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Dual Teaching: A Practical Semi-supervised Wrapper Method | cs.LG stat.ML | 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 in practice. For practical use, this paper proposes a novel
semi-supervised wrapper method, Dual Teaching, whose assumptions are easy to
set up. Dual Teaching adopts two external classifiers to estimate the false
positives and false negatives of the base learner. Only if the recall of every
external classifier is greater than zero and the sum of the precision is
greater than one, Dual Teaching will train a base learner from partially
labeled data as effectively as the fully-labeled-data-trained classifier. The
effectiveness of Dual Teaching is proved in both theory and practice.
| Fuqaing Liu, Chenwei Deng, Fukun Bi, Yiding Yang | null | 1611.03981 | null | null |
Riemannian Tensor Completion with Side Information | stat.ML cs.LG cs.NA | By restricting the iterate on a nonlinear manifold, the recently proposed
Riemannian optimization methods prove to be both efficient and effective in low
rank tensor completion problems. However, existing methods fail to exploit the
easily accessible side information, due to their format mismatch. Consequently,
there is still room for improvement in such methods. To fill the gap, in this
paper, a novel Riemannian model is proposed to organically integrate the
original model and the side information by overcoming their inconsistency. For
this particular model, an efficient Riemannian conjugate gradient descent
solver is devised based on a new metric that captures the curvature of the
objective.Numerical experiments suggest that our solver is more accurate than
the state-of-the-art without compromising the efficiency.
| Tengfei Zhou, Hui Qian, Zebang Shen, Congfu Xu | null | 1611.03993 | null | null |
Prognostics of Surgical Site Infections using Dynamic Health Data | cs.LG | Surgical Site Infection (SSI) is a national priority in healthcare research.
Much research attention has been attracted to develop better SSI risk
prediction models. However, most of the existing SSI risk prediction models are
built on static risk factors such as comorbidities and operative factors. In
this paper, we investigate the use of the dynamic wound data for SSI risk
prediction. There have been emerging mobile health (mHealth) tools that can
closely monitor the patients and generate continuous measurements of many
wound-related variables and other evolving clinical variables. Since existing
prediction models of SSI have quite limited capacity to utilize the evolving
clinical data, we develop the corresponding solution to equip these mHealth
tools with decision-making capabilities for SSI prediction with a seamless
assembly of several machine learning models to tackle the analytic challenges
arising from the spatial-temporal data. The basic idea is to exploit the
low-rank property of the spatial-temporal data via the bilinear formulation,
and further enhance it with automatic missing data imputation by the matrix
completion technique. We derive efficient optimization algorithms to implement
these models and demonstrate the superior performances of our new predictive
model on a real-world dataset of SSI, compared to a range of state-of-the-art
methods.
| Chuyang Ke, Yan Jin, Heather Evans, Bill Lober, Xiaoning Qian, Ji Liu,
Shuai Huang | null | 1611.04049 | null | null |
GANS for Sequences of Discrete Elements with the Gumbel-softmax
Distribution | stat.ML cs.LG | 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 the Gumbel-softmax distribution, which is a continuous
approximation to a multinomial distribution parameterized in terms of the
softmax function. In this work, we evaluate the performance of GANs based on
recurrent neural networks with Gumbel-softmax output distributions in the task
of generating sequences of discrete elements.
| Matt J. Kusner, Jos\'e Miguel Hern\'andez-Lobato | null | 1611.04051 | null | null |
Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly
Accelerated Dynamic Imaging | stat.ML cs.LG | 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 demonstrated the promise of sparsity and low-rank techniques. For
example, the patches of the underlying data are modeled as sparse in an
adaptive dictionary domain, and the resulting image and dictionary estimation
from undersampled measurements is called dictionary-blind compressed sensing,
or the dynamic image sequence is modeled as a sum of low-rank and sparse (in
some transform domain) components (L+S model) that are estimated from limited
measurements. In this work, we investigate a data-adaptive extension of the L+S
model, dubbed LASSI, where the temporal image sequence is decomposed into a
low-rank component and a component whose spatiotemporal (3D) patches are sparse
in some adaptive dictionary domain. We investigate various formulations and
efficient methods for jointly estimating the underlying dynamic signal
components and the spatiotemporal dictionary from limited measurements. We also
obtain efficient sparsity penalized dictionary-blind compressed sensing methods
as special cases of our LASSI approaches. Our numerical experiments demonstrate
the promising performance of LASSI schemes for dynamic magnetic resonance image
reconstruction from limited k-t space data compared to recent methods such as
k-t SLR and L+S, and compared to the proposed dictionary-blind compressed
sensing method.
| Saiprasad Ravishankar, Brian E. Moore, Raj Rao Nadakuditi, and Jeffrey
A. Fessler | 10.1109/TMI.2017.2650960 | 1611.04069 | null | null |
Batched Gaussian Process Bandit Optimization via Determinantal Point
Processes | cs.LG | Gaussian Process bandit optimization has emerged as a powerful tool for
optimizing noisy black box functions. One example in machine learning is
hyper-parameter optimization where each evaluation of the target function
requires training a model which may involve days or even weeks of computation.
Most methods for this so-called "Bayesian optimization" only allow sequential
exploration of the parameter space. However, it is often desirable to propose
batches or sets of parameter values to explore simultaneously, especially when
there are large parallel processing facilities at our disposal. Batch methods
require modeling the interaction between the different evaluations in the
batch, which can be expensive in complex scenarios. In this paper, we propose a
new approach for parallelizing Bayesian optimization by modeling the diversity
of a batch via Determinantal point processes (DPPs) whose kernels are learned
automatically. This allows us to generalize a previous result as well as prove
better regret bounds based on DPP sampling. Our experiments on a variety of
synthetic and real-world robotics and hyper-parameter optimization tasks
indicate that our DPP-based methods, especially those based on DPP sampling,
outperform state-of-the-art methods.
| Tarun Kathuria, Amit Deshpande, Pushmeet Kohli | null | 1611.04088 | null | null |
Accelerated Variance Reduced Block Coordinate Descent | stat.ML cs.LG | 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 lack at least one of these qualities, and thus are
inefficient in handling such big data challenge. In this paper, we propose a
method enjoying all these merits with an accelerated convergence rate
$O(\frac{1}{k^2})$. Empirical studies on large scale datasets with more than
one million features are conducted to show the effectiveness of our methods in
practice.
| Zebang Shen, Hui Qian, Chao Zhang, and Tengfei Zhou | null | 1611.04149 | null | null |
Realistic risk-mitigating recommendations via inverse classification | cs.LG stat.ML | 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 assumption that recommendations are implemented instantaneously. We
propose using longitudinal data to alleviate such issues in two ways. First, we
use past outcome probabilities as features in the present. Use of such past
probabilities ties historical behavior to the present, allowing for more
information to be taken into account when making initial probability estimates
and subsequently performing inverse classification. Secondly, following inverse
classification application, optimized instances' unchangeable features
(e.g.,~age) are updated using values from the next longitudinal time period.
Optimized test instance probabilities are then reassessed. Updating the
unchangeable features in this manner reflects the notion that improvements in
outcome likelihood, which result from following the inverse classification
recommendations, do not materialize instantaneously. As our experiments
demonstrate, more realistic estimates of probability can be obtained by
factoring in such considerations.
| Michael T. Lash and W. Nick Street | null | 1611.04199 | null | null |
CAD2RL: Real Single-Image Flight without a Single Real Image | cs.LG cs.CV cs.RO | Deep reinforcement learning has emerged as a promising and powerful technique
for automatically acquiring control policies that can process raw sensory
inputs, such as images, and perform complex behaviors. However, extending deep
RL to real-world robotic tasks has proven challenging, particularly in
safety-critical domains such as autonomous flight, where a trial-and-error
learning process is often impractical. In this paper, we explore the following
question: can we train vision-based navigation policies entirely in simulation,
and then transfer them into the real world to achieve real-world flight without
a single real training image? We propose a learning method that we call
CAD$^2$RL, which can be used to perform collision-free indoor flight in the
real world while being trained entirely on 3D CAD models. Our method uses
single RGB images from a monocular camera, without needing to explicitly
reconstruct the 3D geometry of the environment or perform explicit motion
planning. Our learned collision avoidance policy is represented by a deep
convolutional neural network that directly processes raw monocular images and
outputs velocity commands. This policy is trained entirely on simulated images,
with a Monte Carlo policy evaluation algorithm that directly optimizes the
network's ability to produce collision-free flight. By highly randomizing the
rendering settings for our simulated training set, we show that we can train a
policy that generalizes to the real world, without requiring the simulator to
be particularly realistic or high-fidelity. We evaluate our method by flying a
real quadrotor through indoor environments, and further evaluate the design
choices in our simulator through a series of ablation studies on depth
prediction. For supplementary video see: https://youtu.be/nXBWmzFrj5s
| Fereshteh Sadeghi and Sergey Levine | null | 1611.04201 | null | null |
Preference Completion from Partial Rankings | stat.ML cs.LG | 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 are often recorded as numerical scores, the predictive quantity of
interest is the underlying rankings. Thus, attempts to closely match the
recorded scores may lead to overfitting and impair generalization performance.
Instead, we propose an estimator that directly fits the underlying preference
order, combined with nuclear norm constraints to encourage low--rank
parameters. Besides (approximate) correctness of the ranking order, the
proposed estimator makes no generative assumption on the numerical scores of
the observations. One consequence is that the proposed estimator can fit any
consistent partial ranking over a subset of the items represented as a directed
acyclic graph (DAG), generalizing standard techniques that can only fit
preference scores. Despite this generality, for supervision representing total
or blockwise total orders, the computational complexity of our algorithm is
within a $\log$ factor of the standard algorithms for nuclear norm
regularization based estimates for matrix completion. We further show promising
empirical results for a novel and challenging application of collaboratively
ranking of the associations between brain--regions and cognitive neuroscience
terms.
| Suriya Gunasekar, Oluwasanmi Koyejo, Joydeep Ghosh | null | 1611.04218 | null | null |
Learning Sparse, Distributed Representations using the Hebbian Principle | cs.LG | The "fire together, wire together" Hebbian model is a central principle for
learning in neuroscience, but surprisingly, it has found limited applicability
in modern machine learning. In this paper, we take a first step towards
bridging this gap, by developing flavors of competitive Hebbian learning which
produce sparse, distributed neural codes using online adaptation with minimal
tuning. We propose an unsupervised algorithm, termed Adaptive Hebbian Learning
(AHL). We illustrate the distributed nature of the learned representations via
output entropy computations for synthetic data, and demonstrate superior
performance, compared to standard alternatives such as autoencoders, in
training a deep convolutional net on standard image datasets.
| Aseem Wadhwa and Upamanyu Madhow | null | 1611.04228 | null | null |
Identity Matters in Deep Learning | cs.LG cs.NE stat.ML | An emerging design principle in deep learning is that each layer of a deep
artificial neural network should be able to easily express the identity
transformation. This idea not only motivated various normalization techniques,
such as \emph{batch normalization}, but was also key to the immense success of
\emph{residual networks}.
In this work, we put the principle of \emph{identity parameterization} on a
more solid theoretical footing alongside further empirical progress. We first
give a strikingly simple proof that arbitrarily deep linear residual networks
have no spurious local optima. The same result for linear feed-forward networks
in their standard parameterization is substantially more delicate. Second, we
show that residual networks with ReLu activations have universal finite-sample
expressivity in the sense that the network can represent any function of its
sample provided that the model has more parameters than the sample size.
