categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
1611.02830
| null | null |
http://arxiv.org/pdf/1611.02830v1
|
2016-11-09T06:21:27Z
|
2016-11-09T06:21:27Z
|
Online Learning for Wireless Distributed Computing
|
There has been a growing interest for Wireless Distributed Computing (WDC), which leverages collaborative computing over multiple wireless devices. WDC enables complex applications that a single device cannot support individually. However, the problem of assigning tasks over multiple devices becomes challenging in the dynamic environments encountered in real-world settings, considering that the resource availability and channel conditions change over time in unpredictable ways due to mobility and other factors. In this paper, we formulate a task assignment problem as an online learning problem using an adversarial multi-armed bandit framework. We propose MABSTA, a novel online learning algorithm that learns the performance of unknown devices and channel qualities continually through exploratory probing and makes task assignment decisions by exploiting the gained knowledge. For maximal adaptability, MABSTA is designed to make no stochastic assumption about the environment. We analyze it mathematically and provide a worst-case performance guarantee for any dynamic environment. We also compare it with the optimal offline policy as well as other baselines via emulations on trace-data obtained from a wireless IoT testbed, and show that it offers competitive and robust performance in all cases. To the best of our knowledge, MABSTA is the first online algorithm in this domain of task assignment problems and provides provable performance guarantee.
|
[
"['Yi-Hsuan Kao' 'Kwame Wright' 'Bhaskar Krishnamachari' 'Fan Bai']"
] |
cs.NE cs.LG
| null |
1611.02854
| null | null |
http://arxiv.org/pdf/1611.02854v2
|
2017-03-05T21:03:22Z
|
2016-11-09T08:51:54Z
|
Lie-Access Neural Turing Machines
|
External neural memory structures have recently become a popular tool for
algorithmic deep learning (Graves et al. 2014, Weston et al. 2014). These
models generally utilize differentiable versions of traditional discrete
memory-access structures (random access, stacks, tapes) to provide the storage
necessary for computational tasks. In this work, we argue that these neural
memory systems lack specific structure important for relative indexing, and
propose an alternative model, Lie-access memory, that is explicitly designed
for the neural setting. In this paradigm, memory is accessed using a continuous
head in a key-space manifold. The head is moved via Lie group actions, such as
shifts or rotations, generated by a controller, and memory access is performed
by linear smoothing in key space. We argue that Lie groups provide a natural
generalization of discrete memory structures, such as Turing machines, as they
provide inverse and identity operators while maintaining differentiability. To
experiment with this approach, we implement a simplified Lie-access neural
Turing machine (LANTM) with different Lie groups. We find that this approach is
able to perform well on a range of algorithmic tasks.
|
[
"Greg Yang, Alexander M. Rush",
"['Greg Yang' 'Alexander M. Rush']"
] |
cs.CV cs.CL cs.LG
| null |
1611.02879
| null | null |
http://arxiv.org/pdf/1611.02879v1
|
2016-11-09T10:24:52Z
|
2016-11-09T10:24:52Z
|
Audio Visual Speech Recognition using Deep Recurrent Neural Networks
|
In this work, we propose a training algorithm for an audio-visual automatic
speech recognition (AV-ASR) system using deep recurrent neural network
(RNN).First, we train a deep RNN acoustic model with a Connectionist Temporal
Classification (CTC) objective function. The frame labels obtained from the
acoustic model are then used to perform a non-linear dimensionality reduction
of the visual features using a deep bottleneck network. Audio and visual
features are fused and used to train a fusion RNN. The use of bottleneck
features for visual modality helps the model to converge properly during
training. Our system is evaluated on GRID corpus. Our results show that
presence of visual modality gives significant improvement in character error
rate (CER) at various levels of noise even when the model is trained without
noisy data. We also provide a comparison of two fusion methods: feature fusion
and decision fusion.
|
[
"['Abhinav Thanda' 'Shankar M Venkatesan']",
"Abhinav Thanda, Shankar M Venkatesan"
] |
q-bio.QM cs.LG
|
10.1016/j.jbi.2017.03.006
|
1611.02945
| null | null |
http://arxiv.org/abs/1611.02945v1
|
2016-11-08T20:18:57Z
|
2016-11-08T20:18:57Z
|
Heter-LP: A heterogeneous label propagation algorithm and its
application in drug repositioning
|
Drug repositioning offers an effective solution to drug discovery, saving
both time and resources by finding new indications for existing drugs.
Typically, a drug takes effect via its protein targets in the cell. As a
result, it is necessary for drug development studies to conduct an
investigation into the interrelationships of drugs, protein targets, and
diseases. Although previous studies have made a strong case for the
effectiveness of integrative network-based methods for predicting these
interrelationships, little progress has been achieved in this regard within
drug repositioning research. Moreover, the interactions of new drugs and
targets (lacking any known targets and drugs, respectively) cannot be
accurately predicted by most established methods. In this paper, we propose a
novel semi-supervised heterogeneous label propagation algorithm named Heter-LP,
which applies both local as well as global network features for data
integration. To predict drug-target, disease-target, and drug-disease
associations, we use information about drugs, diseases, and targets as
collected from multiple sources at different levels. Our algorithm integrates
these various types of data into a heterogeneous network and implements a label
propagation algorithm to find new interactions. Statistical analyses of 10-fold
cross-validation results and experimental analysis support the effectiveness of
the proposed algorithm.
|
[
"Maryam Lotfi Shahreza, Nasser Ghadiri, Seyed Rasul Mossavi, Jaleh\n Varshosaz, James Green",
"['Maryam Lotfi Shahreza' 'Nasser Ghadiri' 'Seyed Rasul Mossavi'\n 'Jaleh Varshosaz' 'James Green']"
] |
cs.IT cs.DS cs.LG math.IT
| null |
1611.0296
| null | null | null | null | null |
A Unified Maximum Likelihood Approach for Optimal Distribution Property
Estimation
|
The advent of data science has spurred interest in estimating properties of
distributions over large alphabets. Fundamental symmetric properties such as
support size, support coverage, entropy, and proximity to uniformity, received
most attention, with each property estimated using a different technique and
often intricate analysis tools.
We prove that for all these properties, a single, simple, plug-in
estimator---profile maximum likelihood (PML)---performs as well as the best
specialized techniques. This raises the possibility that PML may optimally
estimate many other symmetric properties.
|
[
"Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh"
] |
null | null |
1611.02960
| null | null |
http://arxiv.org/pdf/1611.02960v2
|
2016-11-28T16:36:44Z
|
2016-11-09T14:59:23Z
|
A Unified Maximum Likelihood Approach for Optimal Distribution Property
Estimation
|
The advent of data science has spurred interest in estimating properties of distributions over large alphabets. Fundamental symmetric properties such as support size, support coverage, entropy, and proximity to uniformity, received most attention, with each property estimated using a different technique and often intricate analysis tools. We prove that for all these properties, a single, simple, plug-in estimator---profile maximum likelihood (PML)---performs as well as the best specialized techniques. This raises the possibility that PML may optimally estimate many other symmetric properties.
|
[
"['Jayadev Acharya' 'Hirakendu Das' 'Alon Orlitsky'\n 'Ananda Theertha Suresh']"
] |
cs.LG cs.CR stat.AP
| null |
1611.03021
| null | null |
http://arxiv.org/pdf/1611.03021v2
|
2017-08-14T18:25:34Z
|
2016-11-07T15:26:58Z
|
Attributing Hacks
|
In this paper we describe an algorithm for estimating the provenance of hacks
on websites. That is, given properties of sites and the temporal occurrence of
attacks, we are able to attribute individual attacks to joint causes and
vulnerabilities, as well as estimating the evolution of these vulnerabilities
over time. Specifically, we use hazard regression with a time-varying additive
hazard function parameterized in a generalized linear form. The activation
coefficients on each feature are continuous-time functions over time. We
formulate the problem of learning these functions as a constrained variational
maximum likelihood estimation problem with total variation penalty and show
that the optimal solution is a 0th order spline (a piecewise constant function)
with a finite number of known knots. This allows the inference problem to be
solved efficiently and at scale by solving a finite dimensional optimization
problem. Extensive experiments on real data sets show that our method
significantly outperforms Cox's proportional hazard model. We also conduct a
case study and verify that the fitted functions are indeed recovering
vulnerable features and real-life events such as the release of code to exploit
these features in hacker blogs.
|
[
"Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua\n Zheng, Jun Zhou",
"['Ziqi Liu' 'Alexander J. Smola' 'Kyle Soska' 'Yu-Xiang Wang'\n 'Qinghua Zheng' 'Jun Zhou']"
] |
cs.LG cs.NE
| null |
1611.03068
| null | null |
http://arxiv.org/pdf/1611.03068v2
|
2016-12-01T21:33:19Z
|
2016-11-09T20:12:08Z
|
Incremental Sequence Learning
|
Deep learning research over the past years has shown that by increasing the
scope or difficulty of the learning problem over time, increasingly complex
learning problems can be addressed. We study incremental learning in the
context of sequence learning, using generative RNNs in the form of multi-layer
recurrent Mixture Density Networks. While the potential of incremental or
curriculum learning to enhance learning is known, indiscriminate application of
the principle does not necessarily lead to improvement, and it is essential
therefore to know which forms of incremental or curriculum learning have a
positive effect. This research contributes to that aim by comparing three
instantiations of incremental or curriculum learning.
We introduce Incremental Sequence Learning, a simple incremental approach to
sequence learning. Incremental Sequence Learning starts out by using only the
first few steps of each sequence as training data. Each time a performance
criterion has been reached, the length of the parts of the sequences used for
training is increased.
We introduce and make available a novel sequence learning task and data set:
predicting and classifying MNIST pen stroke sequences. We find that Incremental
Sequence Learning greatly speeds up sequence learning and reaches the best test
performance level of regular sequence learning 20 times faster, reduces the
test error by 74%, and in general performs more robustly; it displays lower
variance and achieves sustained progress after all three comparison methods
have stopped improving. The other instantiations of curriculum learning do not
result in any noticeable improvement. A trained sequence prediction model is
also used in transfer learning to the task of sequence classification, where it
is found that transfer learning realizes improved classification performance
compared to methods that learn to classify from scratch.
|
[
"Edwin D. de Jong",
"['Edwin D. de Jong']"
] |
cs.LG
| null |
1611.03071
| null | null |
http://arxiv.org/pdf/1611.03071v4
|
2017-08-06T00:12:49Z
|
2016-11-09T20:19:45Z
|
Fairness in Reinforcement Learning
|
We initiate the study of fairness in reinforcement learning, where the
actions of a learning algorithm may affect its environment and future rewards.
