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6,300 | Reparameterization trick for discrete variables | stat.ML | Low-variance gradient estimation is crucial for learning directed graphical
models parameterized by neural networks, where the reparameterization trick is
widely used for those with continuous variables. While this technique gives
low-variance gradient estimates, it has not been directly applicable to
discrete variable... | computer science |
6,301 | Topology and Geometry of Half-Rectified Network Optimization | stat.ML | The loss surface of deep neural networks has recently attracted interest in
the optimization and machine learning communities as a prime example of
high-dimensional non-convex problem. Some insights were recently gained using
spin glass models and mean-field approximations, but at the expense of strongly
simplifying th... | computer science |
6,302 | Classification with Ultrahigh-Dimensional Features | stat.ML | Although much progress has been made in classification with high-dimensional
features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014},
classification with ultrahigh-dimensional features, wherein the features much
outnumber the sample size, defies most existing work. This paper introduces a
novel and computation... | computer science |
6,303 | Class-prior Estimation for Learning from Positive and Unlabeled Data | cs.LG | We consider the problem of estimating the class prior in an unlabeled
dataset. Under the assumption that an additional labeled dataset is available,
the class prior can be estimated by fitting a mixture of class-wise data
distributions to the unlabeled data distribution. However, in practice, such an
additional labeled... | computer science |
6,304 | Learning to Play in a Day: Faster Deep Reinforcement Learning by
Optimality Tightening | cs.LG | We propose a novel training algorithm for reinforcement learning which
combines the strength of deep Q-learning with a constrained optimization
approach to tighten optimality and encourage faster reward propagation. Our
novel technique makes deep reinforcement learning more practical by drastically
reducing the trainin... | computer science |
6,305 | Learning to Draw Samples: With Application to Amortized MLE for
Generative Adversarial Learning | stat.ML | We propose a simple algorithm to train stochastic neural networks to draw
samples from given target distributions for probabilistic inference. Our method
is based on iteratively adjusting the neural network parameters so that the
output changes along a Stein variational gradient that maximumly decreases the
KL divergen... | computer science |
6,306 | Entropy-SGD: Biasing Gradient Descent Into Wide Valleys | cs.LG | This paper proposes a new optimization algorithm called Entropy-SGD for
training deep neural networks that is motivated by the local geometry of the
energy landscape. Local extrema with low generalization error have a large
proportion of almost-zero eigenvalues in the Hessian with very few positive or
negative eigenval... | computer science |
6,307 | Joint Multimodal Learning with Deep Generative Models | stat.ML | We investigate deep generative models that can exchange multiple modalities
bi-directionally, e.g., generating images from corresponding texts and vice
versa. Recently, some studies handle multiple modalities on deep generative
models, such as variational autoencoders (VAEs). However, these models
typically assume that... | computer science |
6,308 | Linear Convergence of SVRG in Statistical Estimation | stat.ML | SVRG and its variants are among the state of art optimization algorithms for
large scale machine learning problems. It is well known that SVRG converges
linearly when the objective function is strongly convex. However this setup can
be restrictive, and does not include several important formulations such as
Lasso, grou... | computer science |
6,309 | One Class Splitting Criteria for Random Forests | stat.ML | Random Forests (RFs) are strong machine learning tools for classification and
regression. However, they remain supervised algorithms, and no extension of RFs
to the one-class setting has been proposed, except for techniques based on
second-class sampling. This work fills this gap by proposing a natural
methodology to e... | computer science |
6,310 | Does Distributionally Robust Supervised Learning Give Robust
Classifiers? | stat.ML | Distributionally Robust Supervised Learning (DRSL) is necessary for building
reliable machine learning systems. When machine learning is deployed in the
real world, its performance can be significantly degraded because test data may
follow a different distribution from training data. Previous DRSL explicitly
considers ... | computer science |
6,311 | Unrolled Generative Adversarial Networks | cs.LG | We introduce a method to stabilize Generative Adversarial Networks (GANs) by
defining the generator objective with respect to an unrolled optimization of
the discriminator. This allows training to be adjusted between using the
optimal discriminator in the generator's objective, which is ideal but
infeasible in practice... | computer science |
6,312 | Using Social Dynamics to Make Individual Predictions: Variational
Inference with a Stochastic Kinetic Model | stat.ML | Social dynamics is concerned primarily with interactions among individuals
and the resulting group behaviors, modeling the temporal evolution of social
systems via the interactions of individuals within these systems. In
particular, the availability of large-scale data from social networks and
sensor networks offers an... | computer science |
6,313 | Minimax-optimal semi-supervised regression on unknown manifolds | stat.ML | We consider semi-supervised regression when the predictor variables are drawn
from an unknown manifold. A simple two step approach to this problem is to: (i)