Directly inspired by our theory, we experiment with a radically simple
residual architecture consisting of only residual convolutional layers and ReLu
activations, but no batch normalization, dropout, or max pool. Our model
improves significantly on previous all-convolutional networks on the CIFAR10,
CIFAR100, and ImageNet classification benchmarks.
| Moritz Hardt and Tengyu Ma | null | 1611.04231 | null | null |
On the Quantitative Analysis of Decoder-Based Generative Models | cs.LG | The past several years have seen remarkable progress in generative models
which produce convincing samples of images and other modalities. A shared
component of many powerful generative models is a decoder network, a parametric
deep neural net that defines a generative distribution. Examples include
variational autoencoders, generative adversarial networks, and generative
moment matching networks. Unfortunately, it can be difficult to quantify the
performance of these models because of the intractability of log-likelihood
estimation, and inspecting samples can be misleading. We propose to use
Annealed Importance Sampling for evaluating log-likelihoods for decoder-based
models and validate its accuracy using bidirectional Monte Carlo. The
evaluation code is provided at https://github.com/tonywu95/eval_gen. Using this
technique, we analyze the performance of decoder-based models, the
effectiveness of existing log-likelihood estimators, the degree of overfitting,
and the degree to which these models miss important modes of the data
distribution.
| Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger Grosse | null | 1611.04273 | null | null |
Attending to Characters in Neural Sequence Labeling Models | cs.CL cs.LG cs.NE | Sequence labeling architectures use word embeddings for capturing similarity,
but suffer when handling previously unseen or rare words. We investigate
character-level extensions to such models and propose a novel architecture for
combining alternative word representations. By using an attention mechanism,
the model is able to dynamically decide how much information to use from a
word- or character-level component. We evaluated different architectures on a
range of sequence labeling datasets, and character-level extensions were found
to improve performance on every benchmark. In addition, the proposed
attention-based architecture delivered the best results even with a smaller
number of trainable parameters.
| Marek Rei, Gamal K.O. Crichton, Sampo Pyysalo | null | 1611.04361 | null | null |
On numerical approximation schemes for expectation propagation | stat.CO cs.LG stat.ML | Several numerical approximation strategies for the expectation-propagation
algorithm are studied in the context of large-scale learning: the Laplace
method, a faster variant of it, Gaussian quadrature, and a deterministic
version of variational sampling (i.e., combining quadrature with variational
approximation). Experiments in training linear binary classifiers show that the
expectation-propagation algorithm converges best using variational sampling,
while it also converges well using Laplace-style methods with smooth factors
but tends to be unstable with non-differentiable ones. Gaussian quadrature
yields unstable behavior or convergence to a sub-optimal solution in most
experiments.
| Alexis Roche | null | 1611.04416 | null | null |
Generative Models and Model Criticism via Optimized Maximum Mean
Discrepancy | stat.ML cs.AI cs.LG cs.NE stat.ME | We propose a method to optimize the representation and distinguishability of
samples from two probability distributions, by maximizing the estimated power
of a statistical test based on the maximum mean discrepancy (MMD). This
optimized MMD is applied to the setting of unsupervised learning by generative
adversarial networks (GAN), in which a model attempts to generate realistic
samples, and a discriminator attempts to tell these apart from data samples. In
this context, the MMD may be used in two roles: first, as a discriminator,
either directly on the samples, or on features of the samples. Second, the MMD
can be used to evaluate the performance of a generative model, by testing the
model's samples against a reference data set. In the latter role, the optimized
MMD is particularly helpful, as it gives an interpretable indication of how the
model and data distributions differ, even in cases where individual model
samples are not easily distinguished either by eye or by classifier.
| Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De,
Aaditya Ramdas, Alex Smola, Arthur Gretton | null | 1611.04488 | null | null |
Post Training in Deep Learning with Last Kernel | stat.ML cs.LG | 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, which only optimizes the last layer of the network. We show that
this procedure can be analyzed in the context of kernel theory, with the first
layers computing an embedding of the data and the last layer a statistical
model to solve the task based on this embedding. This step makes sure that the
embedding, or representation, of the data is used in the best possible way for
the considered task. This idea is then tested on multiple architectures with
various data sets, showing that it consistently provides a boost in
performance.
| Thomas Moreau and Julien Audiffren | null | 1611.04499 | null | null |
Deep Learning with Sets and Point Clouds | stat.ML cs.LG cs.NE | We introduce a simple permutation equivariant layer for deep learning with
set structure.This type of layer, obtained by parameter-sharing, has a simple
implementation and linear-time complexity in the size of each set. We use deep
permutation-invariant networks to perform point-could classification and
MNIST-digit summation, where in both cases the output is invariant to
permutations of the input. In a semi-supervised setting, where the goal is make
predictions for each instance within a set, we demonstrate the usefulness of
this type of layer in set-outlier detection as well as semi-supervised learning
with clustering side-information.
| Siamak Ravanbakhsh and Jeff Schneider and Barnabas Poczos | null | 1611.045 | null | null |
Normalizing the Normalizers: Comparing and Extending Network
Normalization Schemes | cs.LG stat.ML | 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 networks. On the other hand, layer normalization normalizes
the activations across all activities within a layer. This was shown to work
well in the recurrent setting. In this paper we propose a unified view of
normalization techniques, as forms of divisive normalization, which includes
layer and batch normalization as special cases. Our second contribution is the
finding that a small modification to these normalization schemes, in
conjunction with a sparse regularizer on the activations, leads to significant
benefits over standard normalization techniques. We demonstrate the
effectiveness of our unified divisive normalization framework in the context of
convolutional neural nets and recurrent neural networks, showing improvements
over baselines in image classification, language modeling as well as
super-resolution.
| Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S.
Zemel | null | 1611.0452 | null | null |
Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann
Machines | quant-ph cs.LG stat.ML | Quantum annealing (QA) is a hardware-based heuristic optimization and
sampling method applicable to discrete undirected graphical models. While
similar to simulated annealing, QA relies on quantum, rather than thermal,
effects to explore complex search spaces. For many classes of problems, QA is
known to offer computational advantages over simulated annealing. Here we
report on the ability of recent QA hardware to accelerate training of fully
visible Boltzmann machines. We characterize the sampling distribution of QA
hardware, and show that in many cases, the quantum distributions differ
significantly from classical Boltzmann distributions. In spite of this
difference, training (which seeks to match data and model statistics) using
standard classical gradient updates is still effective. We investigate the use
of QA for seeding Markov chains as an alternative to contrastive divergence
(CD) and persistent contrastive divergence (PCD). Using $k=50$ Gibbs steps, we
show that for problems with high-energy barriers between modes, QA-based seeds
can improve upon chains with CD and PCD initializations. For these hard
problems, QA gradient estimates are more accurate, and allow for faster
learning. Furthermore, and interestingly, even the case of raw QA samples (that
is, $k=0$) achieved similar improvements. We argue that this relates to the
fact that we are training a quantum rather than classical Boltzmann
distribution in this case. The learned parameters give rise to hardware QA
distributions closely approximating classical Boltzmann distributions that are
hard to train with CD/PCD.
| Dmytro Korenkevych, Yanbo Xue, Zhengbing Bian, Fabian Chudak, William
G. Macready, Jason Rolfe, Evgeny Andriyash | null | 1611.04528 | null | null |
Learning-Theoretic Foundations of Algorithm Configuration for
Combinatorial Partitioning Problems | cs.DS cs.AI cs.LG | Max-cut, clustering, and many other partitioning problems that are of
significant importance to machine learning and other scientific fields are
NP-hard, a reality that has motivated researchers to develop a wealth of
approximation algorithms and heuristics. Although the best algorithm to use
typically depends on the specific application domain, a worst-case analysis is
often used to compare algorithms. This may be misleading if worst-case
instances occur infrequently, and thus there is a demand for optimization
methods which return the algorithm configuration best suited for the given
application's typical inputs. We address this problem for clustering, max-cut,
and other partitioning problems, such as integer quadratic programming, by
designing computationally efficient and sample efficient learning algorithms
which receive samples from an application-specific distribution over problem
instances and learn a partitioning algorithm with high expected performance.
Our algorithms learn over common integer quadratic programming and clustering
algorithm families: SDP rounding algorithms and agglomerative clustering
algorithms with dynamic programming. For our sample complexity analysis, we
provide tight bounds on the pseudodimension of these algorithm classes, and
show that surprisingly, even for classes of algorithms parameterized by a
single parameter, the pseudo-dimension is superconstant. In this way, our work
both contributes to the foundations of algorithm configuration and pushes the
boundaries of learning theory, since the algorithm classes we analyze consist
of multi-stage optimization procedures and are significantly more complex than
classes typically studied in learning theory.
| Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik, and Colin
White | null | 1611.04535 | null | null |
Splitting matters: how monotone transformation of predictor variables
may improve the predictions of decision tree models | stat.ML cs.LG | 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 of the split point of a tree rule. The sensitivity of the
prediction error to the split point interpolation is high when the split point
of the tree is estimated based on very few observations, reaching 9%
misclassification error when only 10 observations are used for constructing a
split, and shrinking to 1% when relying on 100 observations. This study
compares the performance of alternative methods for split point interpolation
and concludes that the best choice is taking the mid-point between the two
closest points to the split point of the tree. Furthermore, if the (continuous)
distribution of the predictor variable is known, then using its probability
integral for transforming the variable ("quantile transformation") will reduce
the model's interpolation error by up to about a half on average. Accordingly,
this study provides guidelines for both developers and users of decision tree
models (including bagging and random forest).
| Tal Galili, Isaac Meilijson | null | 1611.04561 | null | null |
Earliness-Aware Deep Convolutional Networks for Early Time Series
Classification | cs.LG | We present Earliness-Aware Deep Convolutional Networks (EA-ConvNets), an
end-to-end deep learning framework, for early classification of time series
data. Unlike most existing methods for early classification of time series
data, that are designed to solve this problem under the assumption of the
availability of a good set of pre-defined (often hand-crafted) features, our
framework can jointly perform feature learning (by learning a deep hierarchy of
\emph{shapelets} capturing the salient characteristics in each time series),
along with a dynamic truncation model to help our deep feature learning
architecture focus on the early parts of each time series. Consequently, our
framework is able to make highly reliable early predictions, outperforming
various state-of-the-art methods for early time series classification, while
also being competitive when compared to the state-of-the-art time series
classification algorithms that work with \emph{fully observed} time series
data. To the best of our knowledge, the proposed framework is the first to
perform data-driven (deep) feature learning in the context of early
classification of time series data. We perform a comprehensive set of
experiments, on several benchmark data sets, which demonstrate that our method
yields significantly better predictions than various state-of-the-art methods
designed for early time series classification. In addition to obtaining high
accuracies, our experiments also show that the learned deep shapelets based
features are also highly interpretable and can help gain better understanding
of the underlying characteristics of time series data.
| Wenlin Wang, Changyou Chen, Wenqi Wang, Piyush Rai, Lawrence Carin | null | 1611.04578 | null | null |
How to scale distributed deep learning? | cs.LG | Training time on large datasets for deep neural networks is the principal
workflow bottleneck in a number of important applications of deep learning,
such as object classification and detection in automatic driver assistance
systems (ADAS). To minimize training time, the training of a deep neural
network must be scaled beyond a single machine to as many machines as possible
by distributing the optimization method used for training. While a number of
approaches have been proposed for distributed stochastic gradient descent
(SGD), at the current time synchronous approaches to distributed SGD appear to
be showing the greatest performance at large scale. Synchronous scaling of SGD
suffers from the need to synchronize all processors on each gradient step and
is not resilient in the face of failing or lagging processors. In asynchronous
approaches using parameter servers, training is slowed by contention to the
parameter server. In this paper we compare the convergence of synchronous and
asynchronous SGD for training a modern ResNet network architecture on the
ImageNet classification problem. We also propose an asynchronous method,
gossiping SGD, that aims to retain the positive features of both systems by
replacing the all-reduce collective operation of synchronous training with a
gossip aggregation algorithm. We find, perhaps counterintuitively, that
asynchronous SGD, including both elastic averaging and gossiping, converges
faster at fewer nodes (up to about 32 nodes), whereas synchronous SGD scales
better to more nodes (up to about 100 nodes).
| Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer | null | 1611.04581 | null | null |
Link Prediction using Embedded Knowledge Graphs | cs.AI cs.CL cs.LG | Since large knowledge bases are typically incomplete, missing facts need to
be inferred from observed facts in a task called knowledge base completion. The
most successful approaches to this task have typically explored explicit paths
through sequences of triples. These approaches have usually resorted to
human-designed sampling procedures, since large knowledge graphs produce
prohibitively large numbers of possible paths, most of which are uninformative.
As an alternative approach, we propose performing a single, short sequence of
interactive lookup operations on an embedded knowledge graph which has been
trained through end-to-end backpropagation to be an optimized and compressed
version of the initial knowledge base. Our proposed model, called Embedded
Knowledge Graph Network (EKGN), achieves new state-of-the-art results on
popular knowledge base completion benchmarks.
| Yelong Shen, Po-Sen Huang, Ming-Wei Chang, Jianfeng Gao | null | 1611.04642 | null | null |
A Generic Coordinate Descent Framework for Learning from Implicit
Feedback | cs.IR cs.LG | In recent years, interest in recommender research has shifted from explicit
feedback towards implicit feedback data. A diversity of complex models has been
proposed for a wide variety of applications. Despite this, learning from
implicit feedback is still computationally challenging. So far, most work
relies on stochastic gradient descent (SGD) solvers which are easy to derive,
but in practice challenging to apply, especially for tasks with many items. For
the simple matrix factorization model, an efficient coordinate descent (CD)
solver has been previously proposed. However, efficient CD approaches have not
been derived for more complex models.