Our fairness constraint requires that an algorithm never prefers one action
over another if the long-term (discounted) reward of choosing the latter action
is higher. Our first result is negative: despite the fact that fairness is
consistent with the optimal policy, any learning algorithm satisfying fairness
must take time exponential in the number of states to achieve non-trivial
approximation to the optimal policy. We then provide a provably fair polynomial
time algorithm under an approximate notion of fairness, thus establishing an
exponential gap between exact and approximate fairness
|
[
"Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern,\n Aaron Roth",
"['Shahin Jabbari' 'Matthew Joseph' 'Michael Kearns' 'Jamie Morgenstern'\n 'Aaron Roth']"
] |
cs.LG cs.AR
| null |
1611.03109
| null | null |
http://arxiv.org/pdf/1611.03109v1
|
2016-10-25T18:45:32Z
|
2016-10-25T18:45:32Z
|
Energy-efficient Machine Learning in Silicon: A Communications-inspired
Approach
|
This position paper advocates a communications-inspired approach to the
design of machine learning systems on energy-constrained embedded `always-on'
platforms. The communications-inspired approach has two versions - 1) a
deterministic version where existing low-power communication IC design methods
are repurposed, and 2) a stochastic version referred to as Shannon-inspired
statistical information processing employing information-based metrics,
statistical error compensation (SEC), and retraining-based methods to implement
ML systems on stochastic circuit/device fabrics operating at the limits of
energy-efficiency. The communications-inspired approach has the potential to
fully leverage the opportunities afforded by ML algorithms and applications in
order to address the challenges inherent in their deployment on
energy-constrained platforms.
|
[
"['Naresh R. Shanbhag']",
"Naresh R. Shanbhag"
] |
cs.LG
| null |
1611.03125
| null | null |
http://arxiv.org/pdf/1611.03125v1
|
2016-11-09T22:40:15Z
|
2016-11-09T22:40:15Z
|
A Modular Theory of Feature Learning
|
Learning representations of data, and in particular learning features for a
subsequent prediction task, has been a fruitful area of research delivering
impressive empirical results in recent years. However, relatively little is
understood about what makes a representation `good'. We propose the idea of a
risk gap induced by representation learning for a given prediction context,
which measures the difference in the risk of some learner using the learned
features as compared to the original inputs. We describe a set of sufficient
conditions for unsupervised representation learning to provide a benefit, as
measured by this risk gap. These conditions decompose the problem of when
representation learning works into its constituent parts, which can be
separately evaluated using an unlabeled sample, suitable domain-specific
assumptions about the joint distribution, and analysis of the feature learner
and subsequent supervised learner. We provide two examples of such conditions
in the context of specific properties of the unlabeled distribution, namely
when the data lies close to a low-dimensional manifold and when it forms
clusters. We compare our approach to a recently proposed analysis of
semi-supervised learning.
|
[
"Daniel McNamara, Cheng Soon Ong, Robert C. Williamson",
"['Daniel McNamara' 'Cheng Soon Ong' 'Robert C. Williamson']"
] |
cs.LG stat.ML
| null |
1611.03131
| null | null |
http://arxiv.org/pdf/1611.03131v3
|
2017-03-03T04:38:29Z
|
2016-11-09T23:19:21Z
|
Diverse Neural Network Learns True Target Functions
|
Neural networks are a powerful class of functions that can be trained with
simple gradient descent to achieve state-of-the-art performance on a variety of
applications. Despite their practical success, there is a paucity of results
that provide theoretical guarantees on why they are so effective. Lying in the
center of the problem is the difficulty of analyzing the non-convex loss
function with potentially numerous local minima and saddle points. Can neural
networks corresponding to the stationary points of the loss function learn the
true target function? If yes, what are the key factors contributing to such
nice optimization properties?
In this paper, we answer these questions by analyzing one-hidden-layer neural
networks with ReLU activation, and show that despite the non-convexity, neural
networks with diverse units have no spurious local minima. We bypass the
non-convexity issue by directly analyzing the first order optimality condition,
and show that the loss can be made arbitrarily small if the minimum singular
value of the "extended feature matrix" is large enough. We make novel use of
techniques from kernel methods and geometric discrepancy, and identify a new
relation linking the smallest singular value to the spectrum of a kernel
function associated with the activation function and to the diversity of the
units. Our results also suggest a novel regularization function to promote unit
diversity for potentially better generalization.
|
[
"['Bo Xie' 'Yingyu Liang' 'Le Song']",
"Bo Xie, Yingyu Liang, Le Song"
] |
cs.LG
| null |
1611.03158
| null | null |
http://arxiv.org/pdf/1611.03158v2
|
2017-03-27T05:21:42Z
|
2016-11-10T01:48:39Z
|
Using Neural Networks to Compute Approximate and Guaranteed Feasible
Hamilton-Jacobi-Bellman PDE Solutions
|
To sidestep the curse of dimensionality when computing solutions to
Hamilton-Jacobi-Bellman partial differential equations (HJB PDE), we propose an
algorithm that leverages a neural network to approximate the value function. We
show that our final approximation of the value function generates near optimal
controls which are guaranteed to successfully drive the system to a target
state. Our framework is not dependent on state space discretization, leading to
a significant reduction in computation time and space complexity in comparison
with dynamic programming-based approaches. Using this grid-free approach also
enables us to plan over longer time horizons with relatively little additional
computation overhead. Unlike many previous neural network HJB PDE approximating
formulations, our approximation is strictly conservative and hence any
trajectories we generate will be strictly feasible. For demonstration, we
specialize our new general framework to the Dubins car model and discuss how
the framework can be applied to other models with higher-dimensional state
spaces.
|
[
"['Frank Jiang' 'Glen Chou' 'Mo Chen' 'Claire J. Tomlin']",
"Frank Jiang, Glen Chou, Mo Chen, Claire J. Tomlin"
] |
cs.CR cs.LG
| null |
1611.03186
| null | null |
http://arxiv.org/pdf/1611.03186v1
|
2016-11-10T05:08:02Z
|
2016-11-10T05:08:02Z
|
SoK: Applying Machine Learning in Security - A Survey
|
The idea of applying machine learning(ML) to solve problems in security
domains is almost 3 decades old. As information and communications grow more
ubiquitous and more data become available, many security risks arise as well as
appetite to manage and mitigate such risks. Consequently, research on applying
and designing ML algorithms and systems for security has grown fast, ranging
from intrusion detection systems(IDS) and malware classification to security
policy management(SPM) and information leak checking. In this paper, we
systematically study the methods, algorithms, and system designs in academic
publications from 2008-2015 that applied ML in security domains. 98 percent of
the surveyed papers appeared in the 6 highest-ranked academic security
conferences and 1 conference known for pioneering ML applications in security.
We examine the generalized system designs, underlying assumptions,
measurements, and use cases in active research. Our examinations lead to 1) a
taxonomy on ML paradigms and security domains for future exploration and
exploitation, and 2) an agenda detailing open and upcoming challenges. Based on
our survey, we also suggest a point of view that treats security as a game
theory problem instead of a batch-trained ML problem.
|
[
"Heju Jiang, Jasvir Nagra, Parvez Ahammad",
"['Heju Jiang' 'Jasvir Nagra' 'Parvez Ahammad']"
] |
cs.LG stat.ML
| null |
1611.03199
| null | null |
http://arxiv.org/pdf/1611.03199v1
|
2016-11-10T07:00:43Z
|
2016-11-10T07:00:43Z
|
Low Data Drug Discovery with One-shot Learning
|
Recent advances in machine learning have made significant contributions to
drug discovery. Deep neural networks in particular have been demonstrated to
provide significant boosts in predictive power when inferring the properties
and activities of small-molecule compounds. However, the applicability of these
techniques has been limited by the requirement for large amounts of training
data. In this work, we demonstrate how one-shot learning can be used to
significantly lower the amounts of data required to make meaningful predictions
in drug discovery applications. We introduce a new architecture, the residual
LSTM embedding, that, when combined with graph convolutional neural networks,
significantly improves the ability to learn meaningful distance metrics over
small-molecules. We open source all models introduced in this work as part of
DeepChem, an open-source framework for deep-learning in drug discovery.
|
[
"['Han Altae-Tran' 'Bharath Ramsundar' 'Aneesh S. Pappu' 'Vijay Pande']",
"Han Altae-Tran, Bharath Ramsundar, Aneesh S. Pappu, and Vijay Pande"
] |
cs.LG
| null |
1611.03214
| null | null |
http://arxiv.org/pdf/1611.03214v1
|
2016-11-10T08:07:46Z
|
2016-11-10T08:07:46Z
|
Ultimate tensorization: compressing convolutional and FC layers alike
|
Convolutional neural networks excel in image recognition tasks, but this
comes at the cost of high computational and memory complexity. To tackle this
problem, [1] developed a tensor factorization framework to compress
fully-connected layers. In this paper, we focus on compressing convolutional
layers. We show that while the direct application of the tensor framework [1]
to the 4-dimensional kernel of convolution does compress the layer, we can do
better. We reshape the convolutional kernel into a tensor of higher order and
factorize it. We combine the proposed approach with the previous work to
compress both convolutional and fully-connected layers of a network and achieve
80x network compression rate with 1.1% accuracy drop on the CIFAR-10 dataset.
|
[
"['Timur Garipov' 'Dmitry Podoprikhin' 'Alexander Novikov' 'Dmitry Vetrov']",
"Timur Garipov, Dmitry Podoprikhin, Alexander Novikov, Dmitry Vetrov"
] |
cs.AI cs.CL cs.LG cs.MA
| null |
1611.03218
| null | null |
http://arxiv.org/pdf/1611.03218v4
|
2017-03-15T11:24:49Z
|
2016-11-10T08:44:52Z
|
Learning to Play Guess Who? and Inventing a Grounded Language as a
Consequence
|
Acquiring your first language is an incredible feat and not easily
duplicated. Learning to communicate using nothing but a few pictureless books,
a corpus, would likely be impossible even for humans. Nevertheless, this is the
dominating approach in most natural language processing today. As an
alternative, we propose the use of situated interactions between agents as a
driving force for communication, and the framework of Deep Recurrent Q-Networks
for evolving a shared language grounded in the provided environment. We task
the agents with interactive image search in the form of the game Guess Who?.