estimate the manifold geodesic distance between any pair of points using both
the labeled and unlabeled instances; and (ii) apply a k nearest neighbor
regressor b... | computer science |
6,314 | Learning Time Series Detection Models from Temporally Imprecise Labels | stat.ML | In this paper, we consider a new low-quality label learning problem: learning
time series detection models from temporally imprecise labels. In this problem,
the data consist of a set of input time series, and supervision is provided by
a sequence of noisy time stamps corresponding to the occurrence of positive
class e... | computer science |
6,315 | NonSTOP: A NonSTationary Online Prediction Method for Time Series | stat.ML | We present online prediction methods for univariate and multivariate time
series that allow us to handle nonstationary artifacts present in most real
time series. Specifically, we show that applying appropriate transformations to
such time series can lead to improved theoretical and empirical prediction
performance. Mo... | computer science |
6,316 | Divide and Conquer Networks | cs.LG | We consider the learning of algorithmic tasks by mere observation of
input-output pairs. Rather than studying this as a black-box discrete
regression problem with no assumption whatsoever on the input-output mapping,
we concentrate on tasks that are amenable to the principle of divide and
conquer, and study what are it... | computer science |
6,317 | Variational Lossy Autoencoder | cs.LG | Representation learning seeks to expose certain aspects of observed data in a
learned representation that's amenable to downstream tasks like classification.
For instance, a good representation for 2D images might be one that describes
only global structure and discards information about detailed texture. In this
paper... | computer science |
6,318 | Diverse Neural Network Learns True Target Functions | cs.LG | Neural networks are a powerful class of functions that can be trained with
simple gradient descent to achieve state-of-the-art performance on a variety of
applications. Despite their practical success, there is a paucity of results
that provide theoretical guarantees on why they are so effective. Lying in the
center of... | computer science |
6,319 | Low Data Drug Discovery with One-shot Learning | cs.LG | Recent advances in machine learning have made significant contributions to
drug discovery. Deep neural networks in particular have been demonstrated to
provide significant boosts in predictive power when inferring the properties
and activities of small-molecule compounds. However, the applicability of these
techniques ... | computer science |
6,320 | Policy Search with High-Dimensional Context Variables | stat.ML | Direct contextual policy search methods learn to improve policy parameters
and simultaneously generalize these parameters to different context or task
variables. However, learning from high-dimensional context variables, such as
camera images, is still a prominent problem in many real-world tasks. A naive
application o... | computer science |
6,321 | Disentangling factors of variation in deep representations using
adversarial training | cs.LG | We introduce a conditional generative model for learning to disentangle the
hidden factors of variation within a set of labeled observations, and separate
them into complementary codes. One code summarizes the specified factors of
variation associated with the labels. The other summarizes the remaining
unspecified vari... | computer science |
6,322 | Multi-Task Multiple Kernel Relationship Learning | stat.ML | This paper presents a novel multitask multiple kernel learning framework that
efficiently learns the kernel weights leveraging the relationship across
multiple tasks. The idea is to automatically infer this task relationship in
the \textit{RKHS} space corresponding to the given base kernels. The problem is
formulated a... | computer science |
6,323 | Simple and Efficient Parallelization for Probabilistic Temporal Tensor
Factorization | stat.ML | Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm
to model the temporal tensor data. It leverages a time constraint to capture
the evolving properties of tensor data. Nowadays the exploding dataset demands
a large scale PTTF analysis, and a parallel solution is critical to accommodate
the tre... | computer science |
6,324 | Tricks from Deep Learning | cs.LG | The deep learning community has devised a diverse set of methods to make
gradient optimization, using large datasets, of large and highly complex models
with deeply cascaded nonlinearities, practical. Taken as a whole, these methods
constitute a breakthrough, allowing computational structures which are quite
wide, very... | computer science |
6,325 | Learning to Learn without Gradient Descent by Gradient Descent | stat.ML | We learn recurrent neural network optimizers trained on simple synthetic
functions by gradient descent. We show that these learned optimizers exhibit a
remarkable degree of transfer in that they can be used to efficiently optimize
a broad range of derivative-free black-box functions, including Gaussian
process bandits,... | computer science |
6,326 | Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM | stat.ML | Restricted Boltzmann Machine (RBM) is a bipartite graphical model that is
used as the building block in energy-based deep generative models. Due to
numerical stability and quantifiability of the likelihood, RBM is commonly used
with Bernoulli units. Here, we consider an alternative member of exponential
family RBM with... | computer science |
6,327 | Low Latency Anomaly Detection and Bayesian Network Prediction of Anomaly
Likelihood | cs.LG | We develop a supervised machine learning model that detects anomalies in
systems in real time. Our model processes unbounded streams of data into time
series which then form the basis of a low-latency anomaly detection model.