In this paper, we provide a new framework for deriving efficient CD
algorithms for complex recommender models. We identify and introduce the
property of k-separable models. We show that k-separability is a sufficient
property to allow efficient optimization of implicit recommender problems with
CD. We illustrate this framework on a variety of state-of-the-art models
including factorization machines and Tucker decomposition. To summarize, our
work provides the theory and building blocks to derive efficient implicit CD
algorithms for complex recommender models.
| Immanuel Bayer, Xiangnan He, Bhargav Kanagal, Steffen Rendle | null | 1611.04666 | null | null |
Robust Matrix Regression | cs.LG | Modern technologies are producing datasets with complex intrinsic structures,
and they can be naturally represented as matrices instead of vectors. To
preserve the latent data structures during processing, modern regression
approaches incorporate the low-rank property to the model and achieve
satisfactory performance for certain applications. These approaches all assume
that both predictors and labels for each pair of data within the training set
are accurate. However, in real-world applications, it is common to see the
training data contaminated by noises, which can affect the robustness of these
matrix regression methods. In this paper, we address this issue by introducing
a novel robust matrix regression method. We also derive efficient proximal
algorithms for model training. To evaluate the performance of our methods, we
apply it to real world applications with comparative studies. Our method
achieves the state-of-the-art performance, which shows the effectiveness and
the practical value of our method.
| Hang Zhang, Fengyuan Zhu and Shixin Li | null | 1611.04686 | null | null |
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement
Learning | cs.AI cs.LG | Count-based exploration algorithms are known to perform near-optimally when
used in conjunction with tabular reinforcement learning (RL) methods for
solving small discrete Markov decision processes (MDPs). It is generally
thought that count-based methods cannot be applied in high-dimensional state
spaces, since most states will only occur once. Recent deep RL exploration
strategies are able to deal with high-dimensional continuous state spaces
through complex heuristics, often relying on optimism in the face of
uncertainty or intrinsic motivation. In this work, we describe a surprising
finding: a simple generalization of the classic count-based approach can reach
near state-of-the-art performance on various high-dimensional and/or continuous
deep RL benchmarks. States are mapped to hash codes, which allows to count
their occurrences with a hash table. These counts are then used to compute a
reward bonus according to the classic count-based exploration theory. We find
that simple hash functions can achieve surprisingly good results on many
challenging tasks. Furthermore, we show that a domain-dependent learned hash
code may further improve these results. Detailed analysis reveals important
aspects of a good hash function: 1) having appropriate granularity and 2)
encoding information relevant to solving the MDP. This exploration strategy
achieves near state-of-the-art performance on both continuous control tasks and
Atari 2600 games, hence providing a simple yet powerful baseline for solving
MDPs that require considerable exploration.
| Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan
Duan, John Schulman, Filip De Turck, Pieter Abbeel | null | 1611.04717 | null | null |
AdversariaLib: An Open-source Library for the Security Evaluation of
Machine Learning Algorithms Under Attack | cs.CR cs.LG | We present AdversariaLib, an open-source python library for the security
evaluation of machine learning (ML) against carefully-targeted attacks. It
supports the implementation of several attacks proposed thus far in the
literature of adversarial learning, allows for the evaluation of a wide range
of ML algorithms, runs on multiple platforms, and has multi-processing enabled.
The library has a modular architecture that makes it easy to use and to extend
by implementing novel attacks and countermeasures. It relies on other
widely-used open-source ML libraries, including scikit-learn and FANN.
Classification algorithms are implemented and optimized in C/C++, allowing for
a fast evaluation of the simulated attacks. The package is distributed under
the GNU General Public License v3, and it is available for download at
http://sourceforge.net/projects/adversarialib.
| Igino Corona and Battista Biggio and Davide Maiorca | null | 1611.04786 | null | null |
The Power of Normalization: Faster Evasion of Saddle Points | cs.LG math.OC stat.ML | A commonly used heuristic in non-convex optimization is Normalized Gradient
Descent (NGD) - a variant of gradient descent in which only the direction of
the gradient is taken into account and its magnitude ignored. We analyze this
heuristic and show that with carefully chosen parameters and noise injection,
this method can provably evade saddle points. We establish the convergence of
NGD to a local minimum, and demonstrate rates which improve upon the fastest
known first order algorithm due to Ge e al. (2015).
The effectiveness of our method is demonstrated via an application to the
problem of online tensor decomposition; a task for which saddle point evasion
is known to result in convergence to global minima.
| Kfir Y. Levy | null | 1611.04831 | null | null |
Multilinear Low-Rank Tensors on Graphs & Applications | cs.CV cs.LG stat.ML | We propose a new framework for the analysis of low-rank tensors which lies at
the intersection of spectral graph theory and signal processing. As a first
step, we present a new graph based low-rank decomposition which approximates
the classical low-rank SVD for matrices and multi-linear SVD for tensors. Then,
building on this novel decomposition we construct a general class of convex
optimization problems for approximately solving low-rank tensor inverse
problems, such as tensor Robust PCA. The whole framework is named as
'Multilinear Low-rank tensors on Graphs (MLRTG)'. Our theoretical analysis
shows: 1) MLRTG stands on the notion of approximate stationarity of
multi-dimensional signals on graphs and 2) the approximation error depends on
the eigen gaps of the graphs. We demonstrate applications for a wide variety of
4 artificial and 12 real tensor datasets, such as EEG, FMRI, BCI, surveillance
videos and hyperspectral images. Generalization of the tensor concepts to
non-euclidean domain, orders of magnitude speed-up, low-memory requirement and
significantly enhanced performance at low SNR are the key aspects of our
framework.
| Nauman Shahid, Francesco Grassi, Pierre Vandergheynst | null | 1611.04835 | null | null |
The Power of Side-information in Subgraph Detection | cs.LG cs.DS | In this work, we tackle the problem of hidden community detection. We
consider Belief Propagation (BP) applied to the problem of detecting a hidden
Erd\H{o}s-R\'enyi (ER) graph embedded in a larger and sparser ER graph, in the
presence of side-information. We derive two related algorithms based on BP to
perform subgraph detection in the presence of two kinds of side-information.
The first variant of side-information consists of a set of nodes, called cues,
known to be from the subgraph. The second variant of side-information consists
of a set of nodes that are cues with a given probability. It was shown in past
works that BP without side-information fails to detect the subgraph correctly
when an effective signal-to-noise ratio (SNR) parameter falls below a
threshold. In contrast, in the presence of non-trivial side-information, we
show that the BP algorithm achieves asymptotically zero error for any value of
the SNR parameter. We validate our results through simulations on synthetic
datasets as well as on a few real world networks.
| Arun Kadavankandy (MAESTRO), Konstantin Avrachenkov (MAESTRO), Laura
Cottatellucci, Rajesh Sundaresan (ECE) | null | 1611.04847 | null | null |
Constrained Low-Rank Learning Using Least Squares-Based Regularization | cs.CV cs.LG | Low-rank learning has attracted much attention recently due to its efficacy
in a rich variety of real-world tasks, e.g., subspace segmentation and image
categorization. Most low-rank methods are incapable of capturing
low-dimensional subspace for supervised learning tasks, e.g., classification
and regression. This paper aims to learn both the discriminant low-rank
representation (LRR) and the robust projecting subspace in a supervised manner.
To achieve this goal, we cast the problem into a constrained rank minimization
framework by adopting the least squares regularization. Naturally, the data
label structure tends to resemble that of the corresponding low-dimensional
representation, which is derived from the robust subspace projection of clean
data by low-rank learning. Moreover, the low-dimensional representation of
original data can be paired with some informative structure by imposing an
appropriate constraint, e.g., Laplacian regularizer. Therefore, we propose a
novel constrained LRR method. The objective function is formulated as a
constrained nuclear norm minimization problem, which can be solved by the
inexact augmented Lagrange multiplier algorithm. Extensive experiments on image
classification, human pose estimation, and robust face recovery have confirmed
the superiority of our method.
| Ping Li and Jun Yu and Meng Wang and Luming Zhang and Deng Cai and
Xuelong Li | 10.1109/TCYB.2016.2623638 | 1611.0487 | null | null |
Audio Event and Scene Recognition: A Unified Approach using Strongly and
Weakly Labeled Data | cs.LG cs.CV cs.SD | In this paper we propose a novel learning framework called Supervised and
Weakly Supervised Learning where the goal is to learn simultaneously from
weakly and strongly labeled data. Strongly labeled data can be simply
understood as fully supervised data where all labeled instances are available.
In weakly supervised learning only data is weakly labeled which prevents one
from directly applying supervised learning methods. Our proposed framework is
motivated by the fact that a small amount of strongly labeled data can give
considerable improvement over only weakly supervised learning. The primary
problem domain focus of this paper is acoustic event and scene detection in
audio recordings. We first propose a naive formulation for leveraging labeled
data in both forms. We then propose a more general framework for Supervised and
Weakly Supervised Learning (SWSL). Based on this general framework, we propose
a graph based approach for SWSL. Our main method is based on manifold
regularization on graphs in which we show that the unified learning can be
formulated as a constraint optimization problem which can be solved by
iterative concave-convex procedure (CCCP). Our experiments show that our
proposed framework can address several concerns of audio content analysis using
weakly labeled data.
| Anurag Kumar, Bhiksha Raj | null | 1611.04871 | null | null |
Unsupervised Learning with Truncated Gaussian Graphical Models | stat.ML cs.LG | 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 variant of GGMs, which relaxes the Gaussian restriction and yet admits
efficient inference. Specifically, we impose a bipartite structure on the GGM
and govern the hidden variables by truncated normal distributions. The
nonlinearity of the model is revealed by its connection to rectified linear
unit (ReLU) neural networks. Meanwhile, thanks to the bipartite structure and
appealing properties of truncated normals, we are able to train the models
efficiently using contrastive divergence. We consider three output constructs,
accounting for real-valued, binary and count data. We further extend the model
to deep constructions and show that deep models can be used for unsupervised
pre-training of rectifier neural networks. Extensive experimental results are
provided to validate the proposed models and demonstrate their superiority over
competing models.
| Qinliang Su, Xuejun Liao, Chunyuan Li, Zhe Gan, Lawrence Carin | null | 1611.0492 | null | null |
Robust Semi-Supervised Graph Classifier Learning with Negative Edge
Weights | cs.LG | In a semi-supervised learning scenario, (possibly noisy) partially observed
labels are used as input to train a classifier, in order to assign labels to
unclassified samples. In this paper, we study this classifier learning problem
from a graph signal processing (GSP) perspective. Specifically, by viewing a
binary classifier as a piecewise constant graph-signal in a high-dimensional
feature space, we cast classifier learning as a signal restoration problem via
a classical maximum a posteriori (MAP) formulation. Unlike previous
graph-signal restoration works, we consider in addition edges with negative
weights that signify anti-correlation between samples. One unfortunate
consequence is that the graph Laplacian matrix $\mathbf{L}$ can be indefinite,
and previously proposed graph-signal smoothness prior $\mathbf{x}^T \mathbf{L}
\mathbf{x}$ for candidate signal $\mathbf{x}$ can lead to pathological
solutions. In response, we derive an optimal perturbation matrix
$\boldsymbol{\Delta}$ - based on a fast lower-bound computation of the minimum
eigenvalue of $\mathbf{L}$ via a novel application of the Haynsworth inertia
additivity formula---so that $\mathbf{L} + \boldsymbol{\Delta}$ is positive
semi-definite, resulting in a stable signal prior. Further, instead of forcing
a hard binary decision for each sample, we define the notion of generalized
smoothness on graph that promotes ambiguity in the classifier signal. Finally,
we propose an algorithm based on iterative reweighted least squares (IRLS) that
solves the posed MAP problem efficiently. Extensive simulation results show
that our proposed algorithm outperforms both SVM variants and graph-based
classifiers using positive-edge graphs noticeably.
| Gene Cheung, Weng-Tai Su, Yu Mao, and Chia-Wen Lin | null | 1611.04924 | null | null |
Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box
Models | cs.LG stat.ML | 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 iterative procedure, based on orthogonal projection of input
attributes, for enabling interpretability of black-box predictive models.
Through our iterative procedure, one can quantify the relative dependence of a
black-box model on its input attributes.The relative significance of the inputs
to a predictive model can then be used to assess the fairness (or
discriminatory extent) of such a model.
| Julius Adebayo, Lalana Kagal | null | 1611.04967 | null | null |
Oracle Complexity of Second-Order Methods for Finite-Sum Problems | math.OC cs.LG stat.ML | Finite-sum optimization problems are ubiquitous in machine learning, and are
commonly solved using first-order methods which rely on gradient computations.
Recently, there has been growing interest in \emph{second-order} methods, which
rely on both gradients and Hessians. In principle, second-order methods can
require much fewer iterations than first-order methods, and hold the promise
for more efficient algorithms. Although computing and manipulating Hessians is
prohibitive for high-dimensional problems in general, the Hessians of
individual functions in finite-sum problems can often be efficiently computed,
e.g. because they possess a low-rank structure. Can second-order information
indeed be used to solve such problems more efficiently? In this paper, we
provide evidence that the answer -- perhaps surprisingly -- is negative, at
least in terms of worst-case guarantees. However, we also discuss what
additional assumptions and algorithmic approaches might potentially circumvent
this negative result.
| Yossi Arjevani and Ohad Shamir | null | 1611.04982 | null | null |
PixelVAE: A Latent Variable Model for Natural Images | cs.LG | Natural image modeling is a landmark challenge of unsupervised learning.
