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The RNN-ELM Classifier | cs.NE cs.LG | In this paper we examine learning methods combining the Random Neural
Network, a biologically inspired neural network and the Extreme Learning
Machine that achieve state of the art classification performance while
requiring much shorter training time. The Random Neural Network is a integrate
and fire computational model of a neural network whose mathematical structure
permits the efficient analysis of large ensembles of neurons. An activation
function is derived from the RNN and used in an Extreme Learning Machine. We
compare the performance of this combination against the ELM with various
activation functions, we reduce the input dimensionality via PCA and compare
its performance vs. autoencoder based versions of the RNN-ELM.
| Athanasios Vlontzos | null | 1609.07724 | null | null |
Accurate and Efficient Hyperbolic Tangent Activation Function on FPGA
using the DCT Interpolation Filter | cs.NE cs.LG | Implementing an accurate and fast activation function with low cost is a
crucial aspect to the implementation of Deep Neural Networks (DNNs) on FPGAs.
We propose a high-accuracy approximation approach for the hyperbolic tangent
activation function of artificial neurons in DNNs. It is based on the Discrete
Cosine Transform Interpolation Filter (DCTIF). The proposed architecture
combines simple arithmetic operations on stored samples of the hyperbolic
tangent function and on input data. The proposed DCTIF implementation achieves
two orders of magnitude greater precision than previous work while using the
same or fewer computational resources. Various combinations of DCTIF parameters
can be chosen to tradeoff the accuracy and complexity of the hyperbolic tangent
function. In one case, the proposed architecture approximates the hyperbolic
tangent activation function with 10E-5 maximum error while requiring only 1.52
Kbits memory and 57 LUTs of a Virtex-7 FPGA. We also discuss how the activation
function accuracy affects the performance of DNNs in terms of their training
and testing accuracies. We show that a high accuracy approximation can be
necessary in order to maintain the same DNN training and testing performances
realized by the exact function.
| Ahmed M. Abdelsalam, J.M. Pierre Langlois and F. Cheriet | null | 1609.0775 | null | null |
Random Forest for Malware Classification | cs.CR cs.LG | The challenge in engaging malware activities involves the correct
identification and classification of different malware variants. Various
malwares incorporate code obfuscation methods that alters their code signatures
effectively countering antimalware detection techniques utilizing static
methods and signature database. In this study, we utilized an approach of
converting a malware binary into an image and use Random Forest to classify
various malware families. The resulting accuracy of 0.9562 exhibits the
effectivess of the method in detecting malware
| Felan Carlo C. Garcia, Felix P. Muga II | null | 1609.0777 | null | null |
Deep Structured Features for Semantic Segmentation | cs.CV cs.LG | We propose a highly structured neural network architecture for semantic
segmentation with an extremely small model size, suitable for low-power
embedded and mobile platforms. Specifically, our architecture combines i) a
Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random
layer realizing a radial basis function kernel approximation, and iii) a linear
classifier. While stages i) and ii) are completely pre-specified, only the
linear classifier is learned from data. We apply the proposed architecture to
outdoor scene and aerial image semantic segmentation and show that the accuracy
of our architecture is competitive with conventional pixel classification CNNs.
Furthermore, we demonstrate that the proposed architecture is data efficient in
the sense of matching the accuracy of pixel classification CNNs when trained on
a much smaller data set.
| Michael Tschannen, Lukas Cavigelli, Fabian Mentzer, Thomas Wiatowski,
Luca Benini | null | 1609.07916 | null | null |
Grounding object perception in a naive agent's sensorimotor experience | cs.RO cs.LG | Artificial object perception usually relies on a priori defined models and
feature extraction algorithms. We study how the concept of object can be
grounded in the sensorimotor experience of a naive agent. Without any knowledge
about itself or the world it is immersed in, the agent explores its
sensorimotor space and identifies objects as consistent networks of
sensorimotor transitions, independent from their context. A fundamental drive
for prediction is assumed to explain the emergence of such networks from a
developmental standpoint. An algorithm is proposed and tested to illustrate the
approach.
| Alban Laflaqui\`ere and Nikolas Hemion | 10.1109/DEVLRN.2015.7346156 | 1609.08009 | null | null |
Dropout with Expectation-linear Regularization | cs.LG stat.ML | Dropout, a simple and effective way to train deep neural networks, has led to
a number of impressive empirical successes and spawned many recent theoretical
investigations. However, the gap between dropout's training and inference
phases, introduced due to tractability considerations, has largely remained
under-appreciated. In this work, we first formulate dropout as a tractable
approximation of some latent variable model, leading to a clean view of
parameter sharing and enabling further theoretical analysis. Then, we introduce
(approximate) expectation-linear dropout neural networks, whose inference gap
we are able to formally characterize. Algorithmically, we show that our
proposed measure of the inference gap can be used to regularize the standard
dropout training objective, resulting in an \emph{explicit} control of the gap.
Our method is as simple and efficient as standard dropout. We further prove the
upper bounds on the loss in accuracy due to expectation-linearization, describe
classes of input distributions that expectation-linearize easily. Experiments
on three image classification benchmark datasets demonstrate that reducing the
inference gap can indeed improve the performance consistently.
| Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard
Hovy | null | 1609.08017 | null | null |
Google's Neural Machine Translation System: Bridging the Gap between
Human and Machine Translation | cs.CL cs.AI cs.LG | Neural Machine Translation (NMT) is an end-to-end learning approach for
automated translation, with the potential to overcome many of the weaknesses of
conventional phrase-based translation systems. Unfortunately, NMT systems are
known to be computationally expensive both in training and in translation
inference. Also, most NMT systems have difficulty with rare words. These issues
have hindered NMT's use in practical deployments and services, where both
accuracy and speed are essential. In this work, we present GNMT, Google's
Neural Machine Translation system, which attempts to address many of these
issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder
layers using attention and residual connections. To improve parallelism and
therefore decrease training time, our attention mechanism connects the bottom
layer of the decoder to the top layer of the encoder. To accelerate the final
translation speed, we employ low-precision arithmetic during inference
computations. To improve handling of rare words, we divide words into a limited
set of common sub-word units ("wordpieces") for both input and output. This
method provides a good balance between the flexibility of "character"-delimited
models and the efficiency of "word"-delimited models, naturally handles
translation of rare words, and ultimately improves the overall accuracy of the
system. Our beam search technique employs a length-normalization procedure and
uses a coverage penalty, which encourages generation of an output sentence that
is most likely to cover all the words in the source sentence. On the WMT'14
English-to-French and English-to-German benchmarks, GNMT achieves competitive
results to state-of-the-art. Using a human side-by-side evaluation on a set of
isolated simple sentences, it reduces translation errors by an average of 60%
compared to Google's phrase-based production system.
| Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi,
Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff
Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, {\L}ukasz Kaiser,
Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens,
George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason
Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey
Dean | null | 1609.08144 | null | null |
Nonnegative autoencoder with simplified random neural network | cs.LG | This paper proposes new nonnegative (shallow and multi-layer) autoencoders by
combining the spiking Random Neural Network (RNN) model, the network
architecture typical used in deep-learning area and the training technique
inspired from nonnegative matrix factorization (NMF). The shallow autoencoder
is a simplified RNN model, which is then stacked into a multi-layer
architecture. The learning algorithm is based on the weight update rules in
NMF, subject to the nonnegative probability constraints of the RNN. The
autoencoders equipped with this learning algorithm are tested on typical image
datasets including the MNIST, Yale face and CIFAR-10 datasets, and also using
16 real-world datasets from different areas. The results obtained through these
tests yield the desired high learning and recognition accuracy. Also, numerical
simulations of the stochastic spiking behavior of this RNN auto encoder, show
that it can be implemented in a highly-distributed manner.
| Yonghua Yin, Erol Gelenbe | null | 1609.08151 | null | null |
Automatic Construction of a Recurrent Neural Network based Classifier
for Vehicle Passage Detection | cs.CV cs.LG stat.ML | Recurrent Neural Networks (RNNs) are extensively used for time-series
modeling and prediction. We propose an approach for automatic construction of a
binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for
detection of a vehicle passage through a checkpoint. As an input to the
classifier we use multidimensional signals of various sensors that are
installed on the checkpoint. Obtained results demonstrate that the previous
approach to handcrafting a classifier, consisting of a set of deterministic
rules, can be successfully replaced by an automatic RNN training on an
appropriately labelled data.
| Evgeny Burnaev and Ivan Koptelov and German Novikov and Timur Khanipov | null | 1609.08209 | null | null |
Simultaneous Low-rank Component and Graph Estimation for
High-dimensional Graph Signals: Application to Brain Imaging | cs.CV cs.LG | We propose an algorithm to uncover the intrinsic low-rank component of a
high-dimensional, graph-smooth and grossly-corrupted dataset, under the
situations that the underlying graph is unknown. Based on a model with a
low-rank component plus a sparse perturbation, and an initial graph estimation,
our proposed algorithm simultaneously learns the low-rank component and refines
the graph. Our evaluations using synthetic and real brain imaging data in
unsupervised and supervised classification tasks demonstrate encouraging
performance.
| Rui Liu, Hossein Nejati, Seyed Hamid Safavi, Ngai-Man Cheung | null | 1609.08221 | null | null |
An Efficient Method for Robust Projection Matrix Design | cs.LG | Our objective is to efficiently design a robust projection matrix $\Phi$ for
the Compressive Sensing (CS) systems when applied to the signals that are not
exactly sparse. The optimal projection matrix is obtained by mainly minimizing
the average coherence of the equivalent dictionary. In order to drop the
requirement of the sparse representation error (SRE) for a set of training data
as in [15] [16], we introduce a novel penalty function independent of a
particular SRE matrix. Without requiring of training data, we can efficiently
design the robust projection matrix and apply it for most of CS systems, like a
CS system for image processing with a conventional wavelet dictionary in which
the SRE matrix is generally not available. Simulation results demonstrate the
efficiency and effectiveness of the proposed approach compared with the
state-of-the-art methods. In addition, we experimentally demonstrate with
natural images that under similar compression rate, a CS system with a learned
dictionary in high dimensions outperforms the one in low dimensions in terms of
reconstruction accuracy. This together with the fact that our proposed method
can efficiently work in high dimension suggests that a CS system can be
potentially implemented beyond the small patches in sparsity-based image
processing.
| Tao Hong and Zhihui Zhu | null | 1609.08281 | null | null |
Online Unsupervised Multi-view Feature Selection | cs.LG | In the era of big data, it is becoming common to have data with multiple
modalities or coming from multiple sources, known as "multi-view data".
