance (LSUV) initialization - a simple method for
weight initialization for deep net learning - is proposed. The method consists
of the two steps. First, pre-initialize weights of each convolution or
inner-product layer with orthonormal matrices. Second, proceed from the first
to the final laye... | computer science |
32,945 | Deconstructing the Ladder Network Architecture | cs.LG | The Manual labeling of data is and will remain a costly endeavor. For this
reason, semi-supervised learning remains a topic of practical importance. The
recently proposed Ladder Network is one such approach that has proven to be
very successful. In addition to the supervised objective, the Ladder Network
also adds an u... | computer science |
32,946 | Blending LSTMs into CNNs | cs.LG | We consider whether deep convolutional networks (CNNs) can represent decision
functions with similar accuracy as recurrent networks such as LSTMs. First, we
show that a deep CNN with an architecture inspired by the models recently
introduced in image recognition can yield better accuracy than previous
convolutional and... | computer science |
32,947 | Comparative Study of Deep Learning Software Frameworks | cs.LG | Deep learning methods have resulted in significant performance improvements
in several application domains and as such several software frameworks have
been developed to facilitate their implementation. This paper presents a
comparative study of five deep learning frameworks, namely Caffe, Neon,
TensorFlow, Theano, and... | computer science |
32,948 | Towards Principled Unsupervised Learning | cs.LG | General unsupervised learning is a long-standing conceptual problem in
machine learning. Supervised learning is successful because it can be solved by
the minimization of the training error cost function. Unsupervised learning is
not as successful, because the unsupervised objective may be unrelated to the
supervised t... | computer science |
32,949 | Task Loss Estimation for Sequence Prediction | cs.LG | Often, the performance on a supervised machine learning task is evaluated
with a emph{task loss} function that cannot be optimized directly. Examples of
such loss functions include the classification error, the edit distance and the
BLEU score. A common workaround for this problem is to instead optimize a
emph{surrogat... | computer science |
32,950 | On the energy landscape of deep networks | cs.LG | We introduce "AnnealSGD", a regularized stochastic gradient descent algorithm
motivated by an analysis of the energy landscape of a particular class of deep
networks with sparse random weights. The loss function of such networks can be
approximated by the Hamiltonian of a spherical spin glass with Gaussian
coupling. Wh... | computer science |
32,951 | Dueling Network Architectures for Deep Reinforcement Learning | cs.LG | In recent years there have been many successes of using deep representations
in reinforcement learning. Still, many of these applications use conventional
architectures, such as convolutional networks, LSTMs, or auto-encoders. In this
paper, we present a new neural network architecture for model-free
reinforcement lear... | computer science |
32,952 | Data Representation and Compression Using Linear-Programming
Approximations | cs.LG | We propose `Dracula', a new framework for unsupervised feature selection from
sequential data such as text. Dracula learns a dictionary of $n$-grams that
efficiently compresses a given corpus and recursively compresses its own
dictionary; in effect, Dracula is a `deep' extension of Compressive Feature
Learning. It requ... | computer science |
32,953 | Modeling the Temporal Nature of Human Behavior for Demographics
Prediction | cs.LG | Mobile phone metadata is increasingly used for humanitarian purposes in
developing countries as traditional data is scarce. Basic demographic
information is however often absent from mobile phone datasets, limiting the
operational impact of the datasets. For these reasons, there has been a growing
interest in predictin... | computer science |
32,954 | Scalable Gradient-Based Tuning of Continuous Regularization
Hyperparameters | cs.LG | Hyperparameter selection generally relies on running multiple full training
trials, with selection based on validation set performance. We propose a
gradient-based approach for locally adjusting hyperparameters during training
of the model. Hyperparameters are adjusted so as to make the model parameter
gradients, and h... | computer science |
32,955 | Data-Dependent Path Normalization in Neural Networks | cs.LG | We propose a unified framework for neural net normalization, regularization
and optimization, which includes Path-SGD and Batch-Normalization and
interpolates between them across two different dimensions. Through this
framework we investigate issue of invariance of the optimization, data
dependence and the connection w... | computer science |