Variational Autoencoders (VAEs) learn a useful latent representation and model
global structure well but have difficulty capturing small details. PixelCNN
models details very well, but lacks a latent code and is difficult to scale for
capturing large structures. We present PixelVAE, a VAE model with an
autoregressive decoder based on PixelCNN. Our model requires very few expensive
autoregressive layers compared to PixelCNN and learns latent codes that are
more compressed than a standard VAE while still capturing most non-trivial
structure. Finally, we extend our model to a hierarchy of latent variables at
different scales. Our model achieves state-of-the-art performance on binarized
MNIST, competitive performance on 64x64 ImageNet, and high-quality samples on
the LSUN bedrooms dataset.
| Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga,
Francesco Visin, David Vazquez, Aaron Courville | null | 1611.05013 | null | null |
Probabilistic Failure Analysis in Model Validation & Verification | cs.SE cs.LG | Automated fault localization is an important issue in model validation and
verification. It helps the end users in analyzing the origin of failure. In
this work, we show the early experiments with probabilistic analysis approaches
in fault localization. Inspired by the Kullback-Leibler Divergence from
Bayesian probabilistic theory, we propose a suspiciousness factor to compute
the fault contribution for the transitions in the reachability graph of model
checking, using which to rank the potential faulty transitions. To
automatically locate design faults in the simulation model of detailed design,
we propose to use the statistical model Hidden Markov Model (HMM), which
provides statistically identical information to component's real behavior. The
core of this method is a fault localization algorithm that gives out the set of
suspicious ranked faulty components and a backward algorithm that computes the
matching degree between the HMM and the simulation model to evaluate the
confidence degree of the localization conclusion.
| Ning Ge, Marc Pantel, Xavier Cr\'egut | null | 1611.05083 | null | null |
Learning Dexterous Manipulation Policies from Experience and Imitation | cs.LG cs.RO cs.SY | We explore learning-based approaches for feedback control of a dexterous
five-finger hand performing non-prehensile manipulation. First, we learn local
controllers that are able to perform the task starting at a predefined initial
state. These controllers are constructed using trajectory optimization with
respect to locally-linear time-varying models learned directly from sensor
data. In some cases, we initialize the optimizer with human demonstrations
collected via teleoperation in a virtual environment. We demonstrate that such
controllers can perform the task robustly, both in simulation and on the
physical platform, for a limited range of initial conditions around the trained
starting state. We then consider two interpolation methods for generalizing to
a wider range of initial conditions: deep learning, and nearest neighbors. We
find that nearest neighbors achieve higher performance. Nevertheless, the
neural network has its advantages: it uses only tactile and proprioceptive
feedback but no visual feedback about the object (i.e. it performs the task
blind) and learns a time-invariant policy. In contrast, the nearest neighbors
method switches between time-varying local controllers based on the proximity
of initial object states sensed via motion capture. While both generalization
methods leave room for improvement, our work shows that (i) local
trajectory-based controllers for complex non-prehensile manipulation tasks can
be constructed from surprisingly small amounts of training data, and (ii)
collections of such controllers can be interpolated to form more global
controllers. Results are summarized in the supplementary video:
https://youtu.be/E0wmO6deqjo
| Vikash Kumar, Abhishek Gupta, Emanuel Todorov and Sergey Levine | null | 1611.05095 | null | null |
Machine Learning Approach for Skill Evaluation in Robotic-Assisted
Surgery | cs.LG stat.ML | 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 frameworks have arisen. In this paper, we applied machine
learning methods to automatically evaluate performance of the surgeon in RMIS.
Six important movement features were used in the evaluation including
completion time, path length, depth perception, speed, smoothness and
curvature. Different classification methods applied to discriminate expert and
novice surgeons. We test our method on real surgical data for suturing task and
compare the classification result with the ground truth data (obtained by
manual labeling). The experimental results show that the proposed framework can
classify surgical skill level with relatively high accuracy of 85.7%. This
study demonstrates the ability of machine learning methods to automatically
classify expert and novice surgeons using movement features for different RMIS
tasks. Due to the simplicity and generalizability of the introduced
classification method, it is easy to implement in existing trainers.
| Mahtab J. Fard, Sattar Ameri, Ratna B. Chinnam, Abhilash K. Pandya,
Michael D. Klein, and R. Darin Ellis | null | 1611.05136 | null | null |
S3Pool: Pooling with Stochastic Spatial Sampling | cs.LG cs.CV | Feature pooling layers (e.g., max pooling) in convolutional neural networks
(CNNs) serve the dual purpose of providing increasingly abstract
representations as well as yielding computational savings in subsequent
convolutional layers. We view the pooling operation in CNNs as a two-step
procedure: first, a pooling window (e.g., $2\times 2$) slides over the feature
map with stride one which leaves the spatial resolution intact, and second,
downsampling is performed by selecting one pixel from each non-overlapping
pooling window in an often uniform and deterministic (e.g., top-left) manner.
Our starting point in this work is the observation that this regularly spaced
downsampling arising from non-overlapping windows, although intuitive from a
signal processing perspective (which has the goal of signal reconstruction), is
not necessarily optimal for \emph{learning} (where the goal is to generalize).
We study this aspect and propose a novel pooling strategy with stochastic
spatial sampling (S3Pool), where the regular downsampling is replaced by a more
general stochastic version. We observe that this general stochasticity acts as
a strong regularizer, and can also be seen as doing implicit data augmentation
by introducing distortions in the feature maps. We further introduce a
mechanism to control the amount of distortion to suit different datasets and
architectures. To demonstrate the effectiveness of the proposed approach, we
perform extensive experiments on several popular image classification
benchmarks, observing excellent improvements over baseline models. Experimental
code is available at https://github.com/Shuangfei/s3pool.
| Shuangfei Zhai, Hui Wu, Abhishek Kumar, Yu Cheng, Yongxi Lu, Zhongfei
Zhang, Rogerio Feris | null | 1611.05138 | null | null |
Training Spiking Deep Networks for Neuromorphic Hardware | cs.NE cs.LG | We describe a method to train spiking deep networks that can be run using
leaky integrate-and-fire (LIF) neurons, achieving state-of-the-art results for
spiking LIF networks on five datasets, including the large ImageNet ILSVRC-2012
benchmark. Our method for transforming deep artificial neural networks into
spiking networks is scalable and works with a wide range of neural
nonlinearities. We achieve these results by softening the neural response
function, such that its derivative remains bounded, and by training the network
with noise to provide robustness against the variability introduced by spikes.
Our analysis shows that implementations of these networks on neuromorphic
hardware will be many times more power-efficient than the equivalent
non-spiking networks on traditional hardware.
| Eric Hunsberger, Chris Eliasmith | 10.13140/RG.2.2.10967.06566 | 1611.05141 | null | null |
A Semi-Markov Switching Linear Gaussian Model for Censored Physiological
Data | cs.LG stat.ML | 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
inpatients' physiol- ogy. The model captures the patients' latent clinical
states and their corresponding observable lab tests and vital signs. We present
an efficient unsupervised learn- ing algorithm that capitalizes on the
informatively censored data in the electronic health records (EHR) to learn the
parameters of the SSLGM; the learned model is then used to assess the new
inpatients' risk for clinical deterioration in an online fashion, allowing for
timely ICU admission. Experiments conducted on a het- erogeneous cohort of
6,094 patients admitted to a large academic medical center show that the
proposed model significantly outperforms the currently deployed risk scores
such as Rothman index, MEWS, SOFA and APACHE.
| Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar | null | 1611.05146 | null | null |
Net-Trim: Convex Pruning of Deep Neural Networks with Performance
Guarantee | cs.LG stat.ML | 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 trained network layer-wise, removing connections at each layer
by solving a convex optimization program. This program seeks a sparse set of
weights at each layer that keeps the layer inputs and outputs consistent with
the originally trained model. The algorithms and associated analysis are
applicable to neural networks operating with the rectified linear unit (ReLU)
as the nonlinear activation. We present both parallel and cascade versions of
the algorithm. While the latter can achieve slightly simpler models with the
same generalization performance, the former can be computed in a distributed
manner. In both cases, Net-Trim significantly reduces the number of connections
in the network, while also providing enough regularization to slightly reduce
the generalization error. We also provide a mathematical analysis of the
consistency between the initial network and the retrained model. To analyze the
model sample complexity, we derive the general sufficient conditions for the
recovery of a sparse transform matrix. For a single layer taking independent
Gaussian random vectors of length $N$ as inputs, we show that if the network
response can be described using a maximum number of $s$ non-zero weights per
node, these weights can be learned from $\mathcal{O}(s\log N)$ samples.
| Alireza Aghasi, Afshin Abdi, Nam Nguyen, Justin Romberg | null | 1611.05162 | null | null |
Graph Learning from Data under Structural and Laplacian Constraints | cs.LG stat.ML | 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 interpretations and (iii) associated algorithms. Specifically,
graph learning problems are posed as estimation of graph Laplacian matrices
from some observed data under given structural constraints (e.g., graph
connectivity and sparsity level). From a probabilistic perspective, the
problems of interest correspond to maximum a posteriori (MAP) parameter
estimation of Gaussian-Markov random field (GMRF) models, whose precision
(inverse covariance) is a graph Laplacian matrix. For the proposed graph
learning problems, specialized algorithms are developed by incorporating the
graph Laplacian and structural constraints. The experimental results
demonstrate that the proposed algorithms outperform the current
state-of-the-art methods in terms of accuracy and computational efficiency.
| Hilmi E. Egilmez, Eduardo Pavez, Antonio Ortega | null | 1611.05181 | null | null |
Bayesian optimization of hyper-parameters in reservoir computing | cs.LG | We describe a method for searching the optimal hyper-parameters in reservoir
computing, which consists of a Gaussian process with Bayesian optimization. It
provides an alternative to other frequently used optimization methods such as
grid, random, or manual search. In addition to a set of optimal
hyper-parameters, the method also provides a probability distribution of the
cost function as a function of the hyper-parameters. We apply this method to
two types of reservoirs: nonlinear delay nodes and echo state networks. It
shows excellent performance on all considered benchmarks, either matching or
significantly surpassing results found in the literature. In general, the
algorithm achieves optimal results in fewer iterations when compared to other
optimization methods. We have optimized up to six hyper-parameters
simultaneously, which would have been infeasible using, e.g., grid search. Due
to its automated nature, this method significantly reduces the need for expert
knowledge when optimizing the hyper-parameters in reservoir computing. Existing
software libraries for Bayesian optimization, such as Spearmint, make the
implementation of the algorithm straightforward. A fork of the Spearmint
framework along with a tutorial on how to use it in practice is available at
https://bitbucket.org/uhasseltmachinelearning/spearmint/
| Jan Yperman, Thijs Becker | null | 1611.05193 | null | null |
Deep Variational Inference Without Pixel-Wise Reconstruction | stat.ML cs.CV cs.LG | Variational autoencoders (VAEs), that are built upon deep neural networks
have emerged as popular generative models in computer vision. Most of the work
towards improving variational autoencoders has focused mainly on making the
approximations to the posterior flexible and accurate, leading to tremendous
progress. However, there have been limited efforts to replace pixel-wise
reconstruction, which have known shortcomings. In this work, we use real-valued
non-volume preserving transformations (real NVP) to exactly compute the
conditional likelihood of the data given the latent distribution. We show that
a simple VAE with this form of reconstruction is competitive with complicated
VAE structures, on image modeling tasks. As part of our model, we develop
powerful conditional coupling layers that enable real NVP to learn with fewer
intermediate layers.
| Siddharth Agrawal, Ambedkar Dukkipati | null | 1611.05209 | null | null |
A Learning Scheme for Microgrid Islanding and Reconnection | cs.LG cs.SY | This paper introduces a potential learning scheme that can dynamically
predict the stability of the reconnection of sub-networks to a main grid. As
the future electrical power systems tend towards smarter and greener
technology, the deployment of self sufficient networks, or microgrids, becomes
more likely. Microgrids may operate on their own or synchronized with the main
grid, thus control methods need to take into account islanding and reconnecting
of said networks. The ability to optimally and safely reconnect a portion of
the grid is not well understood and, as of now, limited to raw synchronization
between interconnection points. A support vector machine (SVM) leveraging
real-time data from phasor measurement units (PMUs) is proposed to predict in
real time whether the reconnection of a sub-network to the main grid would lead
to stability or instability. A dynamics simulator fed with pre-acquired system
parameters is used to create training data for the SVM in various operating
states. The classifier was tested on a variety of cases and operating points to
ensure diversity. Accuracies of approximately 85% were observed throughout most
conditions when making dynamic predictions of a given network.
| Carter Lassetter, Eduardo Cotilla-Sanchez, Jinsub Kim | null | 1611.05317 | null | null |
Approximating Wisdom of Crowds using K-RBMs | cs.LG | An important way to make large training sets is to gather noisy labels from
crowds of non experts. We propose a method to aggregate noisy labels collected
from a crowd of workers or annotators. Eliciting labels is important in tasks
such as judging web search quality and rating products. Our method assumes that
labels are generated by a probability distribution over items and labels. We
formulate the method by drawing parallels between Gaussian Mixture Models
(GMMs) and Restricted Boltzmann Machines (RBMs) and show that the problem of
vote aggregation can be viewed as one of clustering. We use K-RBMs to perform
clustering. We finally show some empirical evaluations over real datasets.
| Abhay Gupta | null | 1611.0534 | null | null |
Fast On-Line Kernel Density Estimation for Active Object Localization | cs.CV cs.LG | A major goal of computer vision is to enable computers to interpret visual
situations---abstract concepts (e.g., "a person walking a dog," "a crowd
waiting for a bus," "a picnic") whose image instantiations are linked more by
their common spatial and semantic structure than by low-level visual
similarity. In this paper, we propose a novel method for prior learning and
active object localization for this kind of knowledge-driven search in static
images. In our system, prior situation knowledge is captured by a set of
flexible, kernel-based density estimations---a situation model---that represent
the expected spatial structure of the given situation. These estimations are
efficiently updated by information gained as the system searches for relevant
objects, allowing the system to use context as it is discovered to narrow the
search.