Multi-view data are usually unlabeled and come from high-dimensional spaces
(such as language vocabularies), unsupervised multi-view feature selection is
crucial to many applications. However, it is nontrivial due to the following
challenges. First, there are too many instances or the feature dimensionality
is too large. Thus, the data may not fit in memory. How to select useful
features with limited memory space? Second, how to select features from
streaming data and handles the concept drift? Third, how to leverage the
consistent and complementary information from different views to improve the
feature selection in the situation when the data are too big or come in as
streams? To the best of our knowledge, none of the previous works can solve all
the challenges simultaneously. In this paper, we propose an Online unsupervised
Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming
multi-view data in an online fashion. OMVFS embeds unsupervised feature
selection into a clustering algorithm via NMF with sparse learning. It further
incorporates the graph regularization to preserve the local structure
information and help select discriminative features. Instead of storing all the
historical data, OMVFS processes the multi-view data chunk by chunk and
aggregates all the necessary information into several small matrices. By using
the buffering technique, the proposed OMVFS can reduce the computational and
storage cost while taking advantage of the structure information. Furthermore,
OMVFS can capture the concept drifts in the data streams. Extensive experiments
on four real-world datasets show the effectiveness and efficiency of the
proposed OMVFS method. More importantly, OMVFS is about 100 times faster than
the off-line methods.
| Weixiang Shao, Lifang He, Chun-Ta Lu, Xiaokai Wei, Philip S. Yu | null | 1609.08286 | null | null |
Duality between Feature Selection and Data Clustering | cs.IT cs.LG math.IT | The feature-selection problem is formulated from an information-theoretic
perspective. We show that the problem can be efficiently solved by an extension
of the recently proposed info-clustering paradigm. This reveals the fundamental
duality between feature selection and data clustering,which is a consequence of
the more general duality between the principal partition and the principal
lattice of partitions in combinatorial optimization.
| Chung Chan, Ali Al-Bashabsheh, Qiaoqiao Zhou, Tie Liu | null | 1609.08312 | null | null |
Asynchronous Stochastic Gradient Descent with Delay Compensation | cs.LG cs.DC | With the fast development of deep learning, it has become common to learn big
neural networks using massive training data. Asynchronous Stochastic Gradient
Descent (ASGD) is widely adopted to fulfill this task for its efficiency, which
is, however, known to suffer from the problem of delayed gradients. That is,
when a local worker adds its gradient to the global model, the global model may
have been updated by other workers and this gradient becomes "delayed". We
propose a novel technology to compensate this delay, so as to make the
optimization behavior of ASGD closer to that of sequential SGD. This is
achieved by leveraging Taylor expansion of the gradient function and efficient
approximation to the Hessian matrix of the loss function. We call the new
algorithm Delay Compensated ASGD (DC-ASGD). We evaluated the proposed algorithm
on CIFAR-10 and ImageNet datasets, and the experimental results demonstrate
that DC-ASGD outperforms both synchronous SGD and asynchronous SGD, and nearly
approaches the performance of sequential SGD.
| Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhi-Ming
Ma, Tie-Yan Liu | null | 1609.08326 | null | null |
Multi-task Recurrent Model for True Multilingual Speech Recognition | cs.CL cs.LG cs.NE | Research on multilingual speech recognition remains attractive yet
challenging. Recent studies focus on learning shared structures under the
multi-task paradigm, in particular a feature sharing structure. This approach
has been found effective to improve performance on each individual language.
However, this approach is only useful when the deployed system supports just
one language. In a true multilingual scenario where multiple languages are
allowed, performance will be significantly reduced due to the competition among
languages in the decoding space. This paper presents a multi-task recurrent
model that involves a multilingual speech recognition (ASR) component and a
language recognition (LR) component, and the ASR component is informed of the
language information by the LR component, leading to a language-aware
recognition. We tested the approach on an English-Chinese bilingual recognition
task. The results show that the proposed multi-task recurrent model can improve
performance of multilingual recognition systems.
| Zhiyuan Tang, Lantian Li and Dong Wang | null | 1609.08337 | null | null |
Multi-label Methods for Prediction with Sequential Data | cs.LG cs.DS stat.ML | The number of methods available for classification of multi-label data has
increased rapidly over recent years, yet relatively few links have been made
with the related task of classification of sequential data. If labels indices
are considered as time indices, the problems can often be seen as equivalent.
In this paper we detect and elaborate on connections between multi-label
methods and Markovian models, and study the suitability of multi-label methods
for prediction in sequential data. From this study we draw upon the most
suitable techniques from the area and develop two novel competitive approaches
which can be applied to either kind of data. We carry out an empirical
evaluation investigating performance on real-world sequential-prediction tasks:
electricity demand, and route prediction. As well as showing that several
popular multi-label algorithms are in fact easily applicable to sequencing
tasks, our novel approaches, which benefit from a unified view of these areas,
prove very competitive against established methods.
| Jesse Read, Luca Martino, Jaakko Hollm\'en | 10.1016/j.patcog.2016.09.015 | 1609.08349 | null | null |
Appraisal of data-driven and mechanistic emulators of nonlinear
hydrodynamic urban drainage simulators | stat.ME cs.CE cs.LG | Many model based scientific and engineering methodologies, such as system
identification, sensitivity analysis, optimization and control, require a large
number of model evaluations. In particular, model based real-time control of
urban water infrastructures and online flood alarm systems require fast
prediction of the network response at different actuation and/or parameter
values. General purpose urban drainage simulators are too slow for this
application. Fast surrogate models, so-called emulators, provide a solution to
this efficiency demand. Emulators are attractive, because they sacrifice
unneeded accuracy in favor of speed. However, they have to be fine-tuned to
predict the system behavior satisfactorily. Also, some emulators fail to
extrapolate the system behavior beyond the training set. Although, there are
many strategies for developing emulators, up until now the selection of the
emulation strategy remains subjective. In this paper, we therefore compare the
performance of two families of emulators for open channel flows in the context
of urban drainage simulators. We compare emulators that explicitly use
knowledge of the simulator's equations, i.e. mechanistic emulators based on
Gaussian Processes, with purely data-driven emulators using matrix
factorization. Our results suggest that in many urban applications, naive
data-driven emulation outperforms mechanistic emulation. Nevertheless, we
discuss scenarios in which we think that mechanistic emulation might be
favorable for i) extrapolation in time and ii) dealing with sparse and unevenly
sampled data. We also provide many references to advances in the field of
Machine Learning that have not yet permeated into the Bayesian environmental
science community.
| Juan Pablo Carbajal, Jo\~ao Paulo Leit\~ao, Carlo Albert, J\"org
Rieckermann | 10.1016/j.envsoft.2017.02.006 | 1609.08395 | null | null |
Generalization Error Bounds for Optimization Algorithms via Stability | stat.ML cs.LG | Many machine learning tasks can be formulated as Regularized Empirical Risk
Minimization (R-ERM), and solved by optimization algorithms such as gradient
descent (GD), stochastic gradient descent (SGD), and stochastic variance
reduction (SVRG). Conventional analysis on these optimization algorithms
focuses on their convergence rates during the training process, however, people
in the machine learning community may care more about the generalization
performance of the learned model on unseen test data. In this paper, we
investigate on this issue, by using stability as a tool. In particular, we
decompose the generalization error for R-ERM, and derive its upper bound for
both convex and non-convex cases. In convex cases, we prove that the
generalization error can be bounded by the convergence rate of the optimization
algorithm and the stability of the R-ERM process, both in expectation (in the
order of $\mathcal{O}((1/n)+\mathbb{E}\rho(T))$, where $\rho(T)$ is the
convergence error and $T$ is the number of iterations) and in high probability
(in the order of
$\mathcal{O}\left(\frac{\log{1/\delta}}{\sqrt{n}}+\rho(T)\right)$ with
probability $1-\delta$). For non-convex cases, we can also obtain a similar
expected generalization error bound. Our theorems indicate that 1) along with
the training process, the generalization error will decrease for all the
optimization algorithms under our investigation; 2) Comparatively speaking,
SVRG has better generalization ability than GD and SGD. We have conducted
experiments on both convex and non-convex problems, and the experimental
results verify our theoretical findings.
| Qi Meng, Yue Wang, Wei Chen, Taifeng Wang, Zhi-Ming Ma, and Tie-Yan
Liu | null | 1609.08397 | null | null |
Deep learning for detection of bird vocalisations | cs.SD cs.LG | This work focuses on reliable detection of bird sound emissions as recorded
in the open field. Acoustic detection of avian sounds can be used for the
automatized monitoring of multiple bird taxa and querying in long-term
recordings for species of interest for researchers, conservation practitioners,
and decision makers. Recordings in the wild can be very noisy due to the
exposure of the microphones to a large number of audio sources originating from
all distances and directions, the number and identity of which cannot be known
a-priori. The co-existence of the target vocalizations with abiotic
interferences in an unconstrained environment is inefficiently treated by
current approaches of audio signal enhancement. A technique that would spot
only bird vocalization while ignoring other audio sources is of prime
importance. These difficulties are tackled in this work, presenting a deep
autoencoder that maps the audio spectrogram of bird vocalizations to its
corresponding binary mask that encircles the spectral blobs of vocalizations
while suppressing other audio sources. The procedure requires minimum human
attendance, it is very fast during execution, thus suitable to scan massive
volumes of data, in order to analyze them, evaluate insights and hypotheses,
identify patterns of bird activity that, hopefully, finally lead to design
policies on biodiversity issues.
| Ilyas Potamitis | null | 1609.08408 | null | null |
Asynchronous Stochastic Proximal Optimization Algorithms with Variance
Reduction | cs.LG | Regularized empirical risk minimization (R-ERM) is an important branch of
machine learning, since it constrains the capacity of the hypothesis space and
guarantees the generalization ability of the learning algorithm. Two classic
proximal optimization algorithms, i.e., proximal stochastic gradient descent
(ProxSGD) and proximal stochastic coordinate descent (ProxSCD) have been widely
used to solve the R-ERM problem. Recently, variance reduction technique was
proposed to improve ProxSGD and ProxSCD, and the corresponding ProxSVRG and
ProxSVRCD have better convergence rate. These proximal algorithms with variance
reduction technique have also achieved great success in applications at small
and moderate scales. However, in order to solve large-scale R-ERM problems and
make more practical impacts, the parallel version of these algorithms are
sorely needed. In this paper, we propose asynchronous ProxSVRG (Async-ProxSVRG)
and asynchronous ProxSVRCD (Async-ProxSVRCD) algorithms, and prove that
Async-ProxSVRG can achieve near linear speedup when the training data is
sparse, while Async-ProxSVRCD can achieve near linear speedup regardless of the
sparse condition, as long as the number of block partitions are appropriately
set. We have conducted experiments on a regularized logistic regression task.