32,956 | Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and
Denoising Autoencoders | cs.LG | Two novel deep hybrid architectures, the Deep Hybrid Boltzmann Machine and
the Deep Hybrid Denoising Auto-encoder, are proposed for handling
semi-supervised learning problems. The models combine experts that model
relevant distributions at different levels of abstraction to improve overall
predictive performance on dis... | computer science |
32,957 | Multiple--Instance Learning: Christoffel Function Approach to
Distribution Regression Problem | cs.LG | A two--step Christoffel function based solution is proposed to distribution
regression problem. On the first step, to model distribution of observations
inside a bag, build Christoffel function for each bag of observations. Then, on
the second step, build outcome variable Christoffel function, but use the bag's
Christo... | computer science |
32,958 | On the Generalization Error Bounds of Neural Networks under
Diversity-Inducing Mutual Angular Regularization | cs.LG | Recently diversity-inducing regularization methods for latent variable models
(LVMs), which encourage the components in LVMs to be diverse, have been studied
to address several issues involved in latent variable modeling: (1) how to
capture long-tail patterns underlying data; (2) how to reduce model complexity
without ... | computer science |
32,959 | Cascading Denoising Auto-Encoder as a Deep Directed Generative Model | cs.LG | Recent work (Bengio et al., 2013) has shown howDenoising Auto-Encoders(DAE)
become gener-ative models as a density estimator. However,in practice, the
framework suffers from a mixingproblem in the MCMC sampling process and
nodirect method to estimate the test log-likelihood.We consider a directed
model with an stochas-... | computer science |
32,960 | Fast and Accurate Deep Network Learning by Exponential Linear Units
(ELUs) | cs.LG | We introduce the "exponential linear unit" (ELU) which speeds up learning in
deep neural networks and leads to higher classification accuracies. Like
rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs
(PReLUs), ELUs alleviate the vanishing gradient problem via the identity for
positive values. ... | computer science |
32,961 | Modular Autoencoders for Ensemble Feature Extraction | cs.LG | We introduce the concept of a Modular Autoencoder (MAE), capable of learning
a set of diverse but complementary representations from unlabelled data, that
can later be used for supervised tasks. The learning of the representations is
controlled by a trade off parameter, and we show on six benchmark datasets the
optimum... | computer science |
32,962 | Weak Convergence Properties of Constrained Emphatic Temporal-difference
Learning with Constant and Slowly Diminishing Stepsize | cs.LG | We consider the emphatic temporal-difference (TD) algorithm, ETD($\lambda$),
for learning the value functions of stationary policies in a discounted, finite
state and action Markov decision process. The ETD($\lambda$) algorithm was
recently proposed by Sutton, Mahmood, and White to solve a long-standing
divergence prob... | computer science |
32,963 | Temporal Convolutional Neural Networks for Diagnosis from Lab Tests | cs.LG | Early diagnosis of treatable diseases is essential for improving healthcare,
and many diseases' onsets are predictable from annual lab tests and their
temporal trends. We introduce a multi-resolution convolutional neural network
for early detection of multiple diseases from irregularly measured sparse lab
values. Our n... | computer science |
32,964 | Learning Halfspaces and Neural Networks with Random Initialization | cs.LG | We study non-convex empirical risk minimization for learning halfspaces and
neural networks. For loss functions that are $L$-Lipschitz continuous, we
present algorithms to learn halfspaces and multi-layer neural networks that
achieve arbitrarily small excess risk $\epsilon>0$. The time complexity is
polynomial in the i... | computer science |
32,965 | Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The
$\ell_0$ Method | cs.LG | The sparsity of natural signals and images in a transform domain or
dictionary has been extensively exploited in several applications such as
compression, denoising and inverse problems. More recently, data-driven
adaptation of synthesis dictionaries has shown promise in many applications
compared to fixed or analytica... | computer science |
32,966 | How do the naive Bayes classifier and the Support Vector Machine compare
in their ability to forecast the Stock Exchange of Thailand? | cs.LG | This essay investigates the question of how the naive Bayes classifier and
the support vector machine compare in their ability to forecast the Stock
Exchange of Thailand. The theory behind the SVM and the naive Bayes classifier
is explored. The algorithms are trained using data from the month of January
2010, extracted... | computer science |