More specifically, at any given time in a run on a test image, our system
uses image features plus contextual information it has discovered to identify a
small subset of training images---an importance cluster---that is deemed most
similar to the given test image, given the context. This subset is used to
generate an updated situation model in an on-line fashion, using an efficient
multipole expansion technique.
As a proof of concept, we apply our algorithm to a highly varied and
challenging dataset consisting of instances of a "dog-walking" situation. Our
results support the hypothesis that dynamically-rendered, context-based
probability models can support efficient object localization in visual
situations. Moreover, our approach is general enough to be applied to diverse
machine learning paradigms requiring interpretable, probabilistic
representations generated from partially observed data.
| Anthony D. Rhodes, Max H. Quinn, and Melanie Mitchell | null | 1611.05369 | null | null |
DeepCas: an End-to-end Predictor of Information Cascades | cs.SI cs.LG | Information cascades, effectively facilitated by most social network
platforms, are recognized as a major factor in almost every social success and
disaster in these networks. Can cascades be predicted? While many believe that
they are inherently unpredictable, recent work has shown that some key
properties of information cascades, such as size, growth, and shape, can be
predicted by a machine learning algorithm that combines many features. These
predictors all depend on a bag of hand-crafting features to represent the
cascade network and the global network structure. Such features, always
carefully and sometimes mysteriously designed, are not easy to extend or to
generalize to a different platform or domain.
Inspired by the recent successes of deep learning in multiple data mining
tasks, we investigate whether an end-to-end deep learning approach could
effectively predict the future size of cascades. Such a method automatically
learns the representation of individual cascade graphs in the context of the
global network structure, without hand-crafted features and heuristics. We find
that node embeddings fall short of predictive power, and it is critical to
learn the representation of a cascade graph as a whole. We present algorithms
that learn the representation of cascade graphs in an end-to-end manner, which
significantly improve the performance of cascade prediction over strong
baselines that include feature based methods, node embedding methods, and graph
kernel methods. Our results also provide interesting implications for cascade
prediction in general.
| Cheng Li, Jiaqi Ma, Xiaoxiao Guo, and Qiaozhu Mei | null | 1611.05373 | null | null |
Fully-adaptive Feature Sharing in Multi-Task Networks with Applications
in Person Attribute Classification | cs.CV cs.LG | Multi-task learning aims to improve generalization performance of multiple
prediction tasks by appropriately sharing relevant information across them. In
the context of deep neural networks, this idea is often realized by
hand-designed network architectures with layers that are shared across tasks
and branches that encode task-specific features. However, the space of possible
multi-task deep architectures is combinatorially large and often the final
architecture is arrived at by manual exploration of this space subject to
designer's bias, which can be both error-prone and tedious. In this work, we
propose a principled approach for designing compact multi-task deep learning
architectures. Our approach starts with a thin network and dynamically widens
it in a greedy manner during training using a novel criterion that promotes
grouping of similar tasks together. Our Extensive evaluation on person
attributes classification tasks involving facial and clothing attributes
suggests that the models produced by the proposed method are fast, compact and
can closely match or exceed the state-of-the-art accuracy from strong baselines
by much more expensive models.
| Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi,
Rogerio Feris | null | 1611.05377 | null | null |
Spectral Convolution Networks | cs.LG stat.ML | 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 operations such as activation, pooling, and dropout. We show,
mathematically, how convolution and activation can both be implemented in the
frequency domain using either the Fourier or Laplace transformation. The main
contributions are a description of spectral activation under the Fourier
transform and a further description of an efficient algorithm for computing
both convolution and activation under the Laplace transform. By computing both
the convolution and activation functions in the frequency domain, we can reduce
the number of transforms required, as well as reducing overall complexity. Our
description of a spectral activation function, together with existing spectral
analogs of other network functions may then be used to compose a fully spectral
implementation of a convolution network.
| Maria Francesca and Arthur Hughes and David Gregg | null | 1611.05378 | null | null |
Reinforcement Learning with Unsupervised Auxiliary Tasks | cs.LG cs.NE | Deep reinforcement learning agents have achieved state-of-the-art results by
directly maximising cumulative reward. However, environments contain a much
wider variety of possible training signals. In this paper, we introduce an
agent that also maximises many other pseudo-reward functions simultaneously by
reinforcement learning. All of these tasks share a common representation that,
like unsupervised learning, continues to develop in the absence of extrinsic
rewards. We also introduce a novel mechanism for focusing this representation
upon extrinsic rewards, so that learning can rapidly adapt to the most relevant
aspects of the actual task. Our agent significantly outperforms the previous
state-of-the-art on Atari, averaging 880\% expert human performance, and a
challenging suite of first-person, three-dimensional \emph{Labyrinth} tasks
leading to a mean speedup in learning of 10$\times$ and averaging 87\% expert
human performance on Labyrinth.
| Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul,
Joel Z Leibo, David Silver, Koray Kavukcuoglu | null | 1611.05397 | null | null |
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 stat.ML | 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 models at end-to-end low precision with provable guarantees?
Can this lead to consistent order-of-magnitude speedups? We present a framework
called ZipML to answer these questions. For linear models, the answer is yes.
We develop a simple framework based on one simple but novel strategy called
double sampling. Our framework is able to execute training at low precision
with no bias, guaranteeing convergence, whereas naive quantization would
introduce significant bias. We validate our framework across a range of
applications, and show that it enables an FPGA prototype that is up to 6.5x
faster than an implementation using full 32-bit precision. We further develop a
variance-optimal stochastic quantization strategy and show that it can make a
significant difference in a variety of settings. When applied to linear models
together with double sampling, we save up to another 1.7x in data movement
compared with uniform quantization. When training deep networks with quantized
models, we achieve higher accuracy than the state-of-the-art XNOR-Net. Finally,
we extend our framework through approximation to non-linear models, such as
SVM. We show that, although using low-precision data induces bias, we can
appropriately bound and control the bias. We find in practice 8-bit precision
is often sufficient to converge to the correct solution. Interestingly,
however, in practice we notice that our framework does not always outperform
the naive rounding approach. We discuss this negative result in detail.
| Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang | null | 1611.05402 | null | null |
Composing Music with Grammar Argumented Neural Networks and Note-Level
Encoding | cs.LG cs.AI cs.SD | Creating aesthetically pleasing pieces of art, including music, has been a
long-term goal for artificial intelligence research. Despite recent successes
of long-short term memory (LSTM) recurrent neural networks (RNNs) in sequential
learning, LSTM neural networks have not, by themselves, been able to generate
natural-sounding music conforming to music theory. To transcend this
inadequacy, we put forward a novel method for music composition that combines
the LSTM with Grammars motivated by music theory. The main tenets of music
theory are encoded as grammar argumented (GA) filters on the training data,
such that the machine can be trained to generate music inheriting the
naturalness of human-composed pieces from the original dataset while adhering
to the rules of music theory. Unlike previous approaches, pitches and durations
are encoded as one semantic entity, which we refer to as note-level encoding.
This allows easy implementation of music theory grammars, as well as closer
emulation of the thinking pattern of a musician. Although the GA rules are
applied to the training data and never directly to the LSTM music generation,
our machine still composes music that possess high incidences of diatonic scale
notes, small pitch intervals and chords, in deference to music theory.
| Zheng Sun, Jiaqi Liu, Zewang Zhang, Jingwen Chen, Zhao Huo, Ching Hua
Lee, and Xiao Zhang | 10.23919/APSIPA.2018.8659792 | 1611.05416 | null | null |
Solving Cold-Start Problem in Large-scale Recommendation Engines: A Deep
Learning Approach | cs.IR cs.LG | Collaborative Filtering (CF) is widely used in large-scale recommendation
engines because of its efficiency, accuracy and scalability. However, in
practice, the fact that recommendation engines based on CF require interactions
between users and items before making recommendations, make it inappropriate
for new items which haven't been exposed to the end users to interact with.
This is known as the cold-start problem. In this paper we introduce a novel
approach which employs deep learning to tackle this problem in any CF based
recommendation engine. One of the most important features of the proposed
technique is the fact that it can be applied on top of any existing CF based
recommendation engine without changing the CF core. We successfully applied
this technique to overcome the item cold-start problem in Careerbuilder's CF
based recommendation engine. Our experiments show that the proposed technique
is very efficient to resolve the cold-start problem while maintaining high
accuracy of the CF recommendations.
| Jianbo Yuan, Walid Shalaby, Mohammed Korayem, David Lin, Khalifeh
AlJadda, and Jiebo Luo | null | 1611.0548 | null | null |
Algebraic multigrid support vector machines | stat.ML cs.DS cs.LG stat.CO | The support vector machine is a flexible optimization-based technique widely
used for classification problems. In practice, its training part becomes
computationally expensive on large-scale data sets because of such reasons as
the complexity and number of iterations in parameter fitting methods,
underlying optimization solvers, and nonlinearity of kernels. We introduce a
fast multilevel framework for solving support vector machine models that is
inspired by the algebraic multigrid. Significant improvement in the running has
been achieved without any loss in the quality. The proposed technique is highly
beneficial on imbalanced sets. We demonstrate computational results on publicly
available and industrial data sets.
| Ehsan Sadrfaridpour, Sandeep Jeereddy, Ken Kennedy, Andre Luckow,
Talayeh Razzaghi, Ilya Safro | null | 1611.05487 | null | null |
Robust Hashing for Multi-View Data: Jointly Learning Low-Rank Kernelized
Similarity Consensus and Hash Functions | cs.LG | Learning hash functions/codes for similarity search over multi-view data is
attracting increasing attention, where similar hash codes are assigned to the
data objects characterizing consistently neighborhood relationship across
views. Traditional methods in this category inherently suffer three
limitations: 1) they commonly adopt a two-stage scheme where similarity matrix
is first constructed, followed by a subsequent hash function learning; 2) these
methods are commonly developed on the assumption that data samples with
multiple representations are noise-free,which is not practical in real-life
applications; 3) they often incur cumbersome training model caused by the
neighborhood graph construction using all $N$ points in the database ($O(N)$).