The results verified our theoretical findings and demonstrated the practical
efficiency of the asynchronous stochastic proximal algorithms with variance
reduction.
| Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhi-Ming Ma, Tie-Yan
Liu | null | 1609.08435 | null | null |
Weakly Supervised PLDA Training | cs.LG cs.AI cs.CL cs.SD | PLDA is a popular normalization approach for the i-vector model, and it has
delivered state-of-the-art performance in speaker verification. However, PLDA
training requires a large amount of labelled development data, which is highly
expensive in most cases. We present a cheap PLDA training approach, which
assumes that speakers in the same session can be easily separated, and speakers
in different sessions are simply different. This results in `weak labels' which
are not fully accurate but cheap, leading to a weak PLDA training.
Our experimental results on real-life large-scale telephony customer service
achieves demonstrated that the weak training can offer good performance when
human-labelled data are limited. More interestingly, the weak training can be
employed as a discriminative adaptation approach, which is more efficient than
the prevailing unsupervised method when human-labelled data are insufficient.
| Lantian Li, Yixiang Chen, Dong Wang, Chenghui Zhao | null | 1609.08441 | null | null |
Topic Modeling over Short Texts by Incorporating Word Embeddings | cs.CL cs.IR cs.LG | Inferring topics from the overwhelming amount of short texts becomes a
critical but challenging task for many content analysis tasks, such as content
charactering, user interest profiling, and emerging topic detecting. Existing
methods such as probabilistic latent semantic analysis (PLSA) and latent
Dirichlet allocation (LDA) cannot solve this prob- lem very well since only
very limited word co-occurrence information is available in short texts. This
paper studies how to incorporate the external word correlation knowledge into
short texts to improve the coherence of topic modeling. Based on recent results
in word embeddings that learn se- mantically representations for words from a
large corpus, we introduce a novel method, Embedding-based Topic Model (ETM),
to learn latent topics from short texts. ETM not only solves the problem of
very limited word co-occurrence information by aggregating short texts into
long pseudo- texts, but also utilizes a Markov Random Field regularized model
that gives correlated words a better chance to be put into the same topic. The
experiments on real-world datasets validate the effectiveness of our model
comparing with the state-of-the-art models.
| Jipeng Qiang, Ping Chen, Tong Wang, Xindong Wu | null | 1609.08496 | null | null |
Correct classification for big/smart/fast data machine learning | cs.LG cs.IT math.IT | Table (database) / Relational database Classification for big/smart/fast data
machine learning is one of the most important tasks of predictive analytics and
extracting valuable information from data. It is core applied technique for
what now understood under data science and/or artificial intelligence. Widely
used Decision Tree (Random Forest) and rare used rule based PRISM , VFST, etc
classifiers are empirical substitutions of theoretically correct to use Boolean
functions minimization. Developing Minimization of Boolean functions algorithms
is started long time ago by Edward Veitch's 1952. Since it, big efforts by wide
scientific/industrial community was done to find feasible solution of Boolean
functions minimization. In this paper we propose consider table data
classification from mathematical point of view, as minimization of Boolean
functions. It is shown that data representation may be transformed to Boolean
functions form and how to use known algorithms. For simplicity, binary output
function is used for development, what opens doors for multivalued outputs
developments.
| Sander Stepanov | null | 1609.0855 | null | null |
Learning Genomic Representations to Predict Clinical Outcomes in Cancer | cs.NE cs.LG | Genomics are rapidly transforming medical practice and basic biomedical
research, providing insights into disease mechanisms and improving therapeutic
strategies, particularly in cancer. The ability to predict the future course of
a patient's disease from high-dimensional genomic profiling will be essential
in realizing the promise of genomic medicine, but presents significant
challenges for state-of-the-art survival analysis methods. In this abstract we
present an investigation in learning genomic representations with neural
networks to predict patient survival in cancer. We demonstrate the advantages
of this approach over existing survival analysis methods using brain tumor
data.
| Safoora Yousefi, Congzheng Song, Nelson Nauata, Lee Cooper | null | 1609.08663 | null | null |
Memory Visualization for Gated Recurrent Neural Networks in Speech
Recognition | cs.LG cs.CL cs.NE | Recurrent neural networks (RNNs) have shown clear superiority in sequence
modeling, particularly the ones with gated units, such as long short-term
memory (LSTM) and gated recurrent unit (GRU). However, the dynamic properties
behind the remarkable performance remain unclear in many applications, e.g.,
automatic speech recognition (ASR). This paper employs visualization techniques
to study the behavior of LSTM and GRU when performing speech recognition tasks.
Our experiments show some interesting patterns in the gated memory, and some of
them have inspired simple yet effective modifications on the network structure.
We report two of such modifications: (1) lazy cell update in LSTM, and (2)
shortcut connections for residual learning. Both modifications lead to more
comprehensible and powerful networks.
| Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng and Shiyue Zhang | null | 1609.08789 | null | null |
Statistical comparison of classifiers through Bayesian hierarchical
modelling | cs.LG stat.ME stat.ML | Usually one compares the accuracy of two competing classifiers via null
hypothesis significance tests (nhst). Yet the nhst tests suffer from important
shortcomings, which can be overcome by switching to Bayesian hypothesis
testing. We propose a Bayesian hierarchical model which jointly analyzes the
cross-validation results obtained by two classifiers on multiple data sets. It
returns the posterior probability of the accuracies of the two classifiers
being practically equivalent or significantly different. A further strength of
the hierarchical model is that, by jointly analyzing the results obtained on
all data sets, it reduces the estimation error compared to the usual approach
of averaging the cross-validation results obtained on a given data set.
| Giorgio Corani and Alessio Benavoli and Janez Dem\v{s}ar and Francesca
Mangili and Marco Zaffalon | null | 1609.08905 | null | null |
The Famine of Forte: Few Search Problems Greatly Favor Your Algorithm | stat.ML cs.IT cs.LG math.IT | Casting machine learning as a type of search, we demonstrate that the
proportion of problems that are favorable for a fixed algorithm is strictly
bounded, such that no single algorithm can perform well over a large fraction
of them. Our results explain why we must either continue to develop new
learning methods year after year or move towards highly parameterized models
that are both flexible and sensitive to their hyperparameters. We further give
an upper bound on the expected performance for a search algorithm as a function
of the mutual information between the target and the information resource
(e.g., training dataset), proving the importance of certain types of dependence
for machine learning. Lastly, we show that the expected per-query probability
of success for an algorithm is mathematically equivalent to a single-query
probability of success under a distribution (called a search strategy), and
prove that the proportion of favorable strategies is also strictly bounded.
Thus, whether one holds fixed the search algorithm and considers all possible
problems or one fixes the search problem and looks at all possible search
strategies, favorable matches are exceedingly rare. The forte (strength) of any
algorithm is quantifiably restricted.
| George D. Montanez | null | 1609.08913 | null | null |
Multiplicative weights, equalizers, and P=PPAD | cs.GT cs.CC cs.LG | We show that, by using multiplicative weights in a game-theoretic thought
experiment (and an important convexity result on the composition of
multiplicative weights with the relative entropy function), a symmetric
bimatrix game (that is, a bimatrix matrix wherein the payoff matrix of each
player is the transpose of the payoff matrix of the other) either has an
interior symmetric equilibrium or there is a pure strategy that is weakly
dominated by some mixed strategy. Weakly dominated pure strategies can be
detected and eliminated in polynomial time by solving a linear program.
Furthermore, interior symmetric equilibria are a special case of a more general
notion, namely, that of an "equalizer," which can also be computed efficiently
in polynomial time by solving a linear program. An elegant "symmetrization
method" of bimatrix games [Jurg et al., 1992] and the well-known
PPAD-completeness results on equilibrium computation in bimatrix games
[Daskalakis et al., 2009, Chen et al., 2009] imply then the compelling P =
PPAD.
| Ioannis Avramopoulos | null | 1609.08934 | null | null |
Variational Autoencoder for Deep Learning of Images, Labels and Captions | stat.ML cs.LG | A novel variational autoencoder is developed to model images, as well as
associated labels or captions. The Deep Generative Deconvolutional Network
(DGDN) is used as a decoder of the latent image features, and a deep
Convolutional Neural Network (CNN) is used as an image encoder; the CNN is used
to approximate a distribution for the latent DGDN features/code. The latent
code is also linked to generative models for labels (Bayesian support vector
machine) or captions (recurrent neural network). When predicting a
label/caption for a new image at test, averaging is performed across the
distribution of latent codes; this is computationally efficient as a
consequence of the learned CNN-based encoder. Since the framework is capable of
modeling the image in the presence/absence of associated labels/captions, a new
semi-supervised setting is manifested for CNN learning with images; the
framework even allows unsupervised CNN learning, based on images alone.
| Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew
Stevens, Lawrence Carin | null | 1609.08976 | null | null |
Learning from the Hindsight Plan -- Episodic MPC Improvement | cs.RO cs.AI cs.LG | Model predictive control (MPC) is a popular control method that has proved
effective for robotics, among other fields. MPC performs re-planning at every
time step. Re-planning is done with a limited horizon per computational and
real-time constraints and often also for robustness to potential model errors.