The images from the game provide a non trivial environment for the agents to
discuss and a natural grounding for the concepts they decide to encode in their
communication. Our experiments show that the agents learn not only to encode
physical concepts in their words, i.e. grounding, but also that the agents
learn to hold a multi-step dialogue remembering the state of the dialogue from
step to step.
|
[
"Emilio Jorge, Mikael K{\\aa}geb\\\"ack, Fredrik D. Johansson, Emil\n Gustavsson",
"['Emilio Jorge' 'Mikael Kågebäck' 'Fredrik D. Johansson' 'Emil Gustavsson']"
] |
cs.NA cs.DS cs.LG math.NA
| null |
1611.0322
| null | null | null | null | null |
Faster Kernel Ridge Regression Using Sketching and Preconditioning
|
Kernel Ridge Regression (KRR) is a simple yet powerful technique for
non-parametric regression whose computation amounts to solving a linear system.
This system is usually dense and highly ill-conditioned. In addition, the
dimensions of the matrix are the same as the number of data points, so direct
methods are unrealistic for large-scale datasets. In this paper, we propose a
preconditioning technique for accelerating the solution of the aforementioned
linear system. The preconditioner is based on random feature maps, such as
random Fourier features, which have recently emerged as a powerful technique
for speeding up and scaling the training of kernel-based methods, such as
kernel ridge regression, by resorting to approximations. However, random
feature maps only provide crude approximations to the kernel function, so
delivering state-of-the-art results by directly solving the approximated system
requires the number of random features to be very large. We show that random
feature maps can be much more effective in forming preconditioners, since under
certain conditions a not-too-large number of random features is sufficient to
yield an effective preconditioner. We empirically evaluate our method and show
it is highly effective for datasets of up to one million training examples.
|
[
"Haim Avron and Kenneth L. Clarkson and David P. Woodruff"
] |
null | null |
1611.03220
| null | null |
http://arxiv.org/pdf/1611.03220v4
|
2017-07-15T06:31:03Z
|
2016-11-10T08:50:05Z
|
Faster Kernel Ridge Regression Using Sketching and Preconditioning
|
Kernel Ridge Regression (KRR) is a simple yet powerful technique for non-parametric regression whose computation amounts to solving a linear system. This system is usually dense and highly ill-conditioned. In addition, the dimensions of the matrix are the same as the number of data points, so direct methods are unrealistic for large-scale datasets. In this paper, we propose a preconditioning technique for accelerating the solution of the aforementioned linear system. The preconditioner is based on random feature maps, such as random Fourier features, which have recently emerged as a powerful technique for speeding up and scaling the training of kernel-based methods, such as kernel ridge regression, by resorting to approximations. However, random feature maps only provide crude approximations to the kernel function, so delivering state-of-the-art results by directly solving the approximated system requires the number of random features to be very large. We show that random feature maps can be much more effective in forming preconditioners, since under certain conditions a not-too-large number of random features is sufficient to yield an effective preconditioner. We empirically evaluate our method and show it is highly effective for datasets of up to one million training examples.
|
[
"['Haim Avron' 'Kenneth L. Clarkson' 'David P. Woodruff']"
] |
cs.DS cs.LG cs.NA math.NA
| null |
1611.03225
| null | null |
http://arxiv.org/pdf/1611.03225v2
|
2017-06-26T12:55:39Z
|
2016-11-10T09:05:43Z
|
Sharper Bounds for Regularized Data Fitting
|
We study matrix sketching methods for regularized variants of linear
regression, low rank approximation, and canonical correlation analysis. Our
main focus is on sketching techniques which preserve the objective function
value for regularized problems, which is an area that has remained largely
unexplored. We study regularization both in a fairly broad setting, and in the
specific context of the popular and widely used technique of ridge
regularization; for the latter, as applied to each of these problems, we show
algorithmic resource bounds in which the {\em statistical dimension} appears in
places where in previous bounds the rank would appear. The statistical
dimension is always smaller than the rank, and decreases as the amount of
regularization increases. In particular, for the ridge low-rank approximation
problem $\min_{Y,X} \lVert YX - A \rVert_F^2 + \lambda \lVert Y\rVert_F^2 +
\lambda\lVert X \rVert_F^2$, where $Y\in\mathbb{R}^{n\times k}$ and
$X\in\mathbb{R}^{k\times d}$, we give an approximation algorithm needing \[
O(\mathtt{nnz}(A)) + \tilde{O}((n+d)\varepsilon^{-1}k \min\{k,
\varepsilon^{-1}\mathtt{sd}_\lambda(Y^*)\})+
\mathtt{poly}(\mathtt{sd}_\lambda(Y^*) \varepsilon^{-1}) \] time, where
$s_{\lambda}(Y^*)\le k$ is the statistical dimension of $Y^*$, $Y^*$ is an
optimal $Y$, $\varepsilon$ is an error parameter, and $\mathtt{nnz}(A)$ is the
number of nonzero entries of $A$.This is faster than prior work, even when
$\lambda=0$.
We also study regularization in a much more general setting. For example, we
obtain sketching-based algorithms for the low-rank approximation problem
$\min_{X,Y} \lVert YX - A \rVert_F^2 + f(Y,X)$ where $f(\cdot,\cdot)$ is a
regularizing function satisfying some very general conditions (chiefly,
invariance under orthogonal transformations).
|
[
"Haim Avron and Kenneth L. Clarkson and David P. Woodruff",
"['Haim Avron' 'Kenneth L. Clarkson' 'David P. Woodruff']"
] |
stat.ML cs.LG
| null |
1611.03231
| null | null |
http://arxiv.org/pdf/1611.03231v1
|
2016-11-10T09:25:12Z
|
2016-11-10T09:25:12Z
|
Policy Search with High-Dimensional Context Variables
|
Direct contextual policy search methods learn to improve policy parameters
and simultaneously generalize these parameters to different context or task
variables. However, learning from high-dimensional context variables, such as
camera images, is still a prominent problem in many real-world tasks. A naive
application of unsupervised dimensionality reduction methods to the context
variables, such as principal component analysis, is insufficient as
task-relevant input may be ignored. In this paper, we propose a contextual
policy search method in the model-based relative entropy stochastic search
framework with integrated dimensionality reduction. We learn a model of the
reward that is locally quadratic in both the policy parameters and the context
variables. Furthermore, we perform supervised linear dimensionality reduction
on the context variables by nuclear norm regularization. The experimental
results show that the proposed method outperforms naive dimensionality
reduction via principal component analysis and a state-of-the-art contextual
policy search method.
|
[
"Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan\n Peters, Masashi Sugiyama",
"['Voot Tangkaratt' 'Herke van Hoof' 'Simone Parisi' 'Gerhard Neumann'\n 'Jan Peters' 'Masashi Sugiyama']"
] |
cs.LG stat.ML
| null |
1611.03383
| null | null |
http://arxiv.org/pdf/1611.03383v1
|
2016-11-10T16:24:16Z
|
2016-11-10T16:24:16Z
|
Disentangling factors of variation in deep representations using
adversarial training
|
We introduce a conditional generative model for learning to disentangle the
hidden factors of variation within a set of labeled observations, and separate
them into complementary codes. One code summarizes the specified factors of
variation associated with the labels. The other summarizes the remaining
unspecified variability. During training, the only available source of
supervision comes from our ability to distinguish among different observations
belonging to the same class. Examples of such observations include images of a
set of labeled objects captured at different viewpoints, or recordings of set
of speakers dictating multiple phrases. In both instances, the intra-class
diversity is the source of the unspecified factors of variation: each object is
observed at multiple viewpoints, and each speaker dictates multiple phrases.
Learning to disentangle the specified factors from the unspecified ones becomes
easier when strong supervision is possible. Suppose that during training, we
have access to pairs of images, where each pair shows two different objects
captured from the same viewpoint. This source of alignment allows us to solve
our task using existing methods. However, labels for the unspecified factors
are usually unavailable in realistic scenarios where data acquisition is not
strictly controlled. We address the problem of disentanglement in this more
general setting by combining deep convolutional autoencoders with a form of
adversarial training. Both factors of variation are implicitly captured in the
organization of the learned embedding space, and can be used for solving
single-image analogies. Experimental results on synthetic and real datasets
show that the proposed method is capable of generalizing to unseen classes and
intra-class variabilities.
|
[
"['Michael Mathieu' 'Junbo Zhao' 'Pablo Sprechmann' 'Aditya Ramesh'\n 'Yann LeCun']",
"Michael Mathieu, Junbo Zhao, Pablo Sprechmann, Aditya Ramesh, Yann\n LeCun"
] |
cs.DC astro-ph.IM cs.LG stat.AP stat.ML
| null |
1611.03404
| null | null |
http://arxiv.org/pdf/1611.03404v1
|
2016-11-10T17:16:04Z
|
2016-11-10T17:16:04Z
|
Learning an Astronomical Catalog of the Visible Universe through
Scalable Bayesian Inference
|
Celeste is a procedure for inferring astronomical catalogs that attains
state-of-the-art scientific results. To date, Celeste has been scaled to at
most hundreds of megabytes of astronomical images: Bayesian posterior inference
is notoriously demanding computationally. In this paper, we report on a
scalable, parallel version of Celeste, suitable for learning catalogs from
modern large-scale astronomical datasets. Our algorithmic innovations include a
fast numerical optimization routine for Bayesian posterior inference and a
statistically efficient scheme for decomposing astronomical optimization
problems into subproblems.
Our scalable implementation is written entirely in Julia, a new high-level
dynamic programming language designed for scientific and numerical computing.
We use Julia's high-level constructs for shared and distributed memory
parallelism, and demonstrate effective load balancing and efficient scaling on
up to 8192 Xeon cores on the NERSC Cori supercomputer.
|
[
"['Jeffrey Regier' 'Kiran Pamnany' 'Ryan Giordano' 'Rollin Thomas'\n 'David Schlegel' 'Jon McAuliffe' 'Prabhat']",
"Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin Thomas, David\n Schlegel, Jon McAuliffe and Prabhat"
] |
cs.PL cs.LG cs.MS
| null |
1611.0341
| null | null | null | null | null |
Binomial Checkpointing for Arbitrary Programs with No User Annotation
|
Heretofore, automatic checkpointing at procedure-call boundaries, to reduce
the space complexity of reverse mode, has been provided by systems like
Tapenade. However, binomial checkpointing, or treeverse, has only been provided
in Automatic Differentiation (AD) systems in special cases, e.g., through
user-provided pragmas on DO loops in Tapenade, or as the nested taping
mechanism in adol-c for time integration processes, which requires that user
code be refactored. We present a framework for applying binomial checkpointing
to arbitrary code with no special annotation or refactoring required. This is
accomplished by applying binomial checkpointing directly to a program trace.