Moreover, we extend our preliminary goal of just anomaly detection to
simultaneous anomaly pre... | computer science |
6,328 | Dual Teaching: A Practical Semi-supervised Wrapper Method | cs.LG | Semi-supervised wrapper methods are concerned with building effective
supervised classifiers from partially labeled data. Though previous works have
succeeded in some fields, it is still difficult to apply semi-supervised
wrapper methods to practice because the assumptions those methods rely on tend
to be unrealistic i... | computer science |
6,329 | GANS for Sequences of Discrete Elements with the Gumbel-softmax
Distribution | stat.ML | Generative Adversarial Networks (GAN) have limitations when the goal is to
generate sequences of discrete elements. The reason for this is that samples
from a distribution on discrete objects such as the multinomial are not
differentiable with respect to the distribution parameters. This problem can be
avoided by using... | computer science |
6,330 | Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly
Accelerated Dynamic Imaging | stat.ML | Sparsity-based approaches have been popular in many applications in image
processing and imaging. Compressed sensing exploits the sparsity of images in a
transform domain or dictionary to improve image recovery from undersampled
measurements. In the context of inverse problems in dynamic imaging, recent
research has de... | computer science |
6,331 | Accelerated Variance Reduced Block Coordinate Descent | stat.ML | Algorithms with fast convergence, small number of data access, and low
per-iteration complexity are particularly favorable in the big data era, due to
the demand for obtaining \emph{highly accurate solutions} to problems with
\emph{a large number of samples} in \emph{ultra-high} dimensional space.
Existing algorithms l... | computer science |
6,332 | Realistic risk-mitigating recommendations via inverse classification | cs.LG | Inverse classification, the process of making meaningful perturbations to a
test point such that it is more likely to have a desired classification, has
previously been addressed using data from a single static point in time. Such
an approach yields inflated probability estimates, stemming from an implicitly
made assum... | computer science |
6,333 | Preference Completion from Partial Rankings | stat.ML | We propose a novel and efficient algorithm for the collaborative preference
completion problem, which involves jointly estimating individualized rankings
for a set of entities over a shared set of items, based on a limited number of
observed affinity values. Our approach exploits the observation that while
preferences ... | computer science |
6,334 | Post Training in Deep Learning with Last Kernel | stat.ML | One of the main challenges of deep learning methods is the choice of an
appropriate training strategy. In particular, additional steps, such as
unsupervised pre-training, have been shown to greatly improve the performances
of deep structures. In this article, we propose an extra training step, called
post-training, whi... | computer science |
6,335 | Normalizing the Normalizers: Comparing and Extending Network
Normalization Schemes | cs.LG | Normalization techniques have only recently begun to be exploited in
supervised learning tasks. Batch normalization exploits mini-batch statistics
to normalize the activations. This was shown to speed up training and result in
better models. However its success has been very limited when dealing with
recurrent neural n... | computer science |
6,336 | Splitting matters: how monotone transformation of predictor variables
may improve the predictions of decision tree models | stat.ML | It is widely believed that the prediction accuracy of decision tree models is
invariant under any strictly monotone transformation of the individual
predictor variables. However, this statement may be false when predicting new
observations with values that were not seen in the training-set and are close
to the location... | computer science |
6,337 | Unsupervised Learning with Truncated Gaussian Graphical Models | stat.ML | Gaussian graphical models (GGMs) are widely used for statistical modeling,
because of ease of inference and the ubiquitous use of the normal distribution
in practical approximations. However, they are also known for their limited
modeling abilities, due to the Gaussian assumption. In this paper, we introduce
a novel va... | computer science |
6,338 | Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box
Models | cs.LG | Predictive models are increasingly deployed for the purpose of determining
access to services such as credit, insurance, and employment. Despite potential
gains in productivity and efficiency, several potential problems have yet to be
addressed, particularly the potential for unintentional discrimination. We
present an... | computer science |
6,339 | Machine Learning Approach for Skill Evaluation in Robotic-Assisted
Surgery | cs.LG | Evaluating surgeon skill has predominantly been a subjective task.