However, the limited horizon leads to suboptimal performance. In this work, we
consider the iterative learning setting, where the same task can be repeated
several times, and propose a policy improvement scheme for MPC. The main idea
is that between executions we can, offline, run MPC with a longer horizon,
resulting in a hindsight plan. To bring the next real-world execution closer to
the hindsight plan, our approach learns to re-shape the original cost function
with the goal of satisfying the following property: short horizon planning (as
realistic during real executions) with respect to the shaped cost should result
in mimicking the hindsight plan. This effectively consolidates long-term
reasoning into the short-horizon planning. We empirically evaluate our approach
in contact-rich manipulation tasks both in simulated and real environments,
such as peg insertion by a real PR2 robot.
| Aviv Tamar, Garrett Thomas, Tianhao Zhang, Sergey Levine, Pieter
Abbeel | null | 1609.09001 | null | null |
Unsupervised Neural Hidden Markov Models | cs.CL cs.LG | In this work, we present the first results for neuralizing an Unsupervised
Hidden Markov Model. We evaluate our approach on tag in- duction. Our approach
outperforms existing generative models and is competitive with the
state-of-the-art though with a simpler model easily extended to include
additional context.
| Ke Tran, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, Kevin Knight | null | 1609.09007 | null | null |
Learning to Push by Grasping: Using multiple tasks for effective
learning | cs.RO cs.CV cs.LG | Recently, end-to-end learning frameworks are gaining prevalence in the field
of robot control. These frameworks input states/images and directly predict the
torques or the action parameters. However, these approaches are often critiqued
due to their huge data requirements for learning a task. The argument of the
difficulty in scalability to multiple tasks is well founded, since training
these tasks often require hundreds or thousands of examples. But do end-to-end
approaches need to learn a unique model for every task? Intuitively, it seems
that sharing across tasks should help since all tasks require some common
understanding of the environment. In this paper, we attempt to take the next
step in data-driven end-to-end learning frameworks: move from the realm of
task-specific models to joint learning of multiple robot tasks. In an
astonishing result we show that models with multi-task learning tend to perform
better than task-specific models trained with same amounts of data. For
example, a deep-network learned with 2.5K grasp and 2.5K push examples performs
better on grasping than a network trained on 5K grasp examples.
| Lerrel Pinto and Abhinav Gupta | null | 1609.09025 | null | null |
Deep Reinforcement Learning for Tensegrity Robot Locomotion | cs.RO cs.LG | Tensegrity robots, composed of rigid rods connected by elastic cables, have a
number of unique properties that make them appealing for use as planetary
exploration rovers. However, control of tensegrity robots remains a difficult
problem due to their unusual structures and complex dynamics. In this work, we
show how locomotion gaits can be learned automatically using a novel extension
of mirror descent guided policy search (MDGPS) applied to periodic locomotion
movements, and we demonstrate the effectiveness of our approach on tensegrity
robot locomotion. We evaluate our method with real-world and simulated
experiments on the SUPERball tensegrity robot, showing that the learned
policies generalize to changes in system parameters, unreliable sensor
measurements, and variation in environmental conditions, including varied
terrains and a range of different gravities. Our experiments demonstrate that
our method not only learns fast, power-efficient feedback policies for rolling
gaits, but that these policies can succeed with only the limited onboard
sensing provided by SUPERball's accelerometers. We compare the learned feedback
policies to learned open-loop policies and hand-engineered controllers, and
demonstrate that the learned policy enables the first continuous, reliable
locomotion gait for the real SUPERball robot. Our code and other supplementary
materials are available from http://rll.berkeley.edu/drl_tensegrity
| Marvin Zhang, Xinyang Geng, Jonathan Bruce, Ken Caluwaerts, Massimo
Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine | null | 1609.09049 | null | null |
HyperNetworks | cs.LG | This work explores hypernetworks: an approach of using a one network, also
known as a hypernetwork, to generate the weights for another network.
Hypernetworks provide an abstraction that is similar to what is found in
nature: the relationship between a genotype - the hypernetwork - and a
phenotype - the main network. Though they are also reminiscent of HyperNEAT in
evolution, our hypernetworks are trained end-to-end with backpropagation and
thus are usually faster. The focus of this work is to make hypernetworks useful
for deep convolutional networks and long recurrent networks, where
hypernetworks can be viewed as relaxed form of weight-sharing across layers.
Our main result is that hypernetworks can generate non-shared weights for LSTM
and achieve near state-of-the-art results on a variety of sequence modelling
tasks including character-level language modelling, handwriting generation and
neural machine translation, challenging the weight-sharing paradigm for
recurrent networks. Our results also show that hypernetworks applied to
convolutional networks still achieve respectable results for image recognition
tasks compared to state-of-the-art baseline models while requiring fewer
learnable parameters.
| David Ha, Andrew Dai and Quoc V. Le | null | 1609.09106 | null | null |
Analysis of Massive Heterogeneous Temporal-Spatial Data with 3D
Self-Organizing Map and Time Vector | cs.LG cs.NE | Self-organizing map(SOM) have been widely applied in clustering, this paper
focused on centroids of clusters and what they reveal. When the input vectors
consists of time, latitude and longitude, the map can be strongly linked to
physical world, providing valuable information. Beyond basic clustering, a
novel approach to address the temporal element is developed, enabling 3D SOM to
track behaviors in multiple periods concurrently. Combined with adaptations
targeting to process heterogeneous data relating to distribution in time and
space, the paper offers a fresh scope for business and services based on
temporal-spatial pattern.
| Yu Ding | null | 1609.09116 | null | null |
Similarity Mapping with Enhanced Siamese Network for Multi-Object
Tracking | cs.CV cs.LG | Multi-object tracking has recently become an important area of computer
vision, especially for Advanced Driver Assistance Systems (ADAS). Despite
growing attention, achieving high performance tracking is still challenging,
with state-of-the- art systems resulting in high complexity with a large number
of hyper parameters. In this paper, we focus on reducing overall system
complexity and the number hyper parameters that need to be tuned to a specific
environment. We introduce a novel tracking system based on similarity mapping
by Enhanced Siamese Neural Network (ESNN), which accounts for both appearance
and geometric information, and is trainable end-to-end. Our system achieves
competitive performance in both speed and accuracy on MOT16 challenge, compared
to known state-of-the-art methods.
| Minyoung Kim, Stefano Alletto, Luca Rigazio | null | 1609.09156 | null | null |
Universum Learning for Multiclass SVM | cs.LG | We introduce Universum learning for multiclass problems and propose a novel
formulation for multiclass universum SVM (MU-SVM). We also propose a span bound
for MU-SVM that can be used for model selection thereby avoiding resampling.
Empirical results demonstrate the effectiveness of MU-SVM and the proposed
bound.
| Sauptik Dhar, Naveen Ramakrishnan, Vladimir Cherkassky, Mohak Shah | null | 1609.09162 | null | null |
OPML: A One-Pass Closed-Form Solution for Online Metric Learning | cs.LG cs.CV | To achieve a low computational cost when performing online metric learning
for large-scale data, we present a one-pass closed-form solution namely OPML in
this paper. Typically, the proposed OPML first adopts a one-pass triplet
construction strategy, which aims to use only a very small number of triplets
to approximate the representation ability of whole original triplets obtained
by batch-manner methods. Then, OPML employs a closed-form solution to update
the metric for new coming samples, which leads to a low space (i.e., $O(d)$)
and time (i.e., $O(d^2)$) complexity, where $d$ is the feature dimensionality.
In addition, an extension of OPML (namely COPML) is further proposed to enhance
the robustness when in real case the first several samples come from the same
class (i.e., cold start problem). In the experiments, we have systematically
evaluated our methods (OPML and COPML) on three typical tasks, including UCI
data classification, face verification, and abnormal event detection in videos,
which aims to fully evaluate the proposed methods on different sample number,
different feature dimensionalities and different feature extraction ways (i.e.,
hand-crafted and deeply-learned). The results show that OPML and COPML can
obtain the promising performance with a very low computational cost. Also, the
effectiveness of COPML under the cold start setting is experimentally verified.
| Wenbin Li, Yang Gao, Lei Wang, Luping Zhou, Jing Huo, Yinghuan Shi | null | 1609.09178 | null | null |
Topic Browsing for Research Papers with Hierarchical Latent Tree
Analysis | cs.CL cs.IR cs.LG | Academic researchers often need to face with a large collection of research
papers in the literature. This problem may be even worse for postgraduate
students who are new to a field and may not know where to start. To address
this problem, we have developed an online catalog of research papers where the
papers have been automatically categorized by a topic model. The catalog
contains 7719 papers from the proceedings of two artificial intelligence
conferences from 2000 to 2015. Rather than the commonly used Latent Dirichlet
Allocation, we use a recently proposed method called hierarchical latent tree
analysis for topic modeling. The resulting topic model contains a hierarchy of
topics so that users can browse the topics from the top level to the bottom
level. The topic model contains a manageable number of general topics at the
top level and allows thousands of fine-grained topics at the bottom level. It
also can detect topics that have emerged recently.
| Leonard K.M. Poon and Nevin L. Zhang | null | 1609.09188 | null | null |
Multi Model Data mining approach for Heart failure prediction | cs.LG cs.CY | Developing predictive modelling solutions for risk estimation is extremely
challenging in health-care informatics. Risk estimation involves integration of
heterogeneous clinical sources having different representation from different
health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time.
Therefore, distributed and parallel computing tools collectively termed big
data tools are in need which can synthesize and assist the physician to make
right clinical decisions. In this work we propose multi-model predictive
architecture, a novel approach for combining the predictive ability of multiple
models for better prediction accuracy. We demonstrate the effectiveness and
efficiency of the proposed work on data from Framingham Heart study. Results
show that the proposed multi-model predictive architecture is able to provide
better accuracy than best model approach. By modelling the error of predictive
models we are able to choose sub set of models which yields accurate results.