This trace is produced by a general-purpose checkpointing mechanism that is
orthogonal to AD.
|
[
"Jeffrey Mark Siskind and Barak A. Pearlmutter"
] |
null | null |
1611.03410
| null | null |
http://arxiv.org/pdf/1611.03410v1
|
2016-11-10T17:29:24Z
|
2016-11-10T17:29:24Z
|
Binomial Checkpointing for Arbitrary Programs with No User Annotation
|
Heretofore, automatic checkpointing at procedure-call boundaries, to reduce the space complexity of reverse mode, has been provided by systems like Tapenade. However, binomial checkpointing, or treeverse, has only been provided in Automatic Differentiation (AD) systems in special cases, e.g., through user-provided pragmas on DO loops in Tapenade, or as the nested taping mechanism in adol-c for time integration processes, which requires that user code be refactored. We present a framework for applying binomial checkpointing to arbitrary code with no special annotation or refactoring required. This is accomplished by applying binomial checkpointing directly to a program trace. This trace is produced by a general-purpose checkpointing mechanism that is orthogonal to AD.
|
[
"['Jeffrey Mark Siskind' 'Barak A. Pearlmutter']"
] |
cs.MS cs.LG
| null |
1611.03423
| null | null |
http://arxiv.org/pdf/1611.03423v1
|
2016-11-10T17:50:06Z
|
2016-11-10T17:50:06Z
|
DiffSharp: An AD Library for .NET Languages
|
DiffSharp is an algorithmic differentiation or automatic differentiation (AD)
library for the .NET ecosystem, which is targeted by the C# and F# languages,
among others. The library has been designed with machine learning applications
in mind, allowing very succinct implementations of models and optimization
routines. DiffSharp is implemented in F# and exposes forward and reverse AD
operators as general nestable higher-order functions, usable by any .NET
language. It provides high-performance linear algebra primitives---scalars,
vectors, and matrices, with a generalization to tensors underway---that are
fully supported by all the AD operators, and which use a BLAS/LAPACK backend
via the highly optimized OpenBLAS library. DiffSharp currently uses operator
overloading, but we are developing a transformation-based version of the
library using F#'s "code quotation" metaprogramming facility. Work on a
CUDA-based GPU backend is also underway.
|
[
"At{\\i}l{\\i}m G\\\"une\\c{s} Baydin and Barak A. Pearlmutter and Jeffrey\n Mark Siskind",
"['Atılım Güneş Baydin' 'Barak A. Pearlmutter' 'Jeffrey Mark Siskind']"
] |
stat.ML cs.LG
| null |
1611.03427
| null | null |
http://arxiv.org/pdf/1611.03427v2
|
2017-03-02T22:09:54Z
|
2016-11-10T17:54:22Z
|
Multi-Task Multiple Kernel Relationship Learning
|
This paper presents a novel multitask multiple kernel learning framework that
efficiently learns the kernel weights leveraging the relationship across
multiple tasks. The idea is to automatically infer this task relationship in
the \textit{RKHS} space corresponding to the given base kernels. The problem is
formulated as a regularization-based approach called \textit{Multi-Task
Multiple Kernel Relationship Learning} (\textit{MK-MTRL}), which models the
task relationship matrix from the weights learned from latent feature spaces of
task-specific base kernels. Unlike in previous work, the proposed formulation
allows one to incorporate prior knowledge for simultaneously learning several
related tasks. We propose an alternating minimization algorithm to learn the
model parameters, kernel weights and task relationship matrix. In order to
tackle large-scale problems, we further propose a two-stage \textit{MK-MTRL}
online learning algorithm and show that it significantly reduces the
computational time, and also achieves performance comparable to that of the
joint learning framework. Experimental results on benchmark datasets show that
the proposed formulations outperform several state-of-the-art multitask
learning methods.
|
[
"['Keerthiram Murugesan' 'Jaime Carbonell']",
"Keerthiram Murugesan, Jaime Carbonell"
] |
cs.LG cs.AI stat.ML
| null |
1611.03451
| null | null |
http://arxiv.org/pdf/1611.03451v1
|
2016-11-10T19:11:09Z
|
2016-11-10T19:11:09Z
|
Importance Sampling with Unequal Support
|
Importance sampling is often used in machine learning when training and
testing data come from different distributions. In this paper we propose a new
variant of importance sampling that can reduce the variance of importance
sampling-based estimates by orders of magnitude when the supports of the
training and testing distributions differ. After motivating and presenting our
new importance sampling estimator, we provide a detailed theoretical analysis
that characterizes both its bias and variance relative to the ordinary
importance sampling estimator (in various settings, which include cases where
ordinary importance sampling is biased, while our new estimator is not, and
vice versa). We conclude with an example of how our new importance sampling
estimator can be used to improve estimates of how well a new treatment policy
for diabetes will work for an individual, using only data from when the
individual used a previous treatment policy.
|
[
"Philip S. Thomas and Emma Brunskill",
"['Philip S. Thomas' 'Emma Brunskill']"
] |
cs.LG cs.CC cs.DS cs.IT math.IT math.ST stat.TH
| null |
1611.03473
| null | null |
http://arxiv.org/pdf/1611.03473v2
|
2017-05-17T15:48:34Z
|
2016-11-10T20:32:48Z
|
Statistical Query Lower Bounds for Robust Estimation of High-dimensional
Gaussians and Gaussian Mixtures
|
We describe a general technique that yields the first {\em Statistical Query
lower bounds} for a range of fundamental high-dimensional learning problems
involving Gaussian distributions. Our main results are for the problems of (1)
learning Gaussian mixture models (GMMs), and (2) robust (agnostic) learning of
a single unknown Gaussian distribution. For each of these problems, we show a
{\em super-polynomial gap} between the (information-theoretic) sample
complexity and the computational complexity of {\em any} Statistical Query
algorithm for the problem. Our SQ lower bound for Problem (1) is qualitatively
matched by known learning algorithms for GMMs. Our lower bound for Problem (2)
implies that the accuracy of the robust learning algorithm
in~\cite{DiakonikolasKKLMS16} is essentially best possible among all
polynomial-time SQ algorithms.
Our SQ lower bounds are attained via a unified moment-matching technique that
is useful in other contexts and may be of broader interest. Our technique
yields nearly-tight lower bounds for a number of related unsupervised
estimation problems. Specifically, for the problems of (3) robust covariance
estimation in spectral norm, and (4) robust sparse mean estimation, we
establish a quadratic {\em statistical--computational tradeoff} for SQ
algorithms, matching known upper bounds. Finally, our technique can be used to
obtain tight sample complexity lower bounds for high-dimensional {\em testing}
problems. Specifically, for the classical problem of robustly {\em testing} an
unknown mean (known covariance) Gaussian, our technique implies an
information-theoretic sample lower bound that scales {\em linearly} in the
dimension. Our sample lower bound matches the sample complexity of the
corresponding robust {\em learning} problem and separates the sample complexity
of robust testing from standard (non-robust) testing.
|
[
"Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart",
"['Ilias Diakonikolas' 'Daniel M. Kane' 'Alistair Stewart']"
] |
cs.LG
| null |
1611.0353
| null | null | null | null | null |
Understanding deep learning requires rethinking generalization
|
Despite their massive size, successful deep artificial neural networks can
exhibit a remarkably small difference between training and test performance.
Conventional wisdom attributes small generalization error either to properties
of the model family, or to the regularization techniques used during training.
Through extensive systematic experiments, we show how these traditional
approaches fail to explain why large neural networks generalize well in
practice. Specifically, our experiments establish that state-of-the-art
convolutional networks for image classification trained with stochastic
gradient methods easily fit a random labeling of the training data. This
phenomenon is qualitatively unaffected by explicit regularization, and occurs
even if we replace the true images by completely unstructured random noise. We
corroborate these experimental findings with a theoretical construction showing
that simple depth two neural networks already have perfect finite sample
expressivity as soon as the number of parameters exceeds the number of data
points as it usually does in practice.
We interpret our experimental findings by comparison with traditional models.
|
[
"Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol\n Vinyals"
] |
null | null |
1611.03530
| null | null |
http://arxiv.org/pdf/1611.03530v2
|
2017-02-26T19:36:40Z
|
2016-11-10T22:02:36Z
|
Understanding deep learning requires rethinking generalization
|
Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family, or to the regularization techniques used during training. Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice. We interpret our experimental findings by comparison with traditional models.
|
[
"['Chiyuan Zhang' 'Samy Bengio' 'Moritz Hardt' 'Benjamin Recht'\n 'Oriol Vinyals']"
] |
cs.LG cs.AI
| null |
1611.03553
| null | null |
http://arxiv.org/pdf/1611.03553v1
|
2016-11-11T00:46:33Z
|
2016-11-11T00:46:33Z
|
The Sum-Product Theorem: A Foundation for Learning Tractable Models
|
Inference in expressive probabilistic models is generally intractable, which
makes them difficult to learn and limits their applicability. Sum-product
networks are a class of deep models where, surprisingly, inference remains
tractable even when an arbitrary number of hidden layers are present. In this
paper, we generalize this result to a much broader set of learning problems:
all those where inference consists of summing a function over a semiring. This
includes satisfiability, constraint satisfaction, optimization, integration,
and others. In any semiring, for summation to be tractable it suffices that the
factors of every product have disjoint scopes. This unifies and extends many
previous results in the literature. Enforcing this condition at learning time
thus ensures that the learned models are tractable. We illustrate the power and
generality of this approach by applying it to a new type of structured
prediction problem: learning a nonconvex function that can be globally
optimized in polynomial time. We show empirically that this greatly outperforms
the standard approach of learning without regard to the cost of optimization.
|
[
"['Abram L. Friesen' 'Pedro Domingos']",
"Abram L. Friesen and Pedro Domingos"
] |
stat.ML cs.LG
| null |
1611.03578
| null | null |
http://arxiv.org/pdf/1611.03578v1
|
2016-11-11T03:54:00Z
|
2016-11-11T03:54:00Z
|
Simple and Efficient Parallelization for Probabilistic Temporal Tensor
Factorization
|
Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm
to model the temporal tensor data. It leverages a time constraint to capture
the evolving properties of tensor data. Nowadays the exploding dataset demands
a large scale PTTF analysis, and a parallel solution is critical to accommodate
the trend. Whereas, the parallelization of PTTF still remains unexplored. In
this paper, we propose a simple yet efficient Parallel Probabilistic Temporal
Tensor Factorization, referred to as P$^2$T$^2$F, to provide a scalable PTTF
solution. P$^2$T$^2$F is fundamentally disparate from existing parallel tensor
factorizations by considering the probabilistic decomposition and the temporal
effects of tensor data. It adopts a new tensor data split strategy to subdivide
a large tensor into independent sub-tensors, the computation of which is
inherently parallel. We train P$^2$T$^2$F with an efficient algorithm of
stochastic Alternating Direction Method of Multipliers, and show that the
convergence is guaranteed. Experiments on several real-word tensor datasets
demonstrate that P$^2$T$^2$F is a highly effective and efficiently scalable
algorithm dedicated for large scale probabilistic temporal tensor analysis.
|
[
"['Guangxi Li' 'Zenglin Xu' 'Linnan Wang' 'Jinmian Ye' 'Irwin King'\n 'Michael Lyu']",
"Guangxi Li, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, Michael\n Lyu"
] |
cs.DS cs.IT cs.LG math.IT math.ST stat.TH
| null |
1611.03579
| null | null |
http://arxiv.org/pdf/1611.03579v1
|
2016-11-11T03:59:24Z
|
2016-11-11T03:59:24Z
|
Collision-based Testers are Optimal for Uniformity and Closeness
|
We study the fundamental problems of (i) uniformity testing of a discrete
distribution, and (ii) closeness testing between two discrete distributions
with bounded $\ell_2$-norm. These problems have been extensively studied in
distribution testing and sample-optimal estimators are known for
them~\cite{Paninski:08, CDVV14, VV14, DKN:15}.