Development of objective methods for surgical skill assessment are of increased
interest. Recently, with technological advances such as robotic-assisted
minimally invasive surgery (RMIS), new opportunities for objective and
automated assessment framewor... | computer science |
6,340 | A Semi-Markov Switching Linear Gaussian Model for Censored Physiological
Data | cs.LG | Critically ill patients in regular wards are vulnerable to unanticipated
clinical dete- rioration which requires timely transfer to the intensive care
unit (ICU). To allow for risk scoring and patient monitoring in such a setting,
we develop a novel Semi- Markov Switching Linear Gaussian Model (SSLGM) for the
inpatient... | computer science |
6,341 | Net-Trim: Convex Pruning of Deep Neural Networks with Performance
Guarantee | cs.LG | We introduce and analyze a new technique for model reduction for deep neural
networks. While large networks are theoretically capable of learning
arbitrarily complex models, overfitting and model redundancy negatively affects
the prediction accuracy and model variance. Our Net-Trim algorithm prunes
(sparsifies) a train... | computer science |
6,342 | Graph Learning from Data under Structural and Laplacian Constraints | cs.LG | Graphs are fundamental mathematical structures used in various fields to
represent data, signals and processes. In this paper, we propose a novel
framework for learning/estimating graphs from data. The proposed framework
includes (i) formulation of various graph learning problems, (ii) their
probabilistic interpretatio... | computer science |
6,343 | Spectral Convolution Networks | cs.LG | Previous research has shown that computation of convolution in the frequency
domain provides a significant speedup versus traditional convolution network
implementations. However, this performance increase comes at the expense of
repeatedly computing the transform and its inverse in order to apply other
network operati... | computer science |
6,344 | The ZipML Framework for Training Models with End-to-End Low Precision:
The Cans, the Cannots, and a Little Bit of Deep Learning | cs.LG | Recently there has been significant interest in training machine-learning
models at low precision: by reducing precision, one can reduce computation and
communication by one order of magnitude. We examine training at reduced
precision, both from a theoretical and practical perspective, and ask: is it
possible to train ... | computer science |
6,345 | Boosting Variational Inference | stat.ML | Variational inference (VI) provides fast approximations of a Bayesian
posterior in part because it formulates posterior approximation as an
optimization problem: to find the closest distribution to the exact posterior
over some family of distributions. For practical reasons, the family of
distributions in VI is usually... | computer science |
6,346 | GENESIM: genetic extraction of a single, interpretable model | stat.ML | Models obtained by decision tree induction techniques excel in being
interpretable.However, they can be prone to overfitting, which results in a low
predictive performance. Ensemble techniques are able to achieve a higher
accuracy. However, this comes at a cost of losing interpretability of the
resulting model. This ma... | computer science |
6,347 | Unimodal Thompson Sampling for Graph-Structured Arms | cs.LG | We study, to the best of our knowledge, the first Bayesian algorithm for
unimodal Multi-Armed Bandit (MAB) problems with graph structure. In this
setting, each arm corresponds to a node of a graph and each edge provides a
relationship, unknown to the learner, between two nodes in terms of expected
reward. Furthermore, ... | computer science |
6,348 | A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer
Survival | cs.LG | Cancer survival prediction is an active area of research that can help
prevent unnecessary therapies and improve patient's quality of life. Gene
expression profiling is being widely used in cancer studies to discover
informative biomarkers that aid predict different clinical endpoint prediction.