More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
| Priyanka H U and Vivek R | 10.5121/ijdkp.2016.6503 | 1609.09194 | null | null |
EXTRACT: Strong Examples from Weakly-Labeled Sensor Data | stat.ML cs.DB cs.LG | Thanks to the rise of wearable and connected devices, sensor-generated time
series comprise a large and growing fraction of the world's data.
Unfortunately, extracting value from this data can be challenging, since
sensors report low-level signals (e.g., acceleration), not the high-level
events that are typically of interest (e.g., gestures). We introduce a
technique to bridge this gap by automatically extracting examples of real-world
events in low-level data, given only a rough estimate of when these events have
taken place.
By identifying sets of features that repeat in the same temporal arrangement,
we isolate examples of such diverse events as human actions, power consumption
patterns, and spoken words with up to 96% precision and recall. Our method is
fast enough to run in real time and assumes only minimal knowledge of which
variables are relevant or the lengths of events. Our evaluation uses numerous
publicly available datasets and over 1 million samples of manually labeled
sensor data.
| Davis W. Blalock, John V. Guttag | null | 1609.09196 | null | null |
Structure-Aware Classification using Supervised Dictionary Learning | cs.LG cs.CV | In this paper, we propose a supervised dictionary learning algorithm that
aims to preserve the local geometry in both dimensions of the data. A
graph-based regularization explicitly takes into account the local manifold
structure of the data points. A second graph regularization gives similar
treatment to the feature domain and helps in learning a more robust dictionary.
Both graphs can be constructed from the training data or learned and adapted
along the dictionary learning process. The combination of these two terms
promotes the discriminative power of the learned sparse representations and
leads to improved classification accuracy. The proposed method was evaluated on
several different datasets, representing both single-label and multi-label
classification problems, and demonstrated better performance compared with
other dictionary based approaches.
| Yael Yankelevsky and Michael Elad | 10.1109/ICASSP.2017.7952992 | 1609.09199 | null | null |
Training Dependency Parsers with Partial Annotation | cs.CL cs.LG | Recently, these has been a surge on studying how to obtain partially
annotated data for model supervision. However, there still lacks a systematic
study on how to train statistical models with partial annotation (PA). Taking
dependency parsing as our case study, this paper describes and compares two
straightforward approaches for three mainstream dependency parsers. The first
approach is previously proposed to directly train a log-linear graph-based
parser (LLGPar) with PA based on a forest-based objective. This work for the
first time proposes the second approach to directly training a linear
graph-based parse (LGPar) and a linear transition-based parser (LTPar) with PA
based on the idea of constrained decoding. We conduct extensive experiments on
Penn Treebank under three different settings for simulating PA, i.e., random
dependencies, most uncertain dependencies, and dependencies with divergent
outputs from the three parsers. The results show that LLGPar is most effective
in learning from PA and LTPar lags behind the graph-based counterparts by large
margin. Moreover, LGPar and LTPar can achieve best performance by using LLGPar
to complete PA into full annotation (FA).
| Zhenghua Li, Yue Zhang, Jiayuan Chao, Min Zhang | null | 1609.09247 | null | null |
Machine Learning Techniques for Stackelberg Security Games: a Survey | cs.GT cs.LG | The present survey aims at presenting the current machine learning techniques
employed in security games domains. Specifically, we focused on papers and
works developed by the Teamcore of University of Southern California, which
deepened different directions in this field. After a brief introduction on
Stackelberg Security Games (SSGs) and the poaching setting, the rest of the
work presents how to model a boundedly rational attacker taking into account
her human behavior, then describes how to face the problem of having attacker's
payoffs not defined and how to estimate them and, finally, presents how online
learning techniques have been exploited to learn a model of the attacker.
| Giuseppe De Nittis and Francesco Trov\`o | null | 1609.09341 | null | null |
Deep Multi-Species Embedding | cs.LG q-bio.PE stat.ML | Understanding how species are distributed across landscapes over time is a
fundamental question in biodiversity research. Unfortunately, most species
distribution models only target a single species at a time, despite strong
ecological evidence that species are not independently distributed. We propose
Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding
to multiple species as well as vectors representing environmental covariates
into a common high-dimensional feature space via a deep neural network. Applied
to bird observational data from the citizen science project \textit{eBird}, we
demonstrate how the DMSE model discovers inter-species relationships to
outperform single-species distribution models (random forests and SVMs) as well
as competing multi-label models. Additionally, we demonstrate the benefit of
using a deep neural network to extract features within the embedding and show
how they improve the predictive performance of species distribution modelling.
An important domain contribution of the DMSE model is the ability to discover
and describe species interactions while simultaneously learning the shared
habitat preferences among species. As an additional contribution, we provide a
graphical embedding of hundreds of bird species in the Northeast US.
| Di Chen, Yexiang Xue, Shuo Chen, Daniel Fink, Carla Gomes | null | 1609.09353 | null | null |
Deep Tracking on the Move: Learning to Track the World from a Moving
Vehicle using Recurrent Neural Networks | cs.CV cs.AI cs.LG cs.RO | This paper presents an end-to-end approach for tracking static and dynamic
objects for an autonomous vehicle driving through crowded urban environments.
Unlike traditional approaches to tracking, this method is learned end-to-end,
and is able to directly predict a full unoccluded occupancy grid map from raw
laser input data. Inspired by the recently presented DeepTracking approach
[Ondruska, 2016], we employ a recurrent neural network (RNN) to capture the
temporal evolution of the state of the environment, and propose to use Spatial
Transformer modules to exploit estimates of the egomotion of the vehicle. Our
results demonstrate the ability to track a range of objects, including cars,
buses, pedestrians, and cyclists through occlusion, from both moving and
stationary platforms, using a single learned model. Experimental results
demonstrate that the model can also predict the future states of objects from
current inputs, with greater accuracy than previous work.
| Julie Dequaire, Dushyant Rao, Peter Ondruska, Dominic Wang and Ingmar
Posner | null | 1609.09365 | null | null |
CNN Architectures for Large-Scale Audio Classification | cs.SD cs.LG stat.ML | Convolutional Neural Networks (CNNs) have proven very effective in image
classification and show promise for audio. We use various CNN architectures to
classify the soundtracks of a dataset of 70M training videos (5.24 million
hours) with 30,871 video-level labels. We examine fully connected Deep Neural
Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We
investigate varying the size of both training set and label vocabulary, finding
that analogs of the CNNs used in image classification do well on our audio
classification task, and larger training and label sets help up to a point. A
model using embeddings from these classifiers does much better than raw
features on the Audio Set [5] Acoustic Event Detection (AED) classification
task.
| Shawn Hershey, Sourish Chaudhuri, Daniel P. W. Ellis, Jort F. Gemmeke,
Aren Jansen, R. Channing Moore, Manoj Plakal, Devin Platt, Rif A. Saurous,
Bryan Seybold, Malcolm Slaney, Ron J. Weiss, Kevin Wilson | null | 1609.0943 | null | null |
Contextual RNN-GANs for Abstract Reasoning Diagram Generation | cs.CV cs.AI cs.LG | Understanding, predicting, and generating object motions and transformations
is a core problem in artificial intelligence. Modeling sequences of evolving
images may provide better representations and models of motion and may
ultimately be used for forecasting, simulation, or video generation.
Diagrammatic Abstract Reasoning is an avenue in which diagrams evolve in
complex patterns and one needs to infer the underlying pattern sequence and
generate the next image in the sequence. For this, we develop a novel
Contextual Generative Adversarial Network based on Recurrent Neural Networks
(Context-RNN-GANs), where both the generator and the discriminator modules are
based on contextual history (modeled as RNNs) and the adversarial discriminator
guides the generator to produce realistic images for the particular time step
in the image sequence. We evaluate the Context-RNN-GAN model (and its variants)
on a novel dataset of Diagrammatic Abstract Reasoning, where it performs
competitively with 10th-grade human performance but there is still scope for
interesting improvements as compared to college-grade human performance. We
also evaluate our model on a standard video next-frame prediction task,
achieving improved performance over comparable state-of-the-art.
| Arnab Ghosh and Viveka Kulharia and Amitabha Mukerjee and Vinay
Namboodiri and Mohit Bansal | null | 1609.09444 | null | null |
Classifier comparison using precision | cs.LG stat.ML | New proposed models are often compared to state-of-the-art using statistical
significance testing. Literature is scarce for classifier comparison using
metrics other than accuracy. We present a survey of statistical methods that
can be used for classifier comparison using precision, accounting for
inter-precision correlation arising from use of same dataset. Comparisons are
made using per-class precision and methods presented to test global null
hypothesis of an overall model comparison. Comparisons are extended to multiple
multi-class classifiers and to models using cross validation or its variants.