In this work, we show that the original collision-based testers proposed for
these problems ~\cite{GRdist:00, BFR+:00} are sample-optimal, up to constant
factors. Previous analyses showed sample complexity upper bounds for these
testers that are optimal as a function of the domain size $n$, but suboptimal
by polynomial factors in the error parameter $\epsilon$. Our main contribution
is a new tight analysis establishing that these collision-based testers are
information-theoretically optimal, up to constant factors, both in the
dependence on $n$ and in the dependence on $\epsilon$.
|
[
"['Ilias Diakonikolas' 'Themis Gouleakis' 'John Peebles' 'Eric Price']",
"Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price"
] |
cs.CL cs.AI cs.LG
| null |
1611.03599
| null | null |
http://arxiv.org/pdf/1611.03599v1
|
2016-11-11T07:05:49Z
|
2016-11-11T07:05:49Z
|
UTCNN: a Deep Learning Model of Stance Classificationon on Social Media
Text
|
Most neural network models for document classification on social media focus
on text infor-mation to the neglect of other information on these platforms. In
this paper, we classify post stance on social media channels and develop UTCNN,
a neural network model that incorporates user tastes, topic tastes, and user
comments on posts. UTCNN not only works on social media texts, but also
analyzes texts in forums and message boards. Experiments performed on Chinese
Facebook data and English online debate forum data show that UTCNN achieves a
0.755 macro-average f-score for supportive, neutral, and unsupportive stance
classes on Facebook data, which is significantly better than models in which
either user, topic, or comment information is withheld. This model design
greatly mitigates the lack of data for the minor class without the use of
oversampling. In addition, UTCNN yields a 0.842 accuracy on English online
debate forum data, which also significantly outperforms results from previous
work as well as other deep learning models, showing that UTCNN performs well
regardless of language or platform.
|
[
"Wei-Fan Chen and Lun-Wei Ku",
"['Wei-Fan Chen' 'Lun-Wei Ku']"
] |
cs.LG
| null |
1611.03608
| null | null |
http://arxiv.org/pdf/1611.03608v1
|
2016-11-11T08:23:30Z
|
2016-11-11T08:23:30Z
|
Greedy Step Averaging: A parameter-free stochastic optimization method
|
In this paper we present the greedy step averaging(GSA) method, a
parameter-free stochastic optimization algorithm for a variety of machine
learning problems. As a gradient-based optimization method, GSA makes use of
the information from the minimizer of a single sample's loss function, and
takes average strategy to calculate reasonable learning rate sequence. While
most existing gradient-based algorithms introduce an increasing number of hyper
parameters or try to make a trade-off between computational cost and
convergence rate, GSA avoids the manual tuning of learning rate and brings in
no more hyper parameters or extra cost. We perform exhaustive numerical
experiments for logistic and softmax regression to compare our method with the
other state of the art ones on 16 datasets. Results show that GSA is robust on
various scenarios.
|
[
"Xiatian Zhang, Fan Yao, Yongjun Tian",
"['Xiatian Zhang' 'Fan Yao' 'Yongjun Tian']"
] |
cs.AI cs.CV cs.LG cs.RO
| null |
1611.03673
| null | null |
http://arxiv.org/pdf/1611.03673v3
|
2017-01-13T11:15:22Z
|
2016-11-11T12:14:45Z
|
Learning to Navigate in Complex Environments
|
Learning to navigate in complex environments with dynamic elements is an
important milestone in developing AI agents. In this work we formulate the
navigation question as a reinforcement learning problem and show that data
efficiency and task performance can be dramatically improved by relying on
additional auxiliary tasks leveraging multimodal sensory inputs. In particular
we consider jointly learning the goal-driven reinforcement learning problem
with auxiliary depth prediction and loop closure classification tasks. This
approach can learn to navigate from raw sensory input in complicated 3D mazes,
approaching human-level performance even under conditions where the goal
location changes frequently. We provide detailed analysis of the agent
behaviour, its ability to localise, and its network activity dynamics, showing
that the agent implicitly learns key navigation abilities.
|
[
"Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J.\n Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray\n Kavukcuoglu, Dharshan Kumaran and Raia Hadsell",
"['Piotr Mirowski' 'Razvan Pascanu' 'Fabio Viola' 'Hubert Soyer'\n 'Andrew J. Ballard' 'Andrea Banino' 'Misha Denil' 'Ross Goroshin'\n 'Laurent Sifre' 'Koray Kavukcuoglu' 'Dharshan Kumaran' 'Raia Hadsell']"
] |
cs.CV cs.LG
| null |
1611.03718
| null | null |
http://arxiv.org/pdf/1611.03718v2
|
2016-11-25T14:31:07Z
|
2016-11-11T14:25:54Z
|
Hierarchical Object Detection with Deep Reinforcement Learning
|
We present a method for performing hierarchical object detection in images
guided by a deep reinforcement learning agent. The key idea is to focus on
those parts of the image that contain richer information and zoom on them. We
train an intelligent agent that, given an image window, is capable of deciding
where to focus the attention among five different predefined region candidates
(smaller windows). This procedure is iterated providing a hierarchical image
analysis.We compare two different candidate proposal strategies to guide the
object search: with and without overlap. Moreover, our work compares two
different strategies to extract features from a convolutional neural network
for each region proposal: a first one that computes new feature maps for each
region proposal, and a second one that computes the feature maps for the whole
image to later generate crops for each region proposal. Experiments indicate
better results for the overlapping candidate proposal strategy and a loss of
performance for the cropped image features due to the loss of spatial
resolution. We argue that, while this loss seems unavoidable when working with
large amounts of object candidates, the much more reduced amount of region
proposals generated by our reinforcement learning agent allows considering to
extract features for each location without sharing convolutional computation
among regions.
|
[
"['Miriam Bellver' 'Xavier Giro-i-Nieto' 'Ferran Marques' 'Jordi Torres']",
"Miriam Bellver, Xavier Giro-i-Nieto, Ferran Marques and Jordi Torres"
] |
cs.LG stat.ML
| null |
1611.03777
| null | null |
http://arxiv.org/pdf/1611.03777v1
|
2016-11-10T17:57:19Z
|
2016-11-10T17:57:19Z
|
Tricks from Deep Learning
|
The deep learning community has devised a diverse set of methods to make
gradient optimization, using large datasets, of large and highly complex models
with deeply cascaded nonlinearities, practical. Taken as a whole, these methods
constitute a breakthrough, allowing computational structures which are quite
wide, very deep, and with an enormous number and variety of free parameters to
be effectively optimized. The result now dominates much of practical machine
learning, with applications in machine translation, computer vision, and speech
recognition. Many of these methods, viewed through the lens of algorithmic
differentiation (AD), can be seen as either addressing issues with the gradient
itself, or finding ways of achieving increased efficiency using tricks that are
AD-related, but not provided by current AD systems.
The goal of this paper is to explain not just those methods of most relevance
to AD, but also the technical constraints and mindset which led to their
discovery. After explaining this context, we present a "laundry list" of
methods developed by the deep learning community. Two of these are discussed in
further mathematical detail: a way to dramatically reduce the size of the tape
when performing reverse-mode AD on a (theoretically) time-reversible process
like an ODE integrator; and a new mathematical insight that allows for the
implementation of a stochastic Newton's method.
|
[
"At{\\i}l{\\i}m G\\\"une\\c{s} Baydin and Barak A. Pearlmutter and Jeffrey\n Mark Siskind",
"['Atılım Güneş Baydin' 'Barak A. Pearlmutter' 'Jeffrey Mark Siskind']"
] |
cs.CR cs.LG
| null |
1611.03814
| null | null |
http://arxiv.org/pdf/1611.03814v1
|
2016-11-11T18:57:15Z
|
2016-11-11T18:57:15Z
|
Towards the Science of Security and Privacy in Machine Learning
|
Advances in machine learning (ML) in recent years have enabled a dizzying
array of applications such as data analytics, autonomous systems, and security
diagnostics. ML is now pervasive---new systems and models are being deployed in
every domain imaginable, leading to rapid and widespread deployment of software
based inference and decision making. There is growing recognition that ML
exposes new vulnerabilities in software systems, yet the technical community's
understanding of the nature and extent of these vulnerabilities remains
limited. We systematize recent findings on ML security and privacy, focusing on
attacks identified on these systems and defenses crafted to date. We articulate
a comprehensive threat model for ML, and categorize attacks and defenses within
an adversarial framework. Key insights resulting from works both in the ML and
security communities are identified and the effectiveness of approaches are
related to structural elements of ML algorithms and the data used to train
them. We conclude by formally exploring the opposing relationship between model
accuracy and resilience to adversarial manipulation. Through these
explorations, we show that there are (possibly unavoidable) tensions between
model complexity, accuracy, and resilience that must be calibrated for the
environments in which they will be used.
|
[
"['Nicolas Papernot' 'Patrick McDaniel' 'Arunesh Sinha' 'Michael Wellman']",
"Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, Michael Wellman"
] |
cs.LG cs.DS stat.ML
| null |
1611.03819
| null | null |
http://arxiv.org/pdf/1611.03819v1
|
2016-11-11T19:13:37Z
|
2016-11-11T19:13:37Z
|
Recovery Guarantee of Non-negative Matrix Factorization via Alternating
Updates
|
Non-negative matrix factorization is a popular tool for decomposing data into
feature and weight matrices under non-negativity constraints. It enjoys
practical success but is poorly understood theoretically. This paper proposes
an algorithm that alternates between decoding the weights and updating the
features, and shows that assuming a generative model of the data, it provably
recovers the ground-truth under fairly mild conditions. In particular, its only
essential requirement on features is linear independence. Furthermore, the
algorithm uses ReLU to exploit the non-negativity for decoding the weights, and
thus can tolerate adversarial noise that can potentially be as large as the
signal, and can tolerate unbiased noise much larger than the signal. The
analysis relies on a carefully designed coupling between two potential
functions, which we believe is of independent interest.