32,967 | Multiple-Instance Learning: Radon-Nikodym Approach to Distribution
Regression Problem | cs.LG | For distribution regression problem, where a bag of $x$--observations is
mapped to a single $y$ value, a one--step solution is proposed. The problem of
random distribution to random value is transformed to random vector to random
value by taking distribution moments of $x$ observations in a bag as random
vector. Then R... | computer science |
32,968 | Scalable and Accurate Online Feature Selection for Big Data | cs.LG | Feature selection is important in many big data applications. Two critical
challenges closely associate with big data. Firstly, in many big data
applications, the dimensionality is extremely high, in millions, and keeps
growing. Secondly, big data applications call for highly scalable feature
selection algorithms in an... | computer science |
32,969 | Reinforcement Learning Applied to an Electric Water Heater: From Theory
to Practice | cs.LG | Electric water heaters have the ability to store energy in their water buffer
without impacting the comfort of the end user. This feature makes them a prime
candidate for residential demand response. However, the stochastic and
nonlinear dynamics of electric water heaters, makes it challenging to harness
their flexibil... | computer science |
32,970 | Centroid Based Binary Tree Structured SVM for Multi Classification | cs.LG | Support Vector Machines (SVMs) were primarily designed for 2-class
classification. But they have been extended for N-class classification also
based on the requirement of multiclasses in the practical applications.
Although N-class classification using SVM has considerable research attention,
getting minimum number of ... | computer science |
32,971 | State of the Art Control of Atari Games Using Shallow Reinforcement
Learning | cs.LG | The recently introduced Deep Q-Networks (DQN) algorithm has gained attention
as one of the first successful combinations of deep neural networks and
reinforcement learning. Its promise was demonstrated in the Arcade Learning
Environment (ALE), a challenging framework composed of dozens of Atari 2600
games used to evalu... | computer science |
32,972 | Deep Attention Recurrent Q-Network | cs.LG | A deep learning approach to reinforcement learning led to a general learner
able to train on visual input to play a variety of arcade games at the human
and superhuman levels. Its creators at the Google DeepMind's team called the
approach: Deep Q-Network (DQN). We present an extension of DQN by "soft" and
"hard" attent... | computer science |
32,973 | Similarity Learning via Adaptive Regression and Its Application to Image
Retrieval | cs.LG | We study the problem of similarity learning and its application to image
retrieval with large-scale data. The similarity between pairs of images can be
measured by the distances between their high dimensional representations, and
the problem of learning the appropriate similarity is often addressed by
distance metric l... | computer science |
32,974 | Rademacher Complexity of the Restricted Boltzmann Machine | cs.LG | Boltzmann machine, as a fundamental construction block of deep belief network
and deep Boltzmann machines, is widely used in deep learning community and
great success has been achieved. However, theoretical understanding of many
aspects of it is still far from clear. In this paper, we studied the Rademacher
complexity ... | computer science |
32,975 | Risk Minimization in Structured Prediction using Orbit Loss | cs.LG | We introduce a new surrogate loss function called orbit loss in the
structured prediction framework, which has good theoretical and practical
advantages. While the orbit loss is not convex, it has a simple analytical
gradient and a simple perceptron-like learning rule. We analyze the new loss
theoretically and state a ... | computer science |
32,976 | The Teaching Dimension of Linear Learners | cs.LG | Teaching dimension is a learning theoretic quantity that specifies the
minimum training set size to teach a target model to a learner. Previous
studies on teaching dimension focused on version-space learners which maintain
all hypotheses consistent with the training data, and cannot be applied to
modern machine learner... | computer science |
32,977 | Online Crowdsourcing | cs.LG | With the success of modern internet based platform, such as Amazon Mechanical
Turk, it is now normal to collect a large number of hand labeled samples from
non-experts. The Dawid- Skene algorithm, which is based on Expectation-
Maximization update, has been widely used for inferring the true labels from
noisy crowdsour... | computer science |
32,978 | Online Gradient Descent in Function Space | cs.LG | In many problems in machine learning and operations research, we need to
optimize a function whose input is a random variable or a probability density
function, i.e. to solve optimization problems in an infinite dimensional space.