Partial Bayesian update to precision is introduced when population prevalence
of a class is known. Applications to compare deep architectures are studied.
| Lovedeep Gondara | null | 1609.09471 | null | null |
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the
Amazon Picking Challenge | cs.CV cs.LG cs.RO | Robot warehouse automation has attracted significant interest in recent
years, perhaps most visibly in the Amazon Picking Challenge (APC). A fully
autonomous warehouse pick-and-place system requires robust vision that reliably
recognizes and locates objects amid cluttered environments, self-occlusions,
sensor noise, and a large variety of objects. In this paper we present an
approach that leverages multi-view RGB-D data and self-supervised, data-driven
learning to overcome those difficulties. The approach was part of the
MIT-Princeton Team system that took 3rd- and 4th- place in the stowing and
picking tasks, respectively at APC 2016. In the proposed approach, we segment
and label multiple views of a scene with a fully convolutional neural network,
and then fit pre-scanned 3D object models to the resulting segmentation to get
the 6D object pose. Training a deep neural network for segmentation typically
requires a large amount of training data. We propose a self-supervised method
to generate a large labeled dataset without tedious manual segmentation. We
demonstrate that our system can reliably estimate the 6D pose of objects under
a variety of scenarios. All code, data, and benchmarks are available at
http://apc.cs.princeton.edu/
| Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker Jr.,
Alberto Rodriguez and Jianxiong Xiao | null | 1609.09475 | null | null |
Fast learning rates with heavy-tailed losses | stat.ML cs.LG | We study fast learning rates when the losses are not necessarily bounded and
may have a distribution with heavy tails. To enable such analyses, we introduce
two new conditions: (i) the envelope function $\sup_{f \in \mathcal{F}}|\ell
\circ f|$, where $\ell$ is the loss function and $\mathcal{F}$ is the
hypothesis class, exists and is $L^r$-integrable, and (ii) $\ell$ satisfies the
multi-scale Bernstein's condition on $\mathcal{F}$. Under these assumptions, we
prove that learning rate faster than $O(n^{-1/2})$ can be obtained and,
depending on $r$ and the multi-scale Bernstein's powers, can be arbitrarily
close to $O(n^{-1})$. We then verify these assumptions and derive fast learning
rates for the problem of vector quantization by $k$-means clustering with
heavy-tailed distributions. The analyses enable us to obtain novel learning
rates that extend and complement existing results in the literature from both
theoretical and practical viewpoints.
| Vu Dinh, Lam Si Tung Ho, Duy Nguyen, Binh T. Nguyen | null | 1609.09481 | null | null |
Max-plus statistical leverage scores | stat.ML cs.LG | The statistical leverage scores of a complex matrix $A\in\mathbb{C}^{n\times
d}$ record the degree of alignment between col$(A)$ and the coordinate axes in
$\mathbb{C}^n$. These score are used in random sampling algorithms for solving
certain numerical linear algebra problems. In this paper we present a max-plus
algebraic analogue for statistical leverage scores. We show that max-plus
statistical leverage scores can be used to calculate the exact asymptotic
behavior of the conventional statistical leverage scores of a generic matrices
of Puiseux series and also provide a novel way to approximate the conventional
statistical leverage scores of a fixed or complex matrix. The advantage of
approximating a complex matrices scores with max-plus scores is that the
max-plus scores can be computed very quickly. This approximation is typically
accurate to within an order or magnitude and should be useful in practical
problems where the true scores are known to vary widely.
| James Hook | null | 1609.09519 | null | null |
Charged Point Normalization: An Efficient Solution to the Saddle Point
Problem | cs.LG | Recently, the problem of local minima in very high dimensional non-convex
optimization has been challenged and the problem of saddle points has been
introduced. This paper introduces a dynamic type of normalization that forces
the system to escape saddle points. Unlike other saddle point escaping
algorithms, second order information is not utilized, and the system can be
trained with an arbitrary gradient descent learner. The system drastically
improves learning in a range of deep neural networks on various data-sets in
comparison to non-CPN neural networks.
| Armen Aghajanyan | null | 1609.09522 | null | null |
Multi-dimensional signal approximation with sparse structured priors
using split Bregman iterations | cs.DS cs.CV cs.LG | This paper addresses the structurally-constrained sparse decomposition of
multi-dimensional signals onto overcomplete families of vectors, called
dictionaries. The contribution of the paper is threefold. Firstly, a generic
spatio-temporal regularization term is designed and used together with the
standard $\ell_1$ regularization term to enforce a sparse decomposition
preserving the spatio-temporal structure of the signal. Secondly, an
optimization algorithm based on the split Bregman approach is proposed to
handle the associated optimization problem, and its convergence is analyzed.
Our well-founded approach yields same accuracy as the other algorithms at the
state-of-the-art, with significant gains in terms of convergence speed.
Thirdly, the empirical validation of the approach on artificial and real-world
problems demonstrates the generality and effectiveness of the method. On
artificial problems, the proposed regularization subsumes the Total Variation
minimization and recovers the expected decomposition. On the real-world problem
of electro-encephalography brainwave decomposition, the approach outperforms
similar approaches in terms of P300 evoked potentials detection, using
structured spatial priors to guide the decomposition.
| Yoann Isaac, Quentin Barth\'elemy, C\'edric Gouy-Pailler, Mich\`ele
Sebag, Jamal Atif | null | 1609.09525 | null | null |
Algorithms for item categorization based on ordinal ranking data | cs.LG cs.SI | We present a new method for identifying the latent categorization of items
based on their rankings. Complimenting a recent work that uses a Dirichlet
prior on preference vectors and variational inference, we show that this
problem can be effectively dealt with using existing community detection
algorithms, with the communities corresponding to item categories. In
particular we convert the bipartite ranking data to a unipartite graph of item
affinities, and apply community detection algorithms. In this context we modify
an existing algorithm - namely the label propagation algorithm to a variant
that uses the distance between the nodes for weighting the label propagation -
to identify the categories. We propose and analyze a synthetic ordinal ranking
model and show its relation to the recently much studied stochastic block
model. We test our algorithms on synthetic data and compare performance with
several popular community detection algorithms. We also test the method on real
data sets of movie categorization from the Movie Lens database. In all of the
cases our algorithm is able to identify the categories for a suitable choice of
tuning parameter.
| Josh Girson and Shuchin Aeron | null | 1609.09544 | null | null |
Asynchronous Multi-Task Learning | cs.LG cs.DC | Many real-world machine learning applications involve several learning tasks
which are inter-related. For example, in healthcare domain, we need to learn a
predictive model of a certain disease for many hospitals. The models for each
hospital may be different because of the inherent differences in the
distributions of the patient populations. However, the models are also closely
related because of the nature of the learning tasks modeling the same disease.
By simultaneously learning all the tasks, multi-task learning (MTL) paradigm
performs inductive knowledge transfer among tasks to improve the generalization
performance. When datasets for the learning tasks are stored at different
locations, it may not always be feasible to transfer the data to provide a
data-centralized computing environment due to various practical issues such as
high data volume and privacy. In this paper, we propose a principled MTL
framework for distributed and asynchronous optimization to address the
aforementioned challenges. In our framework, gradient update does not wait for
collecting the gradient information from all the tasks. Therefore, the proposed
method is very efficient when the communication delay is too high for some task
nodes. We show that many regularized MTL formulations can benefit from this
framework, including the low-rank MTL for shared subspace learning. Empirical
studies on both synthetic and real-world datasets demonstrate the efficiency
and effectiveness of the proposed framework.
| Inci M. Baytas and Ming Yan and Anil K. Jain and Jiayu Zhou | null | 1609.09563 | null | null |
Social Computing for Mobile Big Data in Wireless Networks | cs.SI cs.LG stat.ML | Mobile big data contains vast statistical features in various dimensions,
including spatial, temporal, and the underlying social domain. Understanding
and exploiting the features of mobile data from a social network perspective
will be extremely beneficial to wireless networks, from planning, operation,
and maintenance to optimization and marketing. In this paper, we categorize and
analyze the big data collected from real wireless cellular networks. Then, we
study the social characteristics of mobile big data and highlight several
research directions for mobile big data in the social computing areas.
| Xing Zhang, Zhenglei Yi, Zhi Yan, Geyong Min, Wenbo Wang, Sabita
Maharjan, Yan Zhang | 10.1109/MC.2016.267 | 1609.09597 | null | null |
Big Data analytics. Three use cases with R, Python and Spark | stat.AP cs.LG | Management and analysis of big data are systematically associated with a data
distributed architecture in the Hadoop and now Spark frameworks. This article
offers an introduction for statisticians to these technologies by comparing the
performance obtained by the direct use of three reference environments: R,
Python Scikit-learn, Spark MLlib on three public use cases: character
recognition, recommending films, categorizing products. As main result, it
appears that, if Spark is very efficient for data munging and recommendation by
collaborative filtering (non-negative factorization), current implementations
of conventional learning methods (logistic regression, random forests) in MLlib
or SparkML do not ou poorly compete habitual use of these methods (R, Python
Scikit-learn) in an integrated or undistributed architecture
| Philippe Besse (IMT), Brendan Guillouet (IMT), Jean-Michel Loubes
(IMT) | null | 1609.09619 | null | null |
On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian
Learning Approach | cs.SY cs.LG stat.ML | This paper begins with considering the identification of sparse linear
time-invariant networks described by multivariable ARX models. Such models
possess relatively simple structure thus used as a benchmark to promote further
research. With identifiability of the network guaranteed, this paper presents
an identification method that infers both the Boolean structure of the network
and the internal dynamics between nodes. Identification is performed directly
from data without any prior knowledge of the system, including its order. The
proposed method solves the identification problem using Maximum a posteriori
estimation (MAP) but with inseparable penalties for complexity, both in terms
of element (order of nonzero connections) and group sparsity (network
topology). Such an approach is widely applied in Compressive Sensing (CS) and
known as Sparse Bayesian Learning (SBL). We then propose a novel scheme that
combines sparse Bayesian and group sparse Bayesian to efficiently solve the
problem. The resulted algorithm has a similar form of the standard Sparse Group
Lasso (SGL) while with known noise variance, it simplifies to exact re-weighted
SGL. The method and the developed toolbox can be applied to infer networks from
a wide range of fields, including systems biology applications such as
signaling and genetic regulatory networks.
| J. Jin, Y. Yuan, W. Pan, D. L.T. Pham, C. J. Tomlin, A.Webb, J.