In this paper, we motivate the problem of jointly and efficiently training the
robust hash functions over data objects with multi-feature representations
which may be noise corrupted. To achieve both the robustness and training
efficiency, we propose an approach to effectively and efficiently learning
low-rank kernelized \footnote{We use kernelized similarity rather than kernel,
as it is not a squared symmetric matrix for data-landmark affinity matrix.}
hash functions shared across views. Specifically, we utilize landmark graphs to
construct tractable similarity matrices in multi-views to automatically
discover neighborhood structure in the data. To learn robust hash functions, a
latent low-rank kernel function is used to construct hash functions in order to
accommodate linearly inseparable data. In particular, a latent kernelized
similarity matrix is recovered by rank minimization on multiple kernel-based
similarity matrices. Extensive experiments on real-world multi-view datasets
validate the efficacy of our method in the presence of error corruptions.
| Lin Wu, Yang Wang | null | 1611.05521 | null | null |
Automatic Node Selection for Deep Neural Networks using Group Lasso
Regularization | cs.CL cs.LG stat.ML | We examine the effect of the Group Lasso (gLasso) regularizer in selecting
the salient nodes of Deep Neural Network (DNN) hidden layers by applying a
DNN-HMM hybrid speech recognizer to TED Talks speech data. We test two types of
gLasso regularization, one for outgoing weight vectors and another for incoming
weight vectors, as well as two sizes of DNNs: 2048 hidden layer nodes and 4096
nodes. Furthermore, we compare gLasso and L2 regularizers. Our experiment
results demonstrate that our DNN training, in which the gLasso regularizer was
embedded, successfully selected the hidden layer nodes that are necessary and
sufficient for achieving high classification power.
| Tsubasa Ochiai, Shigeki Matsuda, Hideyuki Watanabe, Shigeru Katagiri | null | 1611.05527 | null | null |
DelugeNets: Deep Networks with Efficient and Flexible Cross-layer
Information Inflows | cs.CV cs.LG cs.NE | Deluge Networks (DelugeNets) are deep neural networks which efficiently
facilitate massive cross-layer information inflows from preceding layers to
succeeding layers. The connections between layers in DelugeNets are established
through cross-layer depthwise convolutional layers with learnable filters,
acting as a flexible yet efficient selection mechanism. DelugeNets can
propagate information across many layers with greater flexibility and utilize
network parameters more effectively compared to ResNets, whilst being more
efficient than DenseNets. Remarkably, a DelugeNet model with just model
complexity of 4.31 GigaFLOPs and 20.2M network parameters, achieve
classification errors of 3.76% and 19.02% on CIFAR-10 and CIFAR-100 dataset
respectively. Moreover, DelugeNet-122 performs competitively to ResNet-200 on
ImageNet dataset, despite costing merely half of the computations needed by the
latter.
| Jason Kuen, Xiangfei Kong, Gang Wang, Yap-Peng Tan | null | 1611.05552 | null | null |
Boosting Variational Inference | stat.ML cs.LG | 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 constrained so that it does not include the
exact posterior, even as a limit point. Thus, no matter how long VI is run, the
resulting approximation will not approach the exact posterior. We propose to
instead consider a more flexible approximating family consisting of all
possible finite mixtures of a parametric base distribution (e.g., Gaussian).
For efficient inference, we borrow ideas from gradient boosting to develop an
algorithm we call boosting variational inference (BVI). BVI iteratively
improves the current approximation by mixing it with a new component from the
base distribution family and thereby yields progressively more accurate
posterior approximations as more computing time is spent. Unlike a number of
common VI variants including mean-field VI, BVI is able to capture
multimodality, general posterior covariance, and nonstandard posterior shapes.
| Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick and David B.
Dunson | null | 1611.05559 | null | null |
Optical Flow Requires Multiple Strategies (but only one network) | cs.CV cs.LG | We show that the matching problem that underlies optical flow requires
multiple strategies, depending on the amount of image motion and other factors.
We then study the implications of this observation on training a deep neural
network for representing image patches in the context of descriptor based
optical flow. We propose a metric learning method, which selects suitable
negative samples based on the nature of the true match. This type of training
produces a network that displays multiple strategies depending on the input and
leads to state of the art results on the KITTI 2012 and KITTI 2015 optical flow
benchmarks.
| Tal Schuster, Lior Wolf and David Gadot | null | 1611.05607 | null | null |
Inverting The Generator Of A Generative Adversarial Network | cs.CV cs.LG | Generative adversarial networks (GANs) learn to synthesise new samples from a
high-dimensional distribution by passing samples drawn from a latent space
through a generative network. When the high-dimensional distribution describes
images of a particular data set, the network should learn to generate visually
similar image samples for latent variables that are close to each other in the
latent space. For tasks such as image retrieval and image classification, it
may be useful to exploit the arrangement of the latent space by projecting
images into it, and using this as a representation for discriminative tasks.
GANs often consist of multiple layers of non-linear computations, making them
very difficult to invert. This paper introduces techniques for projecting image
samples into the latent space using any pre-trained GAN, provided that the
computational graph is available. We evaluate these techniques on both MNIST
digits and Omniglot handwritten characters. In the case of MNIST digits, we
show that projections into the latent space maintain information about the
style and the identity of the digit. In the case of Omniglot characters, we
show that even characters from alphabets that have not been seen during
training may be projected well into the latent space; this suggests that this
approach may have applications in one-shot learning.
| Antonia Creswell and Anil Anthony Bharath | null | 1611.05644 | null | null |
Study on Feature Subspace of Archetypal Emotions for Speech Emotion
Recognition | cs.LG cs.AI | Feature subspace selection is an important part in speech emotion
recognition. Most of the studies are devoted to finding a feature subspace for
representing all emotions. However, some studies have indicated that the
features associated with different emotions are not exactly the same. Hence,
traditional methods may fail to distinguish some of the emotions with just one
global feature subspace. In this work, we propose a new divide and conquer idea
to solve the problem. First, the feature subspaces are constructed for all the
combinations of every two different emotions (emotion-pair). Bi-classifiers are
then trained on these feature subspaces respectively. The final emotion
recognition result is derived by the voting and competition method.
Experimental results demonstrate that the proposed method can get better
results than the traditional multi-classification method.
| Xi Ma, Zhiyong Wu, Jia Jia, Mingxing Xu, Helen Meng, Lianhong Cai | null | 1611.05675 | null | null |
GENESIM: genetic extraction of a single, interpretable model | stat.ML cs.LG | 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 makes ensemble techniques impractical in applications
where decision support, instead of decision making, is crucial.
To bridge this gap, we present the GENESIM algorithm that transforms an
ensemble of decision trees to a single decision tree with an enhanced
predictive performance by using a genetic algorithm. We compared GENESIM to
prevalent decision tree induction and ensemble techniques using twelve publicly
available data sets. The results show that GENESIM achieves a better predictive
performance on most of these data sets than decision tree induction techniques
and a predictive performance in the same order of magnitude as the ensemble
techniques. Moreover, the resulting model of GENESIM has a very low complexity,
making it very interpretable, in contrast to ensemble techniques.
| Gilles Vandewiele, Olivier Janssens, Femke Ongenae, Filip De Turck,
Sofie Van Hoecke | null | 1611.05722 | null | null |
Unimodal Thompson Sampling for Graph-Structured Arms | cs.LG stat.ML | 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, for any node of the graph there is a path leading to the
unique node providing the maximum expected reward, along which the expected
reward is monotonically increasing. Previous results on this setting describe
the behavior of frequentist MAB algorithms. In our paper, we design a Thompson
Sampling-based algorithm whose asymptotic pseudo-regret matches the lower bound
for the considered setting. We show that -as it happens in a wide number of
scenarios- Bayesian MAB algorithms dramatically outperform frequentist ones. In
particular, we provide a thorough experimental evaluation of the performance of
our and state-of-the-art algorithms as the properties of the graph vary.
| Stefano Paladino and Francesco Trov\`o and Marcello Restelli and
Nicola Gatti | null | 1611.05724 | null | null |
Relational Multi-Manifold Co-Clustering | cs.LG | Co-clustering targets on grouping the samples (e.g., documents, users) and
the features (e.g., words, ratings) simultaneously. It employs the dual
relation and the bilateral information between the samples and features. In
many realworld applications, data usually reside on a submanifold of the
ambient Euclidean space, but it is nontrivial to estimate the intrinsic
manifold of the data space in a principled way. In this study, we focus on
improving the co-clustering performance via manifold ensemble learning, which
is able to maximally approximate the intrinsic manifolds of both the sample and
feature spaces. To achieve this, we develop a novel co-clustering algorithm
called Relational Multi-manifold Co-clustering (RMC) based on symmetric
nonnegative matrix tri-factorization, which decomposes the relational data
matrix into three submatrices. This method considers the intertype relationship
revealed by the relational data matrix, and also the intra-type information
reflected by the affinity matrices encoded on the sample and feature data
distributions. Specifically, we assume the intrinsic manifold of the sample or
feature space lies in a convex hull of some pre-defined candidate manifolds. We
want to learn a convex combination of them to maximally approach the desired
intrinsic manifold. To optimize the objective function, the multiplicative
rules are utilized to update the submatrices alternatively. Besides, both the
entropic mirror descent algorithm and the coordinate descent algorithm are
exploited to learn the manifold coefficient vector. Extensive experiments on
documents, images and gene expression data sets have demonstrated the
superiority of the proposed algorithm compared to other well-established
methods.
| Ping Li, Jiajun Bu, Chun Chen, Zhanying He, Deng Cai | 10.1109/TSMCB.2012.2234108 | 1611.05743 | null | null |
A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer
Survival | cs.LG stat.ML | 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 modalities of data derived from RNA deep-sequencing (RNA-seq)
to predict survival of cancer patients. Despite the wealth of information
available in expression profiles of cancer tumors, fulfilling the
aforementioned objective remains a big challenge, for the most part, due to the
paucity of data samples compared to the high dimension of the expression
profiles. As such, analysis of transcriptomic data modalities calls for
state-of-the-art big-data analytics techniques that can maximally use all the
available data to discover the relevant information hidden within a significant
amount of noise. In this paper, we propose a pipeline that predicts cancer
patients' survival by exploiting the structure of the input (manifold learning)
and by leveraging the unlabeled samples using Laplacian support vector
machines, a graph-based semi supervised learning (GSSL) paradigm. We show that
under certain circumstances, no single modality per se will result in the best
accuracy and by fusing different models together via a stacked generalization
strategy, we may boost the accuracy synergistically. We apply our approach to
two cancer datasets and present promising results. We maintain that a similar
pipeline can be used for predictive tasks where labeled samples are expensive
to acquire.
| Hamid Reza Hassanzadeh, John H. Phan, May D. Wang | null | 1611.05751 | null | null |
Learning to reinforcement learn | cs.LG cs.AI stat.ML | In recent years deep reinforcement learning (RL) systems have attained
superhuman performance in a number of challenging task domains. However, a
major limitation of such applications is their demand for massive amounts of
training data. A critical present objective is thus to develop deep RL methods
that can adapt rapidly to new tasks. In the present work we introduce a novel
approach to this challenge, which we refer to as deep meta-reinforcement
learning. Previous work has shown that recurrent networks can support
meta-learning in a fully supervised context. We extend this approach to the RL
setting. What emerges is a system that is trained using one RL algorithm, but
whose recurrent dynamics implement a second, quite separate RL procedure. This
second, learned RL algorithm can differ from the original one in arbitrary
ways. Importantly, because it is learned, it is configured to exploit structure
in the training domain. We unpack these points in a series of seven
proof-of-concept experiments, each of which examines a key aspect of deep
meta-RL. We consider prospects for extending and scaling up the approach, and
also point out some potentially important implications for neuroscience.
| Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z
Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, Matt Botvinick | null | 1611.05763 | null | null |
Gap Safe screening rules for sparsity enforcing penalties | stat.ML cs.LG math.OC stat.CO | In high dimensional regression settings, sparsity enforcing penalties have
proved useful to regularize the data-fitting term. A recently introduced
technique called screening rules propose to ignore some variables in the
optimization leveraging the expected sparsity of the solutions and consequently
leading to faster solvers. When the procedure is guaranteed not to discard
variables wrongly the rules are said to be safe. In this work, we propose a
unifying framework for generalized linear models regularized with standard
sparsity enforcing penalties such as $\ell_1$ or $\ell_1/\ell_2$ norms. Our
technique allows to discard safely more variables than previously considered
safe rules, particularly for low regularization parameters. Our proposed Gap
Safe rules (so called because they rely on duality gap computation) can cope
with any iterative solver but are particularly well suited to (block)
coordinate descent methods. Applied to many standard learning tasks, Lasso,
Sparse-Group Lasso, multi-task Lasso, binary and multinomial logistic
regression, etc., we report significant speed-ups compared to previously
proposed safe rules on all tested data sets.
| Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort and Joseph Salmon | null | 1611.0578 | null | null |
Data Science in Service of Performing Arts: Applying Machine Learning to
Predicting Audience Preferences | stat.AP cs.DB cs.LG | Performing arts organizations aim to enrich their communities through the
arts. To do this, they strive to match their performance offerings to the taste
of those communities. Success relies on understanding audience preference and
predicting their behavior. Similar to most e-commerce or digital entertainment
firms, arts presenters need to recommend the right performance to the right
customer at the right time. As part of the Michigan Data Science Team (MDST),
we partnered with the University Musical Society (UMS), a non-profit performing
arts presenter housed in the University of Michigan, Ann Arbor. We are
providing UMS with analysis and business intelligence, utilizing historical
individual-level sales data. We built a recommendation system based on
collaborative filtering, gaining insights into the artistic preferences of
customers, along with the similarities between performances. To better
understand audience behavior, we used statistical methods from customer-base
analysis. We characterized customer heterogeneity via segmentation, and we
modeled customer cohorts to understand and predict ticket purchasing patterns.