We use multiple modalit... | computer science |
6,349 | "Influence Sketching": Finding Influential Samples In Large-Scale
Regressions | stat.ML | There is an especially strong need in modern large-scale data analysis to
prioritize samples for manual inspection. For example, the inspection could
target important mislabeled samples or key vulnerabilities exploitable by an
adversarial attack. In order to solve the "needle in the haystack" problem of
which samples t... | computer science |
6,350 | Increasing the Interpretability of Recurrent Neural Networks Using
Hidden Markov Models | stat.ML | As deep neural networks continue to revolutionize various application
domains, there is increasing interest in making these powerful models more
understandable and interpretable, and narrowing down the causes of good and bad
predictions. We focus on recurrent neural networks, state of the art models in
speech recogniti... | computer science |
6,351 | A Generalized Stochastic Variational Bayesian Hyperparameter Learning
Framework for Sparse Spectrum Gaussian Process Regression | stat.ML | While much research effort has been dedicated to scaling up sparse Gaussian
process (GP) models based on inducing variables for big data, little attention
is afforded to the other less explored class of low-rank GP approximations that
exploit the sparse spectral representation of a GP kernel. This paper presents
such a... | computer science |
6,352 | Conservative Contextual Linear Bandits | stat.ML | Safety is a desirable property that can immensely increase the applicability
of learning algorithms in real-world decision-making problems. It is much
easier for a company to deploy an algorithm that is safe, i.e., guaranteed to
perform at least as well as a baseline. In this paper, we study the issue of
safety in cont... | computer science |
6,353 | Pruning Convolutional Neural Networks for Resource Efficient Inference | cs.LG | We propose a new formulation for pruning convolutional kernels in neural
networks to enable efficient inference. We interleave greedy criteria-based
pruning with fine-tuning by backpropagation - a computationally efficient
procedure that maintains good generalization in the pruned network. We propose
a new criterion ba... | computer science |
6,354 | Dealing with Range Anxiety in Mean Estimation via Statistical Queries | cs.LG | We give algorithms for estimating the expectation of a given real-valued
function $\phi:X\to {\bf R}$ on a sample drawn randomly from some unknown
distribution $D$ over domain $X$, namely ${\bf E}_{{\bf x}\sim D}[\phi({\bf
x})]$. Our algorithms work in two well-studied models of restricted access to
data samples. The f... | computer science |
6,355 | Linear Thompson Sampling Revisited | stat.ML | We derive an alternative proof for the regret of Thompson sampling (\ts) in
the stochastic linear bandit setting. While we obtain a regret bound of order
$\widetilde{O}(d^{3/2}\sqrt{T})$ as in previous results, the proof sheds new
light on the functioning of the \ts. We leverage on the structure of the
problem to show ... | computer science |
6,356 | Scalable Adaptive Stochastic Optimization Using Random Projections | stat.ML | Adaptive stochastic gradient methods such as AdaGrad have gained popularity
in particular for training deep neural networks. The most commonly used and
studied variant maintains a diagonal matrix approximation to second order
information by accumulating past gradients which are used to tune the step size
adaptively. In... | computer science |
6,357 | Probabilistic structure discovery in time series data | stat.ML | Existing methods for structure discovery in time series data construct
interpretable, compositional kernels for Gaussian process regression models.
While the learned Gaussian process model provides posterior mean and variance
estimates, typically the structure is learned via a greedy optimization
procedure. This restri... | computer science |
6,358 | Spatial contrasting for deep unsupervised learning | stat.ML | Convolutional networks have marked their place over the last few years as the
best performing model for various visual tasks. They are, however, most suited
for supervised learning from large amounts of labeled data. Previous attempts
have been made to use unlabeled data to improve model performance by applying
unsuper... | computer science |
6,359 | GRAM: Graph-based Attention Model for Healthcare Representation Learning | cs.LG | Deep learning methods exhibit promising performance for predictive modeling
in healthcare, but two important challenges remain: -Data insufficiency:Often
in healthcare predictive modeling, the sample size is insufficient for deep
learning methods to achieve satisfactory results. -Interpretation:The
representations lear... | computer science |
6,360 | Structured Prediction by Conditional Risk Minimization | stat.ML | We propose a general approach for supervised learning with structured output
spaces, such as combinatorial and polyhedral sets, that is based on minimizing
estimated conditional risk functions. Given a loss function defined over pairs
of output labels, we first estimate the conditional risk function by solving a
(possi... | computer science |
6,361 | Tree Space Prototypes: Another Look at Making Tree Ensembles
Interpretable | stat.ML | Ensembles of decision trees have good prediction accuracy but suffer from a
lack of interpretability. We propose a new approach for interpreting tree
ensembles by finding prototypes in tree space, utilizing the naturally-learned