On the other hand, online learning has the advantage of dealing with streaming
examples, ... | computer science |
32,979 | Gated networks: an inventory | cs.LG | Gated networks are networks that contain gating connections, in which the
outputs of at least two neurons are multiplied. Initially, gated networks were
used to learn relationships between two input sources, such as pixels from two
images. More recently, they have been applied to learning activity recognition
or multi-... | computer science |
32,980 | Active Distance-Based Clustering using K-medoids | cs.LG | k-medoids algorithm is a partitional, centroid-based clustering algorithm
which uses pairwise distances of data points and tries to directly decompose
the dataset with $n$ points into a set of $k$ disjoint clusters. However,
k-medoids itself requires all distances between data points that are not so
easy to get in many... | computer science |
32,981 | L1-Regularized Distributed Optimization: A Communication-Efficient
Primal-Dual Framework | cs.LG | Despite the importance of sparsity in many large-scale applications, there
are few methods for distributed optimization of sparsity-inducing objectives.
In this paper, we present a communication-efficient framework for
L1-regularized optimization in the distributed environment. By viewing
classical objectives in a more... | computer science |
32,982 | Automatic Incident Classification for Big Traffic Data by Adaptive
Boosting SVM | cs.LG | Modern cities experience heavy traffic flows and congestions regularly across
space and time. Monitoring traffic situations becomes an important challenge
for the Traffic Control and Surveillance Systems (TCSS). In advanced TCSS, it
is helpful to automatically detect and classify different traffic incidents
such as sev... | computer science |
32,983 | Memory-based control with recurrent neural networks | cs.LG | Partially observed control problems are a challenging aspect of reinforcement
learning. We extend two related, model-free algorithms for continuous control
-- deterministic policy gradient and stochastic value gradient -- to solve
partially observed domains using recurrent neural networks trained with
backpropagation t... | computer science |
32,984 | Semisupervised Autoencoder for Sentiment Analysis | cs.LG | In this paper, we investigate the usage of autoencoders in modeling textual
data. Traditional autoencoders suffer from at least two aspects: scalability
with the high dimensionality of vocabulary size and dealing with
task-irrelevant words. We address this problem by introducing supervision via
the loss function of aut... | computer science |
32,985 | Über die Klassifizierung von Knoten in dynamischen Netzwerken mit
Inhalt | cs.LG | This paper explains the DYCOS-Algorithm as it was introduced in by Aggarwal
and Li in 2011. It operates on graphs whichs nodes are partially labeled and
automatically adds missing labels to nodes. To do so, the DYCOS algorithm makes
use of the structure of the graph as well as content which is assigned to the
node. Agg... | computer science |
32,986 | Dropout Training of Matrix Factorization and Autoencoder for Link
Prediction in Sparse Graphs | cs.LG | Matrix factorization (MF) and Autoencoder (AE) are among the most successful
approaches of unsupervised learning. While MF based models have been
extensively exploited in the graph modeling and link prediction literature, the
AE family has not gained much attention. In this paper we investigate both MF
and AE's applica... | computer science |
32,987 | A Light Touch for Heavily Constrained SGD | cs.LG | Minimizing empirical risk subject to a set of constraints can be a useful
strategy for learning restricted classes of functions, such as monotonic
functions, submodular functions, classifiers that guarantee a certain class
label for some subset of examples, etc. However, these restrictions may result
in a very large nu... | computer science |
32,988 | Learning Games and Rademacher Observations Losses | cs.LG | It has recently been shown that supervised learning with the popular logistic
loss is equivalent to optimizing the exponential loss over sufficient
statistics about the class: Rademacher observations (rados). We first show that
this unexpected equivalence can actually be generalized to other example / rado
losses, with... | computer science |
32,989 | Successive Ray Refinement and Its Application to Coordinate Descent for
LASSO | cs.LG | Coordinate descent is one of the most popular approaches for solving Lasso
and its extensions due to its simplicity and efficiency. When applying
coordinate descent to solving Lasso, we update one coordinate at a time while
fixing the remaining coordinates. Such an update, which is usually easy to
compute, greedily dec... | computer science |
32,990 | Discriminative Subnetworks with Regularized Spectral Learning for
Global-state Network Data | cs.LG | Data mining practitioners are facing challenges from data with network