Finally, we combined statistical modeling with natural language processing
(NLP) to explore the impact of wording in program descriptions. These ongoing
efforts provide a platform to launch targeted marketing campaigns, helping UMS
carry out its mission by allocating its resources more efficiently. Celebrating
its 138th season, UMS is a 2014 recipient of the National Medal of Arts, and it
continues to enrich communities by connecting world-renowned artists with
diverse audiences, especially students in their formative years. We aim to
contribute to that mission through data science and customer analytics.
| Jacob Abernethy (University of Michigan), Cyrus Anderson (University
of Michigan), Alex Chojnacki (University of Michigan), Chengyu Dai
(University of Michigan), John Dryden (University of Michigan), Eric Schwartz
(University of Michigan), Wenbo Shen (University of Michigan), Jonathan
Stroud (University of Michigan), Laura Wendlandt (University of Michigan),
Sheng Yang (University of Michigan), Daniel Zhang (University of Michigan) | null | 1611.05788 | null | null |
Nothing Else Matters: Model-Agnostic Explanations By Identifying
Prediction Invariance | stat.ML cs.AI cs.LG | At the core of interpretable machine learning is the question of whether
humans are able to make accurate predictions about a model's behavior. Assumed
in this question are three properties of the interpretable output: coverage,
precision, and effort. Coverage refers to how often humans think they can
predict the model's behavior, precision to how accurate humans are in those
predictions, and effort is either the up-front effort required in interpreting
the model, or the effort required to make predictions about a model's behavior.
In this work, we propose anchor-LIME (aLIME), a model-agnostic technique that
produces high-precision rule-based explanations for which the coverage
boundaries are very clear. We compare aLIME to linear LIME with simulated
experiments, and demonstrate the flexibility of aLIME with qualitative examples
from a variety of domains and tasks.
| Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin | null | 1611.05817 | null | null |
Towards a Mathematical Understanding of the Difficulty in Learning with
Feedforward Neural Networks | cs.LG cs.AI cs.NE math.OC | Training deep neural networks for solving machine learning problems is one
great challenge in the field, mainly due to its associated optimisation problem
being highly non-convex. Recent developments have suggested that many training
algorithms do not suffer from undesired local minima under certain scenario,
and consequently led to great efforts in pursuing mathematical explanations for
such observations. This work provides an alternative mathematical understanding
of the challenge from a smooth optimisation perspective. By assuming exact
learning of finite samples, sufficient conditions are identified via a critical
point analysis to ensure any local minimum to be globally minimal as well.
Furthermore, a state of the art algorithm, known as the Generalised
Gauss-Newton (GGN) algorithm, is rigorously revisited as an approximate
Newton's algorithm, which shares the property of being locally quadratically
convergent to a global minimum under the condition of exact learning.
| Hao Shen | null | 1611.05827 | null | null |
Associative Memories to Accelerate Approximate Nearest Neighbor Search | cs.LG math.PR | Nearest neighbor search is a very active field in machine learning for it
appears in many application cases, including classification and object
retrieval. In its canonical version, the complexity of the search is linear
with both the dimension and the cardinal of the collection of vectors the
search is performed in. Recently many works have focused on reducing the
dimension of vectors using quantization techniques or hashing, while providing
an approximate result. In this paper we focus instead on tackling the cardinal
of the collection of vectors. Namely, we introduce a technique that partitions
the collection of vectors and stores each part in its own associative memory.
When a query vector is given to the system, associative memories are polled to
identify which one contain the closest match. Then an exhaustive search is
conducted only on the part of vectors stored in the selected associative
memory. We study the effectiveness of the system when messages to store are
generated from i.i.d. uniform $\pm$1 random variables or 0-1 sparse i.i.d.
random variables. We also conduct experiment on both synthetic data and real
data and show it is possible to achieve interesting trade-offs between
complexity and accuracy.
| Vincent Gripon, Matthias L\"owe, Franck Vermet | null | 1611.05898 | null | null |
"Influence Sketching": Finding Influential Samples In Large-Scale
Regressions | stat.ML cs.LG | 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 to inspect, we develop a new scalable version of Cook's distance,
a classical statistical technique for identifying samples which unusually
strongly impact the fit of a regression model (and its downstream predictions).
In order to scale this technique up to very large and high-dimensional
datasets, we introduce a new algorithm which we call "influence sketching."
Influence sketching embeds random projections within the influence computation;
in particular, the influence score is calculated using the randomly projected
pseudo-dataset from the post-convergence Generalized Linear Model (GLM). We
validate that influence sketching can reliably and successfully discover
influential samples by applying the technique to a malware detection dataset of
over 2 million executable files, each represented with almost 100,000 features.
For example, we find that randomly deleting approximately 10% of training
samples reduces predictive accuracy only slightly from 99.47% to 99.45%,
whereas deleting the same number of samples with high influence sketch scores
reduces predictive accuracy all the way down to 90.24%. Moreover, we find that
influential samples are especially likely to be mislabeled. In the case study,
we manually inspect the most influential samples, and find that influence
sketching pointed us to new, previously unidentified pieces of malware.
| Mike Wojnowicz, Ben Cruz, Xuan Zhao, Brian Wallace, Matt Wolff, Jay
Luan, and Caleb Crable | 10.1109/BigData.2016.7841024 | 1611.05923 | null | null |
Analysis of a Design Pattern for Teaching with Features and Labels | cs.AI cs.LG | We study the task of teaching a machine to classify objects using features
and labels. We introduce the Error-Driven-Featuring design pattern for teaching
using features and labels in which a teacher prefers to introduce features only
if they are needed. We analyze the potential risks and benefits of this
teaching pattern through the use of teaching protocols, illustrative examples,
and by providing bounds on the effort required for an optimal machine teacher
using a linear learning algorithm, the most commonly used type of learners in
interactive machine learning systems. Our analysis provides a deeper
understanding of potential trade-offs of using different learning algorithms
and between the effort required for featuring (creating new features) and
labeling (providing labels for objects).
| Christopher Meek, Patrice Simard, Xiaojin Zhu | null | 1611.0595 | null | null |
A Characterization of Prediction Errors | cs.LG | Understanding prediction errors and determining how to fix them is critical
to building effective predictive systems. In this paper, we delineate four
types of prediction errors and demonstrate that these four types characterize
all prediction errors. In addition, we describe potential remedies and tools
that can be used to reduce the uncertainty when trying to determine the source
of a prediction error and when trying to take action to remove a prediction
errors.
| Christopher Meek | null | 1611.05955 | null | null |
Robust and Scalable Column/Row Sampling from Corrupted Big Data | cs.LG cs.NA stat.AP stat.ML | Conventional sampling techniques fall short of drawing descriptive sketches
of the data when the data is grossly corrupted as such corruptions break the
low rank structure required for them to perform satisfactorily. In this paper,
we present new sampling algorithms which can locate the informative columns in
presence of severe data corruptions. In addition, we develop new scalable
randomized designs of the proposed algorithms. The proposed approach is
simultaneously robust to sparse corruption and outliers and substantially
outperforms the state-of-the-art robust sampling algorithms as demonstrated by
experiments conducted using both real and synthetic data.
| Mostafa Rahmani, George Atia | null | 1611.05977 | null | null |
Monte Carlo Tableau Proof Search | cs.LO cs.AI cs.LG | We study Monte Carlo Tree Search to guide proof search in tableau calculi.
This includes proposing a number of proof-state evaluation heuristics, some of
which are learnt from previous proofs. We present an implementation based on
the leanCoP prover. The system is trained and evaluated on a large suite of
related problems coming from the Mizar proof assistant, showing that it is
capable to find new and different proofs.
| Michael F\"arber, Cezary Kaliszyk, Josef Urban | 10.1007/978-3-319-63046-5_34 | 1611.0599 | null | null |
A Generalized Stochastic Variational Bayesian Hyperparameter Learning
Framework for Sparse Spectrum Gaussian Process Regression | stat.ML cs.LG | 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 an effort to advance the state of the art of sparse spectrum GP models to
achieve competitive predictive performance for massive datasets. Our
generalized framework of stochastic variational Bayesian sparse spectrum GP
(sVBSSGP) models addresses their shortcomings by adopting a Bayesian treatment
of the spectral frequencies to avoid overfitting, modeling these frequencies
jointly in its variational distribution to enable their interaction a
posteriori, and exploiting local data for boosting the predictive performance.
However, such structural improvements result in a variational lower bound that
is intractable to be optimized. To resolve this, we exploit a variational
parameterization trick to make it amenable to stochastic optimization.
Interestingly, the resulting stochastic gradient has a linearly decomposable
structure that can be exploited to refine our stochastic optimization method to
incur constant time per iteration while preserving its property of being an
unbiased estimator of the exact gradient of the variational lower bound.
Empirical evaluation on real-world datasets shows that sVBSSGP outperforms
state-of-the-art stochastic implementations of sparse GP models.
| Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low | null | 1611.0608 | null | null |
Faster variational inducing input Gaussian process classification | cs.LG cs.AI stat.ML | Gaussian processes (GP) provide a prior over functions and allow finding
complex regularities in data. Gaussian processes are successfully used for
classification/regression problems and dimensionality reduction. In this work
we consider the classification problem only. The complexity of standard methods
for GP-classification scales cubically with the size of the training dataset.
This complexity makes them inapplicable to big data problems. Therefore, a
variety of methods were introduced to overcome this limitation. In the paper we
focus on methods based on so called inducing inputs. This approach is based on
variational inference and proposes a particular lower bound for marginal
likelihood (evidence). This bound is then maximized w.r.t. parameters of kernel
function of the Gaussian process, thus fitting the model to data. The
computational complexity of this method is $O(nm^2)$, where $m$ is the number
of inducing inputs used by the model and is assumed to be substantially smaller
than the size of the dataset $n$. Recently, a new evidence lower bound for
GP-classification problem was introduced. It allows using stochastic
optimization, which makes it suitable for big data problems. However, the new
lower bound depends on $O(m^2)$ variational parameter, which makes optimization
challenging in case of big m. In this work we develop a new approach for
training inducing input GP models for classification problems. Here we use
quadratic approximation of several terms in the aforementioned evidence lower
bound, obtaining analytical expressions for optimal values of most of the
parameters in the optimization, thus sufficiently reducing the dimension of
optimization space. In our experiments we achieve as well or better results,
compared to the existing method. Moreover, our method doesn't require the user
to manually set the learning rate, making it more practical, than the existing
method.
| Pavel Izmailov and Dmitry Kropotov | null | 1611.06132 | null | null |
Compacting Neural Network Classifiers via Dropout Training | stat.ML cs.LG cs.NE | We introduce dropout compaction, a novel method for training feed-forward
neural networks which realizes the performance gains of training a large model
with dropout regularization, yet extracts a compact neural network for run-time
efficiency. In the proposed method, we introduce a sparsity-inducing prior on
the per unit dropout retention probability so that the optimizer can
effectively prune hidden units during training. By changing the prior
hyperparameters, we can control the size of the resulting network. We performed
a systematic comparison of dropout compaction and competing methods on several
real-world speech recognition tasks and found that dropout compaction achieved
comparable accuracy with fewer than 50% of the hidden units, translating to a
2.5x speedup in run-time.
| Yotaro Kubo, George Tucker, Simon Wiesler | null | 1611.06148 | null | null |
Learning Interpretability for Visualizations using Adapted Cox Models
through a User Experiment | stat.ML cs.AI cs.HC cs.LG | In order to be useful, visualizations need to be interpretable. This paper
uses a user-based approach to combine and assess quality measures in order to
better model user preferences. Results show that cluster separability measures
are outperformed by a neighborhood conservation measure, even though the former
are usually considered as intuitively representative of user motives. Moreover,
combining measures, as opposed to using a single measure, further improves
prediction performances.
| Adrien Bibal and Benoit Fr\'enay | null | 1611.06175 | null | null |
Variable Computation in Recurrent Neural Networks | stat.ML cs.AI cs.CL cs.LG | Recurrent neural networks (RNNs) have been used extensively and with
increasing success to model various types of sequential data. Much of this
progress has been achieved through devising recurrent units and architectures
with the flexibility to capture complex statistics in the data, such as long
range dependency or localized attention phenomena. However, while many
sequential data (such as video, speech or language) can have highly variable
information flow, most recurrent models still consume input features at a
constant rate and perform a constant number of computations per time step,
which can be detrimental to both speed and model capacity. In this paper, we
explore a modification to existing recurrent units which allows them to learn
to vary the amount of computation they perform at each step, without prior
knowledge of the sequence's time structure. We show experimentally that not
only do our models require fewer operations, they also lead to better
performance overall on evaluation tasks.
| Yacine Jernite, Edouard Grave, Armand Joulin, Tomas Mikolov | null | 1611.06188 | null | null |
Visualizing and Understanding Curriculum Learning for Long Short-Term
Memory Networks | cs.CL cs.LG cs.NE | Curriculum Learning emphasizes the order of training instances in a
computational learning setup. The core hypothesis is that simpler instances
should be learned early as building blocks to learn more complex ones. Despite
its usefulness, it is still unknown how exactly the internal representation of
models are affected by curriculum learning. In this paper, we study the effect
of curriculum learning on Long Short-Term Memory (LSTM) networks, which have
shown strong competency in many Natural Language Processing (NLP) problems. Our
experiments on sentiment analysis task and a synthetic task similar to sequence
prediction tasks in NLP show that curriculum learning has a positive effect on
the LSTM's internal states by biasing the model towards building constructive
representations i.e. the internal representation at the previous timesteps are
used as building blocks for the final prediction. We also find that smaller
models significantly improves when they are trained with curriculum learning.