similarity measure from the tree ensemble. Demonstrating the method on random
forests, we s... | computer science |
6,362 | Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery | stat.ML | Recurrent neural networks (RNNs) are powerful and effective for processing
sequential data. However, RNNs are usually considered "black box" models whose
internal structure and learned parameters are not interpretable. In this paper,
we propose an interpretable RNN based on the sequential iterative
soft-thresholding al... | computer science |
6,363 | Investigating the influence of noise and distractors on the
interpretation of neural networks | stat.ML | Understanding neural networks is becoming increasingly important. Over the
last few years different types of visualisation and explanation methods have
been proposed. However, none of them explicitly considered the behaviour in the
presence of noise and distracting elements. In this work, we will show how
noise and dis... | computer science |
6,364 | Variational Graph Auto-Encoders | stat.ML | We introduce the variational graph auto-encoder (VGAE), a framework for
unsupervised learning on graph-structured data based on the variational
auto-encoder (VAE). This model makes use of latent variables and is capable of
learning interpretable latent representations for undirected graphs. We
demonstrate this model us... | computer science |
6,365 | TreeView: Peeking into Deep Neural Networks Via Feature-Space
Partitioning | stat.ML | With the advent of highly predictive but opaque deep learning models, it has
become more important than ever to understand and explain the predictions of
such models. Existing approaches define interpretability as the inverse of
complexity and achieve interpretability at the cost of accuracy. This
introduces a risk of ... | computer science |
6,366 | Interpretation of Prediction Models Using the Input Gradient | stat.ML | State of the art machine learning algorithms are highly optimized to provide
the optimal prediction possible, naturally resulting in complex models. While
these models often outperform simpler more interpretable models by order of
magnitudes, in terms of understanding the way the model functions, we are often
facing a ... | computer science |
6,367 | Infinite Variational Autoencoder for Semi-Supervised Learning | cs.LG | This paper presents an infinite variational autoencoder (VAE) whose capacity
adapts to suit the input data. This is achieved using a mixture model where the
mixing coefficients are modeled by a Dirichlet process, allowing us to
integrate over the coefficients when performing inference. Critically, this
then allows us t... | computer science |
6,368 | Interpreting the Predictions of Complex ML Models by Layer-wise
Relevance Propagation | stat.ML | Complex nonlinear models such as deep neural network (DNNs) have become an
important tool for image classification, speech recognition, natural language
processing, and many other fields of application. These models however lack
transparency due to their complex nonlinear structure and to the complex data
distributions... | computer science |
6,369 | Fast Orthonormal Sparsifying Transforms Based on Householder Reflectors | cs.LG | Dictionary learning is the task of determining a data-dependent transform
that yields a sparse representation of some observed data. The dictionary
learning problem is non-convex, and usually solved via computationally complex
iterative algorithms. Furthermore, the resulting transforms obtained generally
lack structure... | computer science |
6,370 | Identifying Significant Predictive Bias in Classifiers | stat.ML | We present a novel subset scan method to detect if a probabilistic binary
classifier has statistically significant bias -- over or under predicting the
risk -- for some subgroup, and identify the characteristics of this subgroup.
This form of model checking and goodness-of-fit test provides a way to
interpretably detec... | computer science |
6,371 | An Overview on Data Representation Learning: From Traditional Feature
Learning to Recent Deep Learning | cs.LG | Since about 100 years ago, to learn the intrinsic structure of data, many
representation learning approaches have been proposed, including both linear
ones and nonlinear ones, supervised ones and unsupervised ones. Particularly,
deep architectures are widely applied for representation learning in recent
years, and have... | computer science |
6,372 | Distributed Optimization of Multi-Class SVMs | stat.ML | Training of one-vs.-rest SVMs can be parallelized over the number of classes
in a straight forward way. Given enough computational resources, one-vs.-rest
SVMs can thus be trained on data involving a large number of classes. The same
cannot be stated, however, for the so-called all-in-one SVMs, which require
solving a ... | computer science |
6,373 | Bottleneck Conditional Density Estimation | stat.ML | We introduce a new framework for training deep generative models for
high-dimensional conditional density estimation. The Bottleneck Conditional
Density Estimator (BCDE) is a variant of the conditional variational
autoencoder (CVAE) that employs layer(s) of stochastic variables as the
bottleneck between the input $x$ a... | computer science |
6,374 | A Benchmark and Comparison of Active Learning for Logistic Regression | stat.ML | Various active learning methods based on logistic regression have been
proposed. In this paper, we investigate seven state-of-the-art strategies,