Goncalves | null | 1609.0966 | null | null |
Predicting the consequence of action in digital control state spaces | cs.LG cs.SY | The objective of this dissertation is to shed light on some fundamental
impediments in learning control laws in continuous state spaces. In particular,
if one wants to build artificial devices capable to learn motor tasks the same
way they learn to classify signals and images, one needs to establish control
rules that do not necessitate comparisons between quantities of the surrounding
space. We propose, in that context, to take inspiration from the "end effector
control" principle, as suggested by neuroscience studies, as opposed to the
"displacement control" principle used in the classical control theory.
| Emmanuel Dauc\'e | null | 1609.09681 | null | null |
Optimal spectral transportation with application to music transcription | stat.ML cs.LG cs.SD | Many spectral unmixing methods rely on the non-negative decomposition of
spectral data onto a dictionary of spectral templates. In particular,
state-of-the-art music transcription systems decompose the spectrogram of the
input signal onto a dictionary of representative note spectra. The typical
measures of fit used to quantify the adequacy of the decomposition compare the
data and template entries frequency-wise. As such, small displacements of
energy from a frequency bin to another as well as variations of timber can
disproportionally harm the fit. We address these issues by means of optimal
transportation and propose a new measure of fit that treats the frequency
distributions of energy holistically as opposed to frequency-wise. Building on
the harmonic nature of sound, the new measure is invariant to shifts of energy
to harmonically-related frequencies, as well as to small and local
displacements of energy. Equipped with this new measure of fit, the dictionary
of note templates can be considerably simplified to a set of Dirac vectors
located at the target fundamental frequencies (musical pitch values). This in
turns gives ground to a very fast and simple decomposition algorithm that
achieves state-of-the-art performance on real musical data.
| R\'emi Flamary, C\'edric F\'evotte, Nicolas Courty, Valentin Emiya | null | 1609.09799 | null | null |
On the Worst-case Communication Overhead for Distributed Data Shuffling | cs.IT cs.DC cs.LG math.IT | Distributed learning platforms for processing large scale data-sets are
becoming increasingly prevalent. In typical distributed implementations, a
centralized master node breaks the data-set into smaller batches for parallel
processing across distributed workers to achieve speed-up and efficiency.
Several computational tasks are of sequential nature, and involve multiple
passes over the data. At each iteration over the data, it is common practice to
randomly re-shuffle the data at the master node, assigning different batches
for each worker to process. This random re-shuffling operation comes at the
cost of extra communication overhead, since at each shuffle, new data points
need to be delivered to the distributed workers.
In this paper, we focus on characterizing the information theoretically
optimal communication overhead for the distributed data shuffling problem. We
propose a novel coded data delivery scheme for the case of no excess storage,
where every worker can only store the assigned data batches under processing.
Our scheme exploits a new type of coding opportunity and is applicable to any
arbitrary shuffle, and for any number of workers. We also present an
information theoretic lower bound on the minimum communication overhead for
data shuffling, and show that the proposed scheme matches this lower bound for
the worst-case communication overhead.
| Mohamed Attia and Ravi Tandon | null | 1609.09823 | null | null |
Structured Inference Networks for Nonlinear State Space Models | stat.ML cs.AI cs.LG | Gaussian state space models have been used for decades as generative models
of sequential data. They admit an intuitive probabilistic interpretation, have
a simple functional form, and enjoy widespread adoption. We introduce a unified
algorithm to efficiently learn a broad class of linear and non-linear state
space models, including variants where the emission and transition
distributions are modeled by deep neural networks. Our learning algorithm
simultaneously learns a compiled inference network and the generative model,
leveraging a structured variational approximation parameterized by recurrent
neural networks to mimic the posterior distribution. We apply the learning
algorithm to both synthetic and real-world datasets, demonstrating its
scalability and versatility. We find that using the structured approximation to
the posterior results in models with significantly higher held-out likelihood.
| Rahul G. Krishnan, Uri Shalit, David Sontag | null | 1609.09869 | null | null |
Outlier Detection from Network Data with Subnetwork Interpretation | cs.AI cs.LG | Detecting a small number of outliers from a set of data observations is
always challenging. This problem is more difficult in the setting of multiple
network samples, where computing the anomalous degree of a network sample is
generally not sufficient. In fact, explaining why the network is exceptional,
expressed in the form of subnetwork, is also equally important. In this paper,
we develop a novel algorithm to address these two key problems. We treat each
network sample as a potential outlier and identify subnetworks that mostly
discriminate it from nearby regular samples. The algorithm is developed in the
framework of network regression combined with the constraints on both network
topology and L1-norm shrinkage to perform subnetwork discovery. Our method thus
goes beyond subspace/subgraph discovery and we show that it converges to a
global optimum. Evaluation on various real-world network datasets demonstrates
that our algorithm not only outperforms baselines in both network and high
dimensional setting, but also discovers highly relevant and interpretable local
subnetworks, further enhancing our understanding of anomalous networks.
| Xuan-Hong Dang, Arlei Silva, Ambuj Singh, Ananthram Swami, Prithwish
Basu | null | 1610.00054 | null | null |
Faster Kernels for Graphs with Continuous Attributes via Hashing | cs.LG stat.ML | While state-of-the-art kernels for graphs with discrete labels scale well to
graphs with thousands of nodes, the few existing kernels for graphs with
continuous attributes, unfortunately, do not scale well. To overcome this
limitation, we present hash graph kernels, a general framework to derive
kernels for graphs with continuous attributes from discrete ones. The idea is
to iteratively turn continuous attributes into discrete labels using randomized
hash functions. We illustrate hash graph kernels for the Weisfeiler-Lehman
subtree kernel and for the shortest-path kernel. The resulting novel graph
kernels are shown to be, both, able to handle graphs with continuous attributes
and scalable to large graphs and data sets. This is supported by our
theoretical analysis and demonstrated by an extensive experimental evaluation.
| Christopher Morris, Nils M. Kriege, Kristian Kersting, Petra Mutzel | null | 1610.00064 | null | null |
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows
Prediction | cs.AI cs.LG | Forecasting the flow of crowds is of great importance to traffic management
and public safety, yet a very challenging task affected by many complex
factors, such as inter-region traffic, events and weather. In this paper, we
propose a deep-learning-based approach, called ST-ResNet, to collectively
forecast the in-flow and out-flow of crowds in each and every region through a
city. We design an end-to-end structure of ST-ResNet based on unique properties
of spatio-temporal data. More specifically, we employ the framework of the
residual neural networks to model the temporal closeness, period, and trend
properties of the crowd traffic, respectively. For each property, we design a
branch of residual convolutional units, each of which models the spatial
properties of the crowd traffic. ST-ResNet learns to dynamically aggregate the
output of the three residual neural networks based on data, assigning different
weights to different branches and regions. The aggregation is further combined
with external factors, such as weather and day of the week, to predict the
final traffic of crowds in each and every region. We evaluate ST-ResNet based
on two types of crowd flows in Beijing and NYC, finding that its performance
exceeds six well-know methods.
| Junbo Zhang, Yu Zheng, Dekang Qi | null | 1610.00081 | null | null |
Latent Tree Analysis | cs.LG | Latent tree analysis seeks to model the correlations among a set of random
variables using a tree of latent variables. It was proposed as an improvement
to latent class analysis --- a method widely used in social sciences and
medicine to identify homogeneous subgroups in a population. It provides new and
fruitful perspectives on a number of machine learning areas, including cluster
analysis, topic detection, and deep probabilistic modeling. This paper gives an
overview of the research on latent tree analysis and various ways it is used in
practice.
| Nevin L. Zhang and Leonard K. M. Poon | null | 1610.00085 | null | null |
Very Deep Convolutional Neural Networks for Raw Waveforms | cs.SD cs.LG cs.NE | Learning acoustic models directly from the raw waveform data with minimal
processing is challenging. Current waveform-based models have generally used
very few (~2) convolutional layers, which might be insufficient for building
high-level discriminative features. In this work, we propose very deep
convolutional neural networks (CNNs) that directly use time-domain waveforms as
inputs. Our CNNs, with up to 34 weight layers, are efficient to optimize over
very long sequences (e.g., vector of size 32000), necessary for processing
acoustic waveforms. This is achieved through batch normalization, residual
learning, and a careful design of down-sampling in the initial layers. Our
networks are fully convolutional, without the use of fully connected layers and
dropout, to maximize representation learning. We use a large receptive field in
the first convolutional layer to mimic bandpass filters, but very small
receptive fields subsequently to control the model capacity. We demonstrate the
performance gains with the deeper models. Our evaluation shows that the CNN
with 18 weight layers outperform the CNN with 3 weight layers by over 15% in
absolute accuracy for an environmental sound recognition task and matches the
performance of models using log-mel features.
| Wei Dai, Chia Dai, Shuhui Qu, Juncheng Li, Samarjit Das | null | 1610.00087 | null | null |
A large scale study of SVM based methods for abstract screening in
systematic reviews | cs.IR cs.LG | A major task in systematic reviews is abstract screening, i.e., excluding,
often hundreds or thousand of, irrelevant citations returned from a database
search based on titles and abstracts. Thus, a systematic review platform that
can automate the abstract screening process is of huge importance. Several
methods have been proposed for this task. However, it is very hard to clearly
understand the applicability of these methods in a systematic review platform
because of the following challenges: (1) the use of non-overlapping metrics for
the evaluation of the proposed methods, (2) usage of features that are very
hard to collect, (3) using a small set of reviews for the evaluation, and (4)
no solid statistical testing or equivalence grouping of the methods. In this
paper, we use feature representation that can be extracted per citation. We
evaluate SVM-based methods (commonly used) on a large set of reviews ($61$) and
metrics ($11$) to provide equivalence grouping of methods based on a solid
statistical test. Our analysis also includes a strong variability of the
metrics using $500$x$2$ cross validation. While some methods shine for
different metrics and for different datasets, there is no single method that
dominates the pack. Furthermore, we observe that in some cases relevant
(included) citations can be found after screening only 15-20% of them via a
certainty based sampling. A few included citations present outlying
characteristics and can only be found after a very large number of screening
steps. Finally, we present an ensemble algorithm for producing a $5$-star
rating of citations based on their relevance. Such algorithm combines the best
methods from our evaluation and through its $5$-star rating outputs a more
easy-to-consume prediction.
| Tanay Kumar Saha, Mourad Ouzzani, Hossam M. Hammady, Ahmed K.