|
[
"Yuanzhi Li, Yingyu Liang, Andrej Risteski",
"['Yuanzhi Li' 'Yingyu Liang' 'Andrej Risteski']"
] |
stat.ML cs.LG
| null |
1611.03824
| null | null |
http://arxiv.org/pdf/1611.03824v6
|
2017-06-12T11:19:30Z
|
2016-11-11T19:33:01Z
|
Learning to Learn without Gradient Descent by Gradient Descent
|
We learn recurrent neural network optimizers trained on simple synthetic
functions by gradient descent. We show that these learned optimizers exhibit a
remarkable degree of transfer in that they can be used to efficiently optimize
a broad range of derivative-free black-box functions, including Gaussian
process bandits, simple control objectives, global optimization benchmarks and
hyper-parameter tuning tasks. Up to the training horizon, the learned
optimizers learn to trade-off exploration and exploitation, and compare
favourably with heavily engineered Bayesian optimization packages for
hyper-parameter tuning.
|
[
"Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha\n Denil, Timothy P. Lillicrap, Matt Botvinick, Nando de Freitas",
"['Yutian Chen' 'Matthew W. Hoffman' 'Sergio Gomez Colmenarejo'\n 'Misha Denil' 'Timothy P. Lillicrap' 'Matt Botvinick' 'Nando de Freitas']"
] |
cs.LG cs.AI
| null |
1611.03852
| null | null |
http://arxiv.org/pdf/1611.03852v3
|
2016-11-25T08:09:55Z
|
2016-11-11T20:53:45Z
|
A Connection between Generative Adversarial Networks, Inverse
Reinforcement Learning, and Energy-Based Models
|
Generative adversarial networks (GANs) are a recently proposed class of
generative models in which a generator is trained to optimize a cost function
that is being simultaneously learned by a discriminator. While the idea of
learning cost functions is relatively new to the field of generative modeling,
learning costs has long been studied in control and reinforcement learning (RL)
domains, typically for imitation learning from demonstrations. In these fields,
learning cost function underlying observed behavior is known as inverse
reinforcement learning (IRL) or inverse optimal control. While at first the
connection between cost learning in RL and cost learning in generative modeling
may appear to be a superficial one, we show in this paper that certain IRL
methods are in fact mathematically equivalent to GANs. In particular, we
demonstrate an equivalence between a sample-based algorithm for maximum entropy
IRL and a GAN in which the generator's density can be evaluated and is provided
as an additional input to the discriminator. Interestingly, maximum entropy IRL
is a special case of an energy-based model. We discuss the interpretation of
GANs as an algorithm for training energy-based models, and relate this
interpretation to other recent work that seeks to connect GANs and EBMs. By
formally highlighting the connection between GANs, IRL, and EBMs, we hope that
researchers in all three communities can better identify and apply transferable
ideas from one domain to another, particularly for developing more stable and
scalable algorithms: a major challenge in all three domains.
|
[
"['Chelsea Finn' 'Paul Christiano' 'Pieter Abbeel' 'Sergey Levine']",
"Chelsea Finn, Paul Christiano, Pieter Abbeel, Sergey Levine"
] |
stat.ML cs.LG
| null |
1611.03879
| null | null |
http://arxiv.org/pdf/1611.03879v1
|
2016-11-11T21:08:36Z
|
2016-11-11T21:08:36Z
|
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM
|
Restricted Boltzmann Machine (RBM) is a bipartite graphical model that is
used as the building block in energy-based deep generative models. Due to
numerical stability and quantifiability of the likelihood, RBM is commonly used
with Bernoulli units. Here, we consider an alternative member of exponential
family RBM with leaky rectified linear units -- called leaky RBM. We first
study the joint and marginal distributions of leaky RBM under different
leakiness, which provides us important insights by connecting the leaky RBM
model and truncated Gaussian distributions. The connection leads us to a simple
yet efficient method for sampling from this model, where the basic idea is to
anneal the leakiness rather than the energy; -- i.e., start from a fully
Gaussian/Linear unit and gradually decrease the leakiness over iterations. This
serves as an alternative to the annealing of the temperature parameter and
enables numerical estimation of the likelihood that are more efficient and more
accurate than the commonly used annealed importance sampling (AIS). We further
demonstrate that the proposed sampling algorithm enjoys faster mixing property
than contrastive divergence algorithm, which benefits the training without any
additional computational cost.
|
[
"['Chun-Liang Li' 'Siamak Ravanbakhsh' 'Barnabas Poczos']",
"Chun-Liang Li, Siamak Ravanbakhsh, Barnabas Poczos"
] |
cs.LG
| null |
1611.03894
| null | null |
http://arxiv.org/pdf/1611.03894v1
|
2016-11-11T22:01:41Z
|
2016-11-11T22:01:41Z
|
Unsupervised Learning For Effective User Engagement on Social Media
|
In this paper, we investigate the effectiveness of unsupervised feature
learning techniques in predicting user engagement on social media.
Specifically, we compare two methods to predict the number of feedbacks (i.e.,
comments) that a blog post is likely to receive. We compare Principal Component
Analysis (PCA) and sparse Autoencoder to a baseline method where the data are
only centered and scaled, on each of two models: Linear Regression and
Regression Tree. We find that unsupervised learning techniques significantly
improve the prediction accuracy on both models. For the Linear Regression
model, sparse Autoencoder achieves the best result, with an improvement in the
root mean squared error (RMSE) on the test set of 42% over the baseline method.
For the Regression Tree model, PCA achieves the best result, with an
improvement in RMSE of 15% over the baseline.
|
[
"Thai Pham and Camelia Simoiu",
"['Thai Pham' 'Camelia Simoiu']"
] |
cs.LG stat.ML
| null |
1611.03898
| null | null |
http://arxiv.org/pdf/1611.03898v1
|
2016-11-11T22:20:41Z
|
2016-11-11T22:20:41Z
|
Low Latency Anomaly Detection and Bayesian Network Prediction of Anomaly
Likelihood
|
We develop a supervised machine learning model that detects anomalies in
systems in real time. Our model processes unbounded streams of data into time
series which then form the basis of a low-latency anomaly detection model.
Moreover, we extend our preliminary goal of just anomaly detection to
simultaneous anomaly prediction. We approach this very challenging problem by
developing a Bayesian Network framework that captures the information about the
parameters of the lagged regressors calibrated in the first part of our
approach and use this structure to learn local conditional probability
distributions.
|
[
"Derek Farren and Thai Pham and Marco Alban-Hidalgo",
"['Derek Farren' 'Thai Pham' 'Marco Alban-Hidalgo']"
] |
cs.AI cs.LG stat.ML
| null |
1611.03907
| null | null |
http://arxiv.org/pdf/1611.03907v4
|
2018-06-19T20:14:54Z
|
2016-11-11T22:39:01Z
|
Reinforcement Learning in Rich-Observation MDPs using Spectral Methods
|
Reinforcement learning (RL) in Markov decision processes (MDPs) with large
state spaces is a challenging problem. The performance of standard RL
algorithms degrades drastically with the dimensionality of state space.
However, in practice, these large MDPs typically incorporate a latent or hidden
low-dimensional structure. In this paper, we study the setting of
rich-observation Markov decision processes (ROMDP), where there are a small
number of hidden states which possess an injective mapping to the observation
states. In other words, every observation state is generated through a single
hidden state, and this mapping is unknown a priori. We introduce a spectral
decomposition method that consistently learns this mapping, and more
importantly, achieves it with low regret. The estimated mapping is integrated
into an optimistic RL algorithm (UCRL), which operates on the estimated hidden
space. We derive finite-time regret bounds for our algorithm with a weak
dependence on the dimensionality of the observed space. In fact, our algorithm
asymptotically achieves the same average regret as the oracle UCRL algorithm,
which has the knowledge of the mapping from hidden to observed spaces. Thus, we
derive an efficient spectral RL algorithm for ROMDPs.
|
[
"Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar",
"['Kamyar Azizzadenesheli' 'Alessandro Lazaric' 'Animashree Anandkumar']"
] |
cs.LG
| null |
1611.03934
| null | null |
http://arxiv.org/pdf/1611.03934v1
|
2016-11-12T01:53:54Z
|
2016-11-12T01:53:54Z
|
Personalized Donor-Recipient Matching for Organ Transplantation
|
Organ transplants can improve the life expectancy and quality of life for the
recipient but carries the risk of serious post-operative complications, such as
septic shock and organ rejection. The probability of a successful transplant
depends in a very subtle fashion on compatibility between the donor and the
recipient but current medical practice is short of domain knowledge regarding
the complex nature of recipient-donor compatibility. Hence a data-driven
approach for learning compatibility has the potential for significant
improvements in match quality. This paper proposes a novel system
(ConfidentMatch) that is trained using data from electronic health records.