structure. In this paper, we address a specific class of global-state networks
which comprises of a set of network instances sharing a similar structure yet
having different values at local nodes. Each instance is associated with a
global state whic... | computer science |
32,991 | Behavioral Modeling for Churn Prediction: Early Indicators and Accurate
Predictors of Custom Defection and Loyalty | cs.LG | Churn prediction, or the task of identifying customers who are likely to
discontinue use of a service, is an important and lucrative concern of firms in
many different industries. As these firms collect an increasing amount of
large-scale, heterogeneous data on the characteristics and behaviors of
customers, new method... | computer science |
32,992 | Predicting the Co-Evolution of Event and Knowledge Graphs | cs.LG | Embedding learning, a.k.a. representation learning, has been shown to be able
to model large-scale semantic knowledge graphs. A key concept is a mapping of
the knowledge graph to a tensor representation whose entries are predicted by
models using latent representations of generalized entities. Knowledge graphs
are typi... | computer science |
32,993 | A C++ library for Multimodal Deep Learning | cs.LG | MDL, Multimodal Deep Learning Library, is a deep learning framework that
supports multiple models, and this document explains its philosophy and
functionality. MDL runs on Linux, Mac, and Unix platforms. It depends on
OpenCV. | computer science |
32,994 | Move from Perturbed scheme to exponential weighting average | cs.LG | In an online decision problem, one makes decisions often with a pool of
decision sequence called experts but without knowledge of the future. After
each step, one pays a cost based on the decision and observed rate. One
reasonal goal would be to perform as well as the best expert in the pool. The
modern and well-known ... | computer science |
32,995 | Fast Parallel SVM using Data Augmentation | cs.LG | As one of the most popular classifiers, linear SVMs still have challenges in
dealing with very large-scale problems, even though linear or sub-linear
algorithms have been developed recently on single machines. Parallel computing
methods have been developed for learning large-scale SVMs. However, existing
methods rely o... | computer science |
32,996 | Context-Based Prediction of App Usage | cs.LG | There are around a hundred installed apps on an average smartphone. The high
number of apps and the limited number of app icons that can be displayed on the
device's screen requires a new paradigm to address their visibility to the
user. In this paper we propose a new online algorithm for dynamically
predicting a set o... | computer science |
32,997 | An unsupervised spatiotemporal graphical modeling approach to anomaly
detection in distributed CPS | cs.LG | Modern distributed cyber-physical systems (CPSs) encounter a large variety of
physical faults and cyber anomalies and in many cases, they are vulnerable to
catastrophic fault propagation scenarios due to strong connectivity among the
sub-systems. This paper presents a new data-driven framework for system-wide
anomaly d... | computer science |
32,998 | The Utility of Abstaining in Binary Classification | cs.LG | We explore the problem of binary classification in machine learning, with a
twist - the classifier is allowed to abstain on any datum, professing ignorance
about the true class label without committing to any prediction. This is
directly motivated by applications like medical diagnosis and fraud risk
assessment, in whi... | computer science |
32,999 | Electricity Demand Forecasting by Multi-Task Learning | cs.LG | We explore the application of kernel-based multi-task learning techniques to
forecast the demand of electricity in multiple nodes of a distribution network.
We show that recently developed output kernel learning techniques are
particularly well suited to solve this problem, as they allow to flexibly model
the complex s... | computer science |
33,000 | High performance ultra-low-precision convolutions on mobile devices | cs.LG | Many applications of mobile deep learning, especially real-time computer
vision workloads, are constrained by computation power. This is particularly
true for workloads running on older consumer phones, where a typical device
might be powered by a single- or dual-core ARMv7 CPU. We provide an open-source
implementation... | computer science |
33,001 | HyperPower: Power- and Memory-Constrained Hyper-Parameter Optimization
for Neural Networks | cs.LG | While selecting the hyper-parameters of Neural Networks (NNs) has been so far
treated as an art, the emergence of more complex, deeper architectures poses
increasingly more challenges to designers and Machine Learning (ML)
practitioners, especially when power and memory constraints need to be
considered. In this work, ... | computer science |
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