Lastly, we show that curriculum learning helps more when the amount of training
data is limited.
| Volkan Cirik, Eduard Hovy, Louis-Philippe Morency | null | 1611.06204 | null | null |
GaDei: On Scale-up Training As A Service For Deep Learning | stat.ML cs.DC cs.LG | Deep learning (DL) training-as-a-service (TaaS) is an important emerging
industrial workload. The unique challenge of TaaS is that it must satisfy a
wide range of customers who have no experience and resources to tune DL
hyper-parameters, and meticulous tuning for each user's dataset is
prohibitively expensive. Therefore, TaaS hyper-parameters must be fixed with
values that are applicable to all users. IBM Watson Natural Language Classifier
(NLC) service, the most popular IBM cognitive service used by thousands of
enterprise-level clients around the globe, is a typical TaaS service. By
evaluating the NLC workloads, we show that only the conservative
hyper-parameter setup (e.g., small mini-batch size and small learning rate) can
guarantee acceptable model accuracy for a wide range of customers. We further
justify theoretically why such a setup guarantees better model convergence in
general. Unfortunately, the small mini-batch size causes a high volume of
communication traffic in a parameter-server based system. We characterize the
high communication bandwidth requirement of TaaS using representative
industrial deep learning workloads and demonstrate that none of the
state-of-the-art scale-up or scale-out solutions can satisfy such a
requirement. We then present GaDei, an optimized shared-memory based scale-up
parameter server design. We prove that the designed protocol is deadlock-free
and it processes each gradient exactly once. Our implementation is evaluated on
both commercial benchmarks and public benchmarks to demonstrate that it
significantly outperforms the state-of-the-art parameter-server based
implementation while maintaining the required accuracy and our implementation
reaches near the best possible runtime performance, constrained only by the
hardware limitation. Furthermore, to the best of our knowledge, GaDei is the
only scale-up DL system that provides fault-tolerance.
| Wei Zhang, Minwei Feng, Yunhui Zheng, Yufei Ren, Yandong Wang, Ji Liu,
Peng Liu, Bing Xiang, Li Zhang, Bowen Zhou, Fei Wang | null | 1611.06213 | null | null |
Foundations of Structural Causal Models with Cycles and Latent Variables | stat.ME cs.AI cs.LG | Structural causal models (SCMs), also known as (nonparametric) structural
equation models (SEMs), are widely used for causal modeling purposes. In
particular, acyclic SCMs, also known as recursive SEMs, form a well-studied
subclass of SCMs that generalize causal Bayesian networks to allow for latent
confounders. In this paper, we investigate SCMs in a more general setting,
allowing for the presence of both latent confounders and cycles. We show that
in the presence of cycles, many of the convenient properties of acyclic SCMs do
not hold in general: they do not always have a solution; they do not always
induce unique observational, interventional and counterfactual distributions; a
marginalization does not always exist, and if it exists the marginal model does
not always respect the latent projection; they do not always satisfy a Markov
property; and their graphs are not always consistent with their causal
semantics. We prove that for SCMs in general each of these properties does hold
under certain solvability conditions. Our work generalizes results for SCMs
with cycles that were only known for certain special cases so far. We introduce
the class of simple SCMs that extends the class of acyclic SCMs to the cyclic
setting, while preserving many of the convenient properties of acyclic SCMs.
With this paper we aim to provide the foundations for a general theory of
statistical causal modeling with SCMs.
| Stephan Bongers, Patrick Forr\'e, Jonas Peters, Joris M. Mooij | 10.1214/21-AOS2064 | 1611.06221 | null | null |
Approximate Near Neighbors for General Symmetric Norms | cs.DS cs.CG cs.LG math.MG | We show that every symmetric normed space admits an efficient nearest
neighbor search data structure with doubly-logarithmic approximation.
Specifically, for every $n$, $d = n^{o(1)}$, and every $d$-dimensional
symmetric norm $\|\cdot\|$, there exists a data structure for
$\mathrm{poly}(\log \log n)$-approximate nearest neighbor search over
$\|\cdot\|$ for $n$-point datasets achieving $n^{o(1)}$ query time and
$n^{1+o(1)}$ space. The main technical ingredient of the algorithm is a
low-distortion embedding of a symmetric norm into a low-dimensional iterated
product of top-$k$ norms.
We also show that our techniques cannot be extended to general norms.
| Alexandr Andoni, Huy L. Nguyen, Aleksandar Nikolov, Ilya Razenshteyn,
Erik Waingarten | null | 1611.06222 | null | null |
Using LSTM recurrent neural networks for monitoring the LHC
superconducting magnets | physics.ins-det cs.LG physics.acc-ph | The superconducting LHC magnets are coupled with an electronic monitoring
system which records and analyses voltage time series reflecting their
performance. A currently used system is based on a range of preprogrammed
triggers which launches protection procedures when a misbehavior of the magnets
is detected. All the procedures used in the protection equipment were designed
and implemented according to known working scenarios of the system and are
updated and monitored by human operators.
This paper proposes a novel approach to monitoring and fault protection of
the Large Hadron Collider (LHC) superconducting magnets which employs
state-of-the-art Deep Learning algorithms. Consequently, the authors of the
paper decided to examine the performance of LSTM recurrent neural networks for
modeling of voltage time series of the magnets. In order to address this
challenging task different network architectures and hyper-parameters were used
to achieve the best possible performance of the solution. The regression
results were measured in terms of RMSE for different number of future steps and
history length taken into account for the prediction. The best result of
RMSE=0.00104 was obtained for a network of 128 LSTM cells within the internal
layer and 16 steps history buffer.
| Maciej Wielgosz and Andrzej Skocze\'n and Matej Mertik | 10.1016/j.nima.2017.06.020 | 1611.06241 | null | null |
Spikes as regularizers | cs.NE cs.LG stat.ML | We present a confidence-based single-layer feed-forward learning algorithm
SPIRAL (Spike Regularized Adaptive Learning) relying on an encoding of
activation spikes. We adaptively update a weight vector relying on confidence
estimates and activation offsets relative to previous activity. We regularize
updates proportionally to item-level confidence and weight-specific support,
loosely inspired by the observation from neurophysiology that high spike rates
are sometimes accompanied by low temporal precision. Our experiments suggest
that the new learning algorithm SPIRAL is more robust and less prone to
overfitting than both the averaged perceptron and AROW.
| Anders S{\o}gaard | null | 1611.06245 | null | null |
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a
GPU | cs.LG | We introduce a hybrid CPU/GPU version of the Asynchronous Advantage
Actor-Critic (A3C) algorithm, currently the state-of-the-art method in
reinforcement learning for various gaming tasks. We analyze its computational
traits and concentrate on aspects critical to leveraging the GPU's
computational power. We introduce a system of queues and a dynamic scheduling
strategy, potentially helpful for other asynchronous algorithms as well. Our
hybrid CPU/GPU version of A3C, based on TensorFlow, achieves a significant
speed up compared to a CPU implementation; we make it publicly available to
other researchers at https://github.com/NVlabs/GA3C .
| Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan
Kautz | null | 1611.06256 | null | null |
Deep Clustering and Conventional Networks for Music Separation: Stronger
Together | stat.ML cs.LG cs.SD | Deep clustering is the first method to handle general audio separation
scenarios with multiple sources of the same type and an arbitrary number of
sources, performing impressively in speaker-independent speech separation
tasks. However, little is known about its effectiveness in other challenging
situations such as music source separation. Contrary to conventional networks
that directly estimate the source signals, deep clustering generates an
embedding for each time-frequency bin, and separates sources by clustering the
bins in the embedding space. We show that deep clustering outperforms
conventional networks on a singing voice separation task, in both matched and
mismatched conditions, even though conventional networks have the advantage of
end-to-end training for best signal approximation, presumably because its more
flexible objective engenders better regularization. Since the strengths of deep
clustering and conventional network architectures appear complementary, we
explore combining them in a single hybrid network trained via an approach akin
to multi-task learning. Remarkably, the combination significantly outperforms
either of its components.
| Yi Luo, Zhuo Chen, John R. Hershey, Jonathan Le Roux, Nima Mesgarani | 10.1109/ICASSP.2017.7952118 | 1611.06265 | null | null |
Cross-model convolutional neural network for multiple modality data
representation | cs.LG | A novel data representation method of convolutional neural net- work (CNN) is
proposed in this paper to represent data of different modalities. We learn a
CNN model for the data of each modality to map the data of differ- ent
modalities to a common space, and regularize the new representations in the
common space by a cross-model relevance matrix. We further impose that the
class label of data points can also be predicted from the CNN representa- tions
in the common space. The learning problem is modeled as a minimiza- tion
problem, which is solved by an augmented Lagrange method (ALM) with updating
rules of Alternating direction method of multipliers (ADMM). The experiments
over benchmark of sequence data of multiple modalities show its advantage.
| Yanbin Wu, Li Wang, Fan Cui, Hongbin Zhai, Baoming Dong, Jim Jing-Yan
Wang | null | 1611.06306 | null | null |
Local minima in training of neural networks | stat.ML cs.LG cs.NE | There has been a lot of recent interest in trying to characterize the error
surface of deep models. This stems from a long standing question. Given that
deep networks are highly nonlinear systems optimized by local gradient methods,
why do they not seem to be affected by bad local minima? It is widely believed
that training of deep models using gradient methods works so well because the
error surface either has no local minima, or if they exist they need to be
close in value to the global minimum. It is known that such results hold under
very strong assumptions which are not satisfied by real models. In this paper
we present examples showing that for such theorem to be true additional
assumptions on the data, initialization schemes and/or the model classes have
to be made. We look at the particular case of finite size datasets. We
demonstrate that in this scenario one can construct counter-examples (datasets
or initialization schemes) when the network does become susceptible to bad
local minima over the weight space.
| Grzegorz Swirszcz, Wojciech Marian Czarnecki and Razvan Pascanu | null | 1611.0631 | null | null |
Learning the Number of Neurons in Deep Networks | cs.CV cs.LG cs.NE | Nowadays, the number of layers and of neurons in each layer of a deep network
are typically set manually. While very deep and wide networks have proven
effective in general, they come at a high memory and computation cost, thus
making them impractical for constrained platforms. These networks, however, are
known to have many redundant parameters, and could thus, in principle, be
replaced by more compact architectures. In this paper, we introduce an approach
to automatically determining the number of neurons in each layer of a deep
network during learning. To this end, we propose to make use of structured
sparsity during learning. More precisely, we use a group sparsity regularizer
on the parameters of the network, where each group is defined to act on a
single neuron. Starting from an overcomplete network, we show that our approach
can reduce the number of parameters by up to 80\% while retaining or even
improving the network accuracy.
| Jose M Alvarez and Mathieu Salzmann | null | 1611.06321 | null | null |
Quantized neural network design under weight capacity constraint | cs.LG cs.NE | The complexity of deep neural network algorithms for hardware implementation
can be lowered either by scaling the number of units or reducing the
word-length of weights. Both approaches, however, can accompany the performance
degradation although many types of research are conducted to relieve this
problem. Thus, it is an important question which one, between the network size
scaling and the weight quantization, is more effective for hardware
optimization. For this study, the performances of fully-connected deep neural
networks (FCDNNs) and convolutional neural networks (CNNs) are evaluated while
changing the network complexity and the word-length of weights. Based on these
experiments, we present the effective compression ratio (ECR) to guide the
trade-off between the network size and the precision of weights when the
hardware resource is limited.
| Sungho Shin, Kyuyeon Hwang, and Wonyong Sung | null | 1611.06342 | null | null |
Conservative Contextual Linear Bandits | stat.ML cs.LG | 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 contextual linear bandits that have application in many different
fields including personalized ad recommendation in online marketing. We
formulate a notion of safety for this class of algorithms. We develop a safe
contextual linear bandit algorithm, called conservative linear UCB (CLUCB),
that simultaneously minimizes its regret and satisfies the safety constraint,
i.e., maintains its performance above a fixed percentage of the performance of
a baseline strategy, uniformly over time. We prove an upper-bound on the regret
of CLUCB and show that it can be decomposed into two terms: 1) an upper-bound
for the regret of the standard linear UCB algorithm that grows with the time
horizon and 2) a constant (does not grow with the time horizon) term that
accounts for the loss of being conservative in order to satisfy the safety
constraint. We empirically show that our algorithm is safe and validate our
theoretical analysis.
| Abbas Kazerouni, Mohammad Ghavamzadeh, Yasin Abbasi-Yadkori and
Benjamin Van Roy | null | 1611.06426 | null | null |
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