ConfidentMatch predicts the success of an organ transplant (in terms of the 3
year survival rates) on the basis of clinical and demographic traits of the
donor and recipient. ConfidentMatch captures the heterogeneity of the donor and
recipient traits by optimally dividing the feature space into clusters and
constructing different optimal predictive models to each cluster. The system
controls the complexity of the learned predictive model in a way that allows
for assuring more granular and confident predictions for a larger number of
potential recipient-donor pairs, thereby ensuring that predictions are
"personalized" and tailored to individual characteristics to the finest
possible granularity. Experiments conducted on the UNOS heart transplant
dataset show the superiority of the prognostic value of ConfidentMatch to other
competing benchmarks; ConfidentMatch can provide predictions of success with
95% confidence for 5,489 patients of a total population of 9,620 patients,
which corresponds to 410 more patients than the most competitive benchmark
algorithm (DeepBoost).
|
[
"['Jinsung Yoon' 'Ahmed M. Alaa' 'Martin Cadeiras' 'Mihaela van der Schaar']",
"Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, and Mihaela van der\n Schaar"
] |
cs.LG cs.CR
| null |
1611.03941
| null | null |
http://arxiv.org/pdf/1611.03941v2
|
2017-02-25T00:56:26Z
|
2016-11-12T02:39:41Z
|
Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods
|
The problem of anomaly detection has been studied for a long time. In short,
anomalies are abnormal or unlikely things. In financial networks, thieves and
illegal activities are often anomalous in nature. Members of a network want to
detect anomalies as soon as possible to prevent them from harming the network's
community and integrity. Many Machine Learning techniques have been proposed to
deal with this problem; some results appear to be quite promising but there is
no obvious superior method. In this paper, we consider anomaly detection
particular to the Bitcoin transaction network. Our goal is to detect which
users and transactions are the most suspicious; in this case, anomalous
behavior is a proxy for suspicious behavior. To this end, we use three
unsupervised learning methods including k-means clustering, Mahalanobis
distance, and Unsupervised Support Vector Machine (SVM) on two graphs generated
by the Bitcoin transaction network: one graph has users as nodes, and the other
has transactions as nodes.
|
[
"['Thai Pham' 'Steven Lee']",
"Thai Pham and Steven Lee"
] |
stat.CO cs.LG stat.ML
| null |
1611.03969
| null | null |
http://arxiv.org/pdf/1611.03969v1
|
2016-11-12T08:18:38Z
|
2016-11-12T08:18:38Z
|
An Introduction to MM Algorithms for Machine Learning and Statistical
|
MM (majorization--minimization) algorithms are an increasingly popular tool
for solving optimization problems in machine learning and statistical
estimation. This article introduces the MM algorithm framework in general and
via three popular example applications: Gaussian mixture regressions,
multinomial logistic regressions, and support vector machines. Specific
algorithms for the three examples are derived and numerical demonstrations are
presented. Theoretical and practical aspects of MM algorithm design are
discussed.
|
[
"Hien D. Nguyen",
"['Hien D. Nguyen']"
] |
cs.LG stat.ML
| null |
1611.03981
| null | null |
http://arxiv.org/pdf/1611.03981v1
|
2016-11-12T10:23:53Z
|
2016-11-12T10:23:53Z
|
Dual Teaching: A Practical Semi-supervised Wrapper Method
|
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']",
"Fuqaing Liu, Chenwei Deng, Fukun Bi, Yiding Yang"
] |
stat.ML cs.LG cs.NA
| null |
1611.03993
| null | null |
http://arxiv.org/pdf/1611.03993v2
|
2017-02-23T09:09:58Z
|
2016-11-12T11:58:17Z
|
Riemannian Tensor Completion with Side Information
|
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",
"['Tengfei Zhou' 'Hui Qian' 'Zebang Shen' 'Congfu Xu']"
] |
cs.LG
| null |
1611.04049
| null | null |
http://arxiv.org/pdf/1611.04049v1
|
2016-11-12T22:08:15Z
|
2016-11-12T22:08:15Z
|
Prognostics of Surgical Site Infections using Dynamic Health Data
|
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,\n Shuai Huang",
"['Chuyang Ke' 'Yan Jin' 'Heather Evans' 'Bill Lober' 'Xiaoning Qian'\n 'Ji Liu' 'Shuai Huang']"
] |
stat.ML cs.LG
| null |
1611.04051
| null | null |
http://arxiv.org/pdf/1611.04051v1
|
2016-11-12T22:54:45Z
|
2016-11-12T22:54:45Z
|
GANS for Sequences of Discrete Elements with the Gumbel-softmax
Distribution
|
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' 'José Miguel Hernández-Lobato']",
"Matt J. Kusner, Jos\\'e Miguel Hern\\'andez-Lobato"
] |
stat.ML cs.LG
|
10.1109/TMI.2017.2650960
|
1611.04069
| null | null |
http://arxiv.org/abs/1611.04069v2
|
2017-01-09T10:27:05Z
|
2016-11-13T02:21:07Z
|
Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly
Accelerated Dynamic Imaging
|
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'\n 'Jeffrey A. Fessler']",
"Saiprasad Ravishankar, Brian E. Moore, Raj Rao Nadakuditi, and Jeffrey\n A. Fessler"
] |
cs.LG
| null |
1611.04088
| null | null |
http://arxiv.org/pdf/1611.04088v1
|
2016-11-13T05:52:58Z
|
2016-11-13T05:52:58Z
|
Batched Gaussian Process Bandit Optimization via Determinantal Point
Processes
|
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",
"['Tarun Kathuria' 'Amit Deshpande' 'Pushmeet Kohli']"
] |
stat.ML cs.LG
| null |
1611.04149
| null | null |
http://arxiv.org/pdf/1611.04149v1
|
2016-11-13T16:01:10Z
|
2016-11-13T16:01:10Z
|
Accelerated Variance Reduced Block Coordinate Descent
|
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",
"['Zebang Shen' 'Hui Qian' 'Chao Zhang' 'Tengfei Zhou']"
] |
cs.LG stat.ML
| null |
1611.04199
| null | null |
http://arxiv.org/pdf/1611.04199v1
|
2016-11-13T22:53:51Z
|
2016-11-13T22:53:51Z
|
Realistic risk-mitigating recommendations via inverse classification
|
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",
"['Michael T. Lash' 'W. Nick Street']"
] |
cs.LG cs.CV cs.RO
| null |
1611.04201
| null | null |
http://arxiv.org/pdf/1611.04201v4
|
2017-06-08T07:21:39Z
|
2016-11-13T23:08:42Z
|
CAD2RL: Real Single-Image Flight without a Single Real Image
|
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' 'Sergey Levine']",
"Fereshteh Sadeghi and Sergey Levine"
] |
stat.ML cs.LG
| null |
1611.04218
| null | null |
http://arxiv.org/pdf/1611.04218v1
|
2016-11-14T01:17:14Z
|
2016-11-14T01:17:14Z
|
Preference Completion from Partial Rankings
|
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']",
"Suriya Gunasekar, Oluwasanmi Koyejo, Joydeep Ghosh"
] |
cs.LG
| null |
1611.04228
| null | null |
http://arxiv.org/pdf/1611.04228v1
|
2016-11-14T02:28:13Z
|
2016-11-14T02:28:13Z
|
Learning Sparse, Distributed Representations using the Hebbian Principle
|
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' 'Upamanyu Madhow']",
"Aseem Wadhwa and Upamanyu Madhow"
] |
cs.LG cs.NE stat.ML
| null |
1611.04231
| null | null |
http://arxiv.org/pdf/1611.04231v3
|
2018-07-20T04:38:23Z
|
2016-11-14T02:44:18Z
|
Identity Matters in Deep Learning
|
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",
"['Moritz Hardt' 'Tengyu Ma']"
] |
cs.LG
| null |
1611.04273
| null | null |
http://arxiv.org/pdf/1611.04273v2
|
2017-06-06T22:36:35Z
|
2016-11-14T07:36:22Z
|
On the Quantitative Analysis of Decoder-Based Generative Models
|
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",
"['Yuhuai Wu' 'Yuri Burda' 'Ruslan Salakhutdinov' 'Roger Grosse']"
] |
cs.CL cs.LG cs.NE
| null |
1611.04361
| null | null |
http://arxiv.org/pdf/1611.04361v1
|
2016-11-14T12:36:07Z
|
2016-11-14T12:36:07Z
|
Attending to Characters in Neural Sequence Labeling Models
|
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",
"['Marek Rei' 'Gamal K. O. Crichton' 'Sampo Pyysalo']"
] |
stat.CO cs.LG stat.ML
| null |
1611.04416
| null | null |
http://arxiv.org/pdf/1611.04416v1
|
2016-11-14T15:21:23Z
|
2016-11-14T15:21:23Z
|
On numerical approximation schemes for expectation propagation
|
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",
"['Alexis Roche']"
] |
stat.ML cs.AI cs.LG cs.NE stat.ME
| null |
1611.04488
| null | null | null | null | null |
Generative Models and Model Criticism via Optimized Maximum Mean
Discrepancy
|
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,\n Aaditya Ramdas, Alex Smola, Arthur Gretton"
] |
stat.ML cs.LG
| null |
1611.04499
| null | null |
http://arxiv.org/pdf/1611.04499v2
|
2017-10-31T09:25:13Z
|
2016-11-14T17:54:28Z
|
Post Training in Deep Learning with Last Kernel
|
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' 'Julien Audiffren']",
"Thomas Moreau and Julien Audiffren"
] |
stat.ML cs.LG cs.NE
| null |
1611.045
| null | null | null | null | null |
Deep Learning with Sets and Point Clouds
|
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 | null |
1611.04500
| null | null |
http://arxiv.org/pdf/1611.04500v3
|
2017-02-24T01:54:59Z
|
2016-11-14T17:55:34Z
|
Deep Learning with Sets and Point Clouds
|
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' 'Jeff Schneider' 'Barnabas Poczos']"
] |
cs.LG stat.ML
| null |
1611.0452
| null | null | null | null | null |
Normalizing the Normalizers: Comparing and Extending Network
Normalization Schemes
|
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.\n Zemel"
] |
null | null |
1611.04520
| null | null |
http://arxiv.org/pdf/1611.04520v2
|
2017-03-06T21:03:03Z
|
2016-11-14T19:04:58Z
|
Normalizing the Normalizers: Comparing and Extending Network
Normalization Schemes
|
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'\n 'Richard S. Zemel']"
] |
quant-ph cs.LG stat.ML
| null |
1611.04528
| null | null |
http://arxiv.org/pdf/1611.04528v1
|
2016-11-14T19:15:57Z
|
2016-11-14T19:15:57Z
|
Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann
Machines
|
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\n G. Macready, Jason Rolfe, Evgeny Andriyash",
"['Dmytro Korenkevych' 'Yanbo Xue' 'Zhengbing Bian' 'Fabian Chudak'\n 'William G. Macready' 'Jason Rolfe' 'Evgeny Andriyash']"
] |
cs.DS cs.AI cs.LG
| null |
1611.04535
| null | null |
http://arxiv.org/pdf/1611.04535v4
|
2018-10-16T16:07:08Z
|
2016-11-14T19:22:21Z
|
Learning-Theoretic Foundations of Algorithm Configuration for
Combinatorial Partitioning Problems
|
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\n White",
"['Maria-Florina Balcan' 'Vaishnavh Nagarajan' 'Ellen Vitercik'\n 'Colin White']"
] |
stat.ML cs.LG
| null |
1611.04561
| null | null |
http://arxiv.org/pdf/1611.04561v1
|
2016-11-14T20:34:29Z
|
2016-11-14T20:34:29Z
|
Splitting matters: how monotone transformation of predictor variables
may improve the predictions of decision tree models
|
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']",
"Tal Galili, Isaac Meilijson"
] |
cs.LG
| null |
1611.04578
| null | null |
http://arxiv.org/pdf/1611.04578v1
|
2016-11-14T20:55:33Z
|
2016-11-14T20:55:33Z
|
Earliness-Aware Deep Convolutional Networks for Early Time Series
Classification
|
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",
"['Wenlin Wang' 'Changyou Chen' 'Wenqi Wang' 'Piyush Rai' 'Lawrence Carin']"
] |
cs.LG
| null |
1611.04581
| null | null |
http://arxiv.org/pdf/1611.04581v1
|
2016-11-14T20:59:54Z
|
2016-11-14T20:59:54Z
|
How to scale distributed deep learning?