present an extensive benchmark, and provide a better understanding of their
underlying characteristics. Experiments are carried out both on 3 synthetic
datasets and 43 real-w... | computer science |
6,375 | Should I use TensorFlow | cs.LG | Google's Machine Learning framework TensorFlow was open-sourced in November
2015 [1] and has since built a growing community around it. TensorFlow is
supposed to be flexible for research purposes while also allowing its models to
be deployed productively. This work is aimed towards people with experience in
Machine Lea... | computer science |
6,376 | Robust Variational Inference | cs.LG | Variational inference is a powerful tool for approximate inference. However,
it mainly focuses on the evidence lower bound as variational objective and the
development of other measures for variational inference is a promising area of
research. This paper proposes a robust modification of evidence and a lower
bound for... | computer science |
6,377 | Diet Networks: Thin Parameters for Fat Genomics | cs.LG | Learning tasks such as those involving genomic data often poses a serious
challenge: the number of input features can be orders of magnitude larger than
the number of training examples, making it difficult to avoid overfitting, even
when using the known regularization techniques. We focus here on tasks in which
the inp... | computer science |
6,378 | The Emergence of Organizing Structure in Conceptual Representation | cs.LG | Both scientists and children make important structural discoveries, yet their
computational underpinnings are not well understood. Structure discovery has
previously been formalized as probabilistic inference about the right
structural form --- where form could be a tree, ring, chain, grid, etc. [Kemp &
Tenenbaum (2008... | computer science |
6,379 | The empirical size of trained neural networks | stat.ML | ReLU neural networks define piecewise linear functions of their inputs.
However, initializing and training a neural network is very different from
fitting a linear spline. In this paper, we expand empirically upon previous
theoretical work to demonstrate features of trained neural networks. Standard
network initializat... | computer science |
6,380 | The Upper Bound on Knots in Neural Networks | stat.ML | Neural networks with rectified linear unit activations are essentially
multivariate linear splines. As such, one of many ways to measure the
"complexity" or "expressivity" of a neural network is to count the number of
knots in the spline model. We study the number of knots in fully-connected
feedforward neural networks... | computer science |
6,381 | Cost-Sensitive Reference Pair Encoding for Multi-Label Learning | cs.LG | A general framework for multi-label classification(MLC) called multi-label
error-correcting code (ML-ECC) utilizes coding schemes in communication to
improve MLC performance. The framework includes some key algorithms for some
special cases of MLC, such as binary relevance and random k-labelsets.
Nevertheless, current ... | computer science |
6,382 | Graph-Based Manifold Frequency Analysis for Denoising | cs.LG | We propose a new framework for manifold denoising based on processing in the
graph Fourier frequency domain, derived from the spectral decomposition of the
discrete graph Laplacian. Our approach uses the Spectral Graph Wavelet
transform in order to per- form non-iterative denoising directly in the graph
frequency domai... | computer science |
6,383 | Improving Variational Auto-Encoders using Householder Flow | cs.LG | Variational auto-encoders (VAE) are scalable and powerful generative models.
However, the choice of the variational posterior determines tractability and
flexibility of the VAE. Commonly, latent variables are modeled using the normal
distribution with a diagonal covariance matrix. This results in computational
efficien... | computer science |
6,384 | Co-adaptive learning over a countable space | stat.ML | Co-adaptation is a special form of on-line learning where an algorithm
$\mathcal{A}$ must assist an unknown algorithm $\mathcal{B}$ to perform some
task. This is a general framework and has applications in recommendation
systems, search, education, and much more. Today, the most common use of
co-adaptive algorithms is ... | computer science |
6,385 | Autism Spectrum Disorder Classification using Graph Kernels on
Multidimensional Time Series | stat.ML | We present an approach to model time series data from resting state fMRI for
autism spectrum disorder (ASD) severity classification. We propose to adopt
kernel machines and employ graph kernels that define a kernel dot product
between two graphs. This enables us to take advantage of spatio-temporal
information to captu... | computer science |
6,386 | Weighted bandits or: How bandits learn distorted values that are not
expected | cs.LG | Motivated by models of human decision making proposed to explain commonly
observed deviations from conventional expected value preferences, we formulate
two stochastic multi-armed bandit problems with distorted probabilities on the
cost distributions: the classic $K$-armed bandit and the linearly parameterized
bandit. ... | computer science |
6,387 | Very Fast Kernel SVM under Budget Constraints | stat.ML | In this paper we propose a fast online Kernel SVM algorithm under tight
budget constraints. We propose to split the input space using LVQ and train a
Kernel SVM in each cluster. To allow for online training, we propose to limit
the size of the support vector set of each cluster using different strategies.
We show in th... | computer science |
6,388 | Outlier Robust Online Learning | cs.LG | We consider the problem of learning from noisy data in practical settings
where the size of data is too large to store on a single machine. More
challenging, the data coming from the wild may contain malicious outliers. To
address the scalability and robustness issues, we present an online robust
learning (ORL) approac... | computer science |
6,389 | Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs
by Selective Execution | cs.LG | We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward
deep neural network that allows selective execution. Given an input, only a
subset of D2NN neurons are executed, and the particular subset is determined by
the D2NN itself. By pruning unnecessary computation depending on input, D2NNs
provide a... | computer science |
6,390 | New Methods of Enhancing Prediction Accuracy in Linear Models with
Missing Data | stat.ML | In this paper, prediction for linear systems with missing information is
investigated. New methods are introduced to improve the Mean Squared Error
(MSE) on the test set in comparison to state-of-the-art methods, through
appropriate tuning of Bias-Variance trade-off. First, the use of proposed Soft
Weighted Prediction ... | computer science |
6,391 | An Interval-Based Bayesian Generative Model for Human Complex Activity
Recognition | stat.ML | Complex activity recognition is challenging due to the inherent uncertainty
and diversity of performing a complex activity. Normally, each instance of a
complex activity has its own configuration of atomic actions and their temporal
dependencies. We propose in this paper an atomic action-based Bayesian model
that const... | computer science |
6,392 | Estimating Quality in Multi-Objective Bandits Optimization | cs.LG | Many real-world applications are characterized by a number of conflicting
performance measures. As optimizing in a multi-objective setting leads to a set
of non-dominated solutions, a preference function is required for selecting the
solution with the appropriate trade-off between the objectives. The question
is: how g... | computer science |
6,393 | OpenML: An R Package to Connect to the Machine Learning Platform OpenML | stat.ML | OpenML is an online machine learning platform where researchers can easily
share data, machine learning tasks and experiments as well as organize them
online to work and collaborate more efficiently. In this paper, we present an R
package to interface with the OpenML platform and illustrate its usage in
combination wit... | computer science |
6,394 | Tunable GMM Kernels | stat.ML | The recently proposed "generalized min-max" (GMM) kernel can be efficiently
linearized, with direct applications in large-scale statistical learning and
fast near neighbor search. The linearized GMM kernel was extensively compared
in with linearized radial basis function (RBF) kernel. On a large number of
classificatio... | computer science |
6,395 | QuickNet: Maximizing Efficiency and Efficacy in Deep Architectures | cs.LG | We present QuickNet, a fast and accurate network architecture that is both
faster and significantly more accurate than other fast deep architectures like
SqueezeNet. Furthermore, it uses less parameters than previous networks, making
it more memory efficient. We do this by making two major modifications to the
referenc... | computer science |
6,396 | AdaGAN: Boosting Generative Models | stat.ML | Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an
effective method for training generative models of complex data such as natural
images. However, they are notoriously hard to train and can suffer from the
problem of missing modes where the model is not able to produce examples in
certain regions o... | computer science |
6,397 | Heterogeneous Transfer Learning: An Unsupervised Approach | cs.LG | Transfer learning leverages the knowledge in one domain, the source domain,
to improve learning efficiency in another domain, the target domain. Existing
transfer learning research is relatively well-progressed, but only in
situations where the feature spaces of the domains are homogeneous and the
target domain contain... | computer science |
6,398 | Similarity Function Tracking using Pairwise Comparisons | stat.ML | Recent work in distance metric learning has focused on learning
transformations of data that best align with specified pairwise similarity and
dissimilarity constraints, often supplied by a human observer. The learned
transformations lead to improved retrieval, classification, and clustering
algorithms due to the bette... | computer science |
6,399 | Fast mixing for Latent Dirichlet allocation | cs.LG | Markov chain Monte Carlo (MCMC) algorithms are ubiquitous in probability
theory in general and in machine learning in particular. A Markov chain is
devised so that its stationary distribution is some probability distribution of
interest. Then one samples from the given distribution by running the Markov
chain for a "lo... | computer science |
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