|
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']",
"Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer"
] |
cs.AI cs.CL cs.LG
| null |
1611.04642
| null | null |
http://arxiv.org/pdf/1611.04642v5
|
2018-04-22T05:22:58Z
|
2016-11-14T22:54:45Z
|
Link Prediction using Embedded Knowledge Graphs
|
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']",
"Yelong Shen, Po-Sen Huang, Ming-Wei Chang, Jianfeng Gao"
] |
cs.IR cs.LG
| null |
1611.04666
| null | null |
http://arxiv.org/pdf/1611.04666v1
|
2016-11-15T01:32:33Z
|
2016-11-15T01:32:33Z
|
A Generic Coordinate Descent Framework for Learning from Implicit
Feedback
|
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",
"['Immanuel Bayer' 'Xiangnan He' 'Bhargav Kanagal' 'Steffen Rendle']"
] |
cs.LG
| null |
1611.04686
| null | null |
http://arxiv.org/pdf/1611.04686v1
|
2016-11-15T03:15:46Z
|
2016-11-15T03:15:46Z
|
Robust Matrix Regression
|
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",
"['Hang Zhang' 'Fengyuan Zhu' 'Shixin Li']"
] |
cs.AI cs.LG
| null |
1611.04717
| null | null |
http://arxiv.org/pdf/1611.04717v3
|
2017-12-05T16:44:47Z
|
2016-11-15T06:42:24Z
|
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement
Learning
|
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'\n 'Yan Duan' 'John Schulman' 'Filip De Turck' 'Pieter Abbeel']",
"Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan\n Duan, John Schulman, Filip De Turck, Pieter Abbeel"
] |
cs.CR cs.LG
| null |
1611.04786
| null | null |
http://arxiv.org/pdf/1611.04786v1
|
2016-11-15T10:54:58Z
|
2016-11-15T10:54:58Z
|
AdversariaLib: An Open-source Library for the Security Evaluation of
Machine Learning Algorithms Under Attack
|
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",
"['Igino Corona' 'Battista Biggio' 'Davide Maiorca']"
] |
cs.LG math.OC stat.ML
| null |
1611.04831
| null | null |
http://arxiv.org/pdf/1611.04831v1
|
2016-11-15T13:56:24Z
|
2016-11-15T13:56:24Z
|
The Power of Normalization: Faster Evasion of Saddle Points
|
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']",
"Kfir Y. Levy"
] |
cs.CV cs.LG stat.ML
| null |
1611.04835
| null | null |
http://arxiv.org/pdf/1611.04835v1
|
2016-11-15T14:05:43Z
|
2016-11-15T14:05:43Z
|
Multilinear Low-Rank Tensors on Graphs & Applications
|
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']",
"Nauman Shahid, Francesco Grassi, Pierre Vandergheynst"
] |
cs.LG cs.DS
| null |
1611.04847
| null | null |
http://arxiv.org/pdf/1611.04847v3
|
2017-03-06T13:26:53Z
|
2016-11-10T10:13:10Z
|
The Power of Side-information in Subgraph Detection
|
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' 'Konstantin Avrachenkov' 'Laura Cottatellucci'\n 'Rajesh Sundaresan']",
"Arun Kadavankandy (MAESTRO), Konstantin Avrachenkov (MAESTRO), Laura\n Cottatellucci, Rajesh Sundaresan (ECE)"
] |
cs.CV cs.LG
|
10.1109/TCYB.2016.2623638
|
1611.0487
| null | null | null | null | null |
Constrained Low-Rank Learning Using Least Squares-Based Regularization
|
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\n Xuelong Li"
] |
null | null |
1611.04870
| null | null |
http://arxiv.org/abs/1611.04870v1
|
2016-11-15T14:50:31Z
|
2016-11-15T14:50:31Z
|
Constrained Low-Rank Learning Using Least Squares-Based Regularization
|
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' 'Jun Yu' 'Meng Wang' 'Luming Zhang' 'Deng Cai' 'Xuelong Li']"
] |
cs.LG cs.CV cs.SD
| null |
1611.04871
| null | null |
http://arxiv.org/pdf/1611.04871v3
|
2017-02-18T07:18:32Z
|
2016-11-12T07:39:50Z
|
Audio Event and Scene Recognition: A Unified Approach using Strongly and
Weakly Labeled Data
|
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']",
"Anurag Kumar, Bhiksha Raj"
] |
stat.ML cs.LG
| null |
1611.0492
| null | null | null | null | null |
Unsupervised Learning with Truncated Gaussian Graphical Models
|
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 | null |
1611.04920
| null | null |
http://arxiv.org/pdf/1611.04920v2
|
2016-11-20T19:08:51Z
|
2016-11-15T16:26:17Z
|
Unsupervised Learning with Truncated Gaussian Graphical Models
|
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']"
] |
cs.LG
| null |
1611.04924
| null | null |
http://arxiv.org/pdf/1611.04924v2
|
2017-07-20T13:10:24Z
|
2016-11-15T16:36:29Z
|
Robust Semi-Supervised Graph Classifier Learning with Negative Edge
Weights
|
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' 'Chia-Wen Lin']",
"Gene Cheung, Weng-Tai Su, Yu Mao, and Chia-Wen Lin"
] |
cs.LG stat.ML
| null |
1611.04967
| null | null |
http://arxiv.org/pdf/1611.04967v1
|
2016-11-15T18:10:24Z
|
2016-11-15T18:10:24Z
|
Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box
Models
|
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']",
"Julius Adebayo, Lalana Kagal"
] |
math.OC cs.LG stat.ML
| null |
1611.04982
| null | null |
http://arxiv.org/pdf/1611.04982v2
|
2017-03-08T11:05:59Z
|
2016-11-15T18:41:55Z
|
Oracle Complexity of Second-Order Methods for Finite-Sum Problems
|
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",
"['Yossi Arjevani' 'Ohad Shamir']"
] |
cs.LG
| null |
1611.05013
| null | null |
http://arxiv.org/pdf/1611.05013v1
|
2016-11-15T20:16:27Z
|
2016-11-15T20:16:27Z
|
PixelVAE: A Latent Variable Model for Natural Images
|
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'\n 'Francesco Visin' 'David Vazquez' 'Aaron Courville']",
"Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga,\n Francesco Visin, David Vazquez, Aaron Courville"
] |
cs.SE cs.LG
| null |
1611.05083
| null | null |
http://arxiv.org/pdf/1611.05083v2
|
2016-11-18T11:37:29Z
|
2016-11-15T22:33:48Z
|
Probabilistic Failure Analysis in Model Validation & Verification
|
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 Crégut']",
"Ning Ge, Marc Pantel, Xavier Cr\\'egut"
] |
cs.LG cs.RO cs.SY
| null |
1611.05095
| null | null |
http://arxiv.org/pdf/1611.05095v1
|
2016-11-15T23:31:40Z
|
2016-11-15T23:31:40Z
|
Learning Dexterous Manipulation Policies from Experience and Imitation
|
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",
"['Vikash Kumar' 'Abhishek Gupta' 'Emanuel Todorov' 'Sergey Levine']"
] |
null | null |
1611.05132
| null | null |
http://arxiv.org/pdf/1611.05132v1
|
2016-11-16T03:28:08Z
|
2016-11-16T03:28:08Z
|
Convergence rate of stochastic k-means
|
We analyze online cite{BottouBengio} and mini-batch cite{Sculley} $k$-means variants. Both scale up the widely used $k$-means algorithm via stochastic approximation, and have become popular for large-scale clustering and unsupervised feature learning. We show, for the first time, that starting with any initial solution, they converge to a "local optimum" at rate $O(frac{1}{t})$ (in terms of the $k$-means objective) under general conditions. In addition, we show if the dataset is clusterable, when initialized with a simple and scalable seeding algorithm, mini-batch $k$-means converges to an optimal $k$-means solution at rate $O(frac{1}{t})$ with high probability. The $k$-means objective is non-convex and non-differentiable: we exploit ideas from recent work on stochastic gradient descent for non-convex problems cite{ge:sgd_tensor, balsubramani13} by providing a novel characterization of the trajectory of $k$-means algorithm on its solution space, and circumvent the non-differentiability problem via geometric insights about $k$-means update.
|
[
"['Cheng Tang' 'Claire Monteleoni']"
] |
cs.LG stat.ML
| null |
1611.05136
| null | null |
http://arxiv.org/pdf/1611.05136v1
|
2016-11-16T03:45:12Z
|
2016-11-16T03:45:12Z
|
Machine Learning Approach for Skill Evaluation in Robotic-Assisted
Surgery
|
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,\n Michael D. Klein, and R. Darin Ellis",
"['Mahtab J. Fard' 'Sattar Ameri' 'Ratna B. Chinnam' 'Abhilash K. Pandya'\n 'Michael D. Klein' 'R. Darin Ellis']"
] |
cs.LG cs.CV
| null |
1611.05138
| null | null |
http://arxiv.org/pdf/1611.05138v1
|
2016-11-16T04:17:52Z
|
2016-11-16T04:17:52Z
|
S3Pool: Pooling with Stochastic Spatial Sampling
|
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\n Zhang, Rogerio Feris",
"['Shuangfei Zhai' 'Hui Wu' 'Abhishek Kumar' 'Yu Cheng' 'Yongxi Lu'\n 'Zhongfei Zhang' 'Rogerio Feris']"
] |
cs.NE cs.LG
|
10.13140/RG.2.2.10967.06566
|
1611.05141
| null | null |
http://arxiv.org/abs/1611.05141v1
|
2016-11-16T04:32:22Z
|
2016-11-16T04:32:22Z
|
Training Spiking Deep Networks for Neuromorphic Hardware
|
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']",
"Eric Hunsberger, Chris Eliasmith"
] |
cs.LG stat.ML
| null |
1611.05146
| null | null |
http://arxiv.org/pdf/1611.05146v1
|
2016-11-16T05:11:36Z
|
2016-11-16T05:11:36Z
|
A Semi-Markov Switching Linear Gaussian Model for Censored Physiological
Data
|
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",
"['Ahmed M. Alaa' 'Jinsung Yoon' 'Scott Hu' 'Mihaela van der Schaar']"
] |
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