Elmagarmid, Wajdi Dhifli, and Mohammad Al Hasan | null | 1610.00192 | null | null |
Deep unsupervised learning through spatial contrasting | cs.LG cs.AI stat.ML | Convolutional networks have marked their place over the last few years as the
best performing model for various visual tasks. They are, however, most suited
for supervised learning from large amounts of labeled data. Previous attempts
have been made to use unlabeled data to improve model performance by applying
unsupervised techniques. These attempts require different architectures and
training methods. In this work we present a novel approach for unsupervised
training of Convolutional networks that is based on contrasting between spatial
regions within images. This criterion can be employed within conventional
neural networks and trained using standard techniques such as SGD and
back-propagation, thus complementing supervised methods.
| Elad Hoffer, Itay Hubara, Nir Ailon | null | 1610.00243 | null | null |
HNP3: A Hierarchical Nonparametric Point Process for Modeling Content
Diffusion over Social Media | stat.ML cs.LG cs.SI | This paper introduces a novel framework for modeling temporal events with
complex longitudinal dependency that are generated by dependent sources. This
framework takes advantage of multidimensional point processes for modeling time
of events. The intensity function of the proposed process is a mixture of
intensities, and its complexity grows with the complexity of temporal patterns
of data. Moreover, it utilizes a hierarchical dependent nonparametric approach
to model marks of events. These capabilities allow the proposed model to adapt
its temporal and topical complexity according to the complexity of data, which
makes it a suitable candidate for real world scenarios. An online inference
algorithm is also proposed that makes the framework applicable to a vast range
of applications. The framework is applied to a real world application, modeling
the diffusion of contents over networks. Extensive experiments reveal the
effectiveness of the proposed framework in comparison with state-of-the-art
methods.
| Seyed Abbas Hosseini, Ali Khodadadi, Soheil Arabzade and Hamid R.
Rabiee | null | 1610.00246 | null | null |
Sparsity-driven weighted ensemble classifier | stat.ML cs.LG | In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC)
that improves classification accuracy and minimizes the number of classifiers
is proposed. Using pre-trained classifiers, an ensemble in which base
classifiers votes according to assigned weights is formed. These assigned
weights directly affect classifier accuracy. In the proposed method, ensemble
weights finding problem is modeled as a cost function with the following terms:
(a) a data fidelity term aiming to decrease misclassification rate, (b) a
sparsity term aiming to decrease the number of classifiers, and (c) a
non-negativity constraint on the weights of the classifiers. As the proposed
cost function is non-convex thus hard to solve, convex relaxation techniques
and novel approximations are employed to obtain a numerically efficient
solution. Sparsity term of cost function allows trade-off between accuracy and
testing time when needed. The efficiency of SDWEC was tested on 11 datasets and
compared with the state-of-the art classifier ensemble methods. The results
show that SDWEC provides better or similar accuracy levels using fewer
classifiers and reduces testing time for ensemble.
| Atilla Ozgur, Hamit Erdem, Fatih Nar | 10.2991/ijcis.11.1.73 | 1610.0027 | null | null |
Accelerating Deep Convolutional Networks using low-precision and
sparsity | cs.LG cs.NE | We explore techniques to significantly improve the compute efficiency and
performance of Deep Convolution Networks without impacting their accuracy. To
improve the compute efficiency, we focus on achieving high accuracy with
extremely low-precision (2-bit) weight networks, and to accelerate the
execution time, we aggressively skip operations on zero-values. We achieve the
highest reported accuracy of 76.6% Top-1/93% Top-5 on the Imagenet object
classification challenge with low-precision network\footnote{github release of
the source code coming soon} while reducing the compute requirement by ~3x
compared to a full-precision network that achieves similar accuracy.
Furthermore, to fully exploit the benefits of our low-precision networks, we
build a deep learning accelerator core, dLAC, that can achieve up to 1
TFLOP/mm^2 equivalent for single-precision floating-point operations (~2
TFLOP/mm^2 for half-precision).
| Ganesh Venkatesh, Eriko Nurvitadhi, Debbie Marr | null | 1610.00324 | null | null |
Funneled Bayesian Optimization for Design, Tuning and Control of
Autonomous Systems | cs.AI cs.LG stat.ML | Bayesian optimization has become a fundamental global optimization algorithm
in many problems where sample efficiency is of paramount importance. Recently,
there has been proposed a large number of new applications in fields such as
robotics, machine learning, experimental design, simulation, etc. In this
paper, we focus on several problems that appear in robotics and autonomous
systems: algorithm tuning, automatic control and intelligent design. All those
problems can be mapped to global optimization problems. However, they become
hard optimization problems. Bayesian optimization internally uses a
probabilistic surrogate model (e.g.: Gaussian process) to learn from the
process and reduce the number of samples required. In order to generalize to
unknown functions in a black-box fashion, the common assumption is that the
underlying function can be modeled with a stationary process. Nonstationary
Gaussian process regression cannot generalize easily and it typically requires
prior knowledge of the function. Some works have designed techniques to
generalize Bayesian optimization to nonstationary functions in an indirect way,
but using techniques originally designed for regression, where the objective is
to improve the quality of the surrogate model everywhere. Instead optimization
should focus on improving the surrogate model near the optimum. In this paper,
we present a novel kernel function specially designed for Bayesian
optimization, that allows nonstationary behavior of the surrogate model in an
adaptive local region. In our experiments, we found that this new kernel
results in an improved local search (exploitation), without penalizing the
global search (exploration). We provide results in well-known benchmarks and
real applications. The new method outperforms the state of the art in Bayesian
optimization both in stationary and nonstationary problems.
| Ruben Martinez-Cantin | null | 1610.00366 | null | null |
Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep
Recurrent models | cs.CL cs.IR cs.LG cs.NE | Sentiment Analysis (SA) is an action research area in the digital age. With
rapid and constant growth of online social media sites and services, and the
increasing amount of textual data such as - statuses, comments, reviews etc.
available in them, application of automatic SA is on the rise. However, most of
the research works on SA in natural language processing (NLP) are based on
English language. Despite being the sixth most widely spoken language in the
world, Bangla still does not have a large and standard dataset. Because of
this, recent research works in Bangla have failed to produce results that can
be both comparable to works done by others and reusable as stepping stones for
future researchers to progress in this field. Therefore, we first tried to
provide a textual dataset - that includes not just Bangla, but Romanized Bangla
texts as well, is substantial, post-processed and multiple validated, ready to
be used in SA experiments. We tested this dataset in Deep Recurrent model,
specifically, Long Short Term Memory (LSTM), using two types of loss functions
- binary crossentropy and categorical crossentropy, and also did some
experimental pre-training by using data from one validation to pre-train the
other and vice versa. Lastly, we documented the results along with some
analysis on them, which were promising.
| A. Hassan, M. R. Amin, N. Mohammed, A. K. A. Azad | null | 1610.00369 | null | null |
Learning to Translate in Real-time with Neural Machine Translation | cs.CL cs.LG | Translating in real-time, a.k.a. simultaneous translation, outputs
translation words before the input sentence ends, which is a challenging
problem for conventional machine translation methods. We propose a neural
machine translation (NMT) framework for simultaneous translation in which an
agent learns to make decisions on when to translate from the interaction with a
pre-trained NMT environment. To trade off quality and delay, we extensively
explore various targets for delay and design a method for beam-search
applicable in the simultaneous MT setting. Experiments against state-of-the-art
baselines on two language pairs demonstrate the efficacy of the proposed
framework both quantitatively and qualitatively.
| Jiatao Gu, Graham Neubig, Kyunghyun Cho and Victor O.K. Li | null | 1610.00388 | null | null |
Can Evolutionary Sampling Improve Bagged Ensembles? | cs.LG cs.AI | Perturb and Combine (P&C) group of methods generate multiple versions of the
predictor by perturbing the training set or construction and then combining
them into a single predictor (Breiman, 1996b). The motive is to improve the
accuracy in unstable classification and regression methods. One of the most
well known method in this group is Bagging. Arcing or Adaptive Resampling and
Combining methods like AdaBoost are smarter variants of P&C methods. In this
extended abstract, we lay the groundwork for a new family of methods under the
P&C umbrella, known as Evolutionary Sampling (ES). We employ Evolutionary
algorithms to suggest smarter sampling in both the feature space (sub-spaces)
as well as training samples. We discuss multiple fitness functions to assess
ensembles and empirically compare our performance against randomized sampling
of training data and feature sub-spaces.
| Harsh Nisar, Bhanu Pratap Singh Rawat | null | 1610.00465 | null | null |
One-Trial Correction of Legacy AI Systems and Stochastic Separation
Theorems | stat.ML cs.LG | We consider the problem of efficient "on the fly" tuning of existing, or {\it
legacy}, Artificial Intelligence (AI) systems. The legacy AI systems are
allowed to be of arbitrary class, albeit the data they are using for computing
interim or final decision responses should posses an underlying structure of a
high-dimensional topological real vector space. The tuning method that we
propose enables dealing with errors without the need to re-train the system.
Instead of re-training a simple cascade of perceptron nodes is added to the
legacy system. The added cascade modulates the AI legacy system's decisions. If
applied repeatedly, the process results in a network of modulating rules
"dressing up" and improving performance of existing AI systems. Mathematical
rationale behind the method is based on the fundamental property of measure
concentration in high dimensional spaces. The method is illustrated with an
example of fine-tuning a deep convolutional network that has been pre-trained
to detect pedestrians in images.
| Alexander N. Gorban, Ilya Romanenko, Richard Burton, Ivan Y. Tyukin | 10.1016/j.ins.2019.02.001 | 1610.00494 | null | null |
Semi-supervised Learning with Sparse Autoencoders in Phone
Classification | stat.ML cs.CL cs.LG | We propose the application of a semi-supervised learning method to improve
the performance of acoustic modelling for automatic speech recognition based on
deep neural net- works. As opposed to unsupervised initialisation followed by
supervised fine tuning, our method takes advantage of both unlabelled and
labelled data simultaneously through mini- batch stochastic gradient descent.
We tested the method with varying proportions of labelled vs unlabelled
observations in frame-based phoneme classification on the TIMIT database. Our
experiments show that the method outperforms standard supervised training for
an equal amount of labelled data and provides competitive error rates compared
to state-of-the-art graph-based semi-supervised learning techniques.
| Akash Kumar Dhaka and Giampiero Salvi | null | 1610.0052 | null | null |
Video Pixel Networks | cs.CV cs.LG | We propose a probabilistic video model, the Video Pixel Network (VPN), that
estimates the discrete joint distribution of the raw pixel values in a video.
The model and the neural architecture reflect the time, space and color
structure of video tensors and encode it as a four-dimensional dependency
chain. The VPN approaches the best possible performance on the Moving MNIST
benchmark, a leap over the previous state of the art, and the generated videos
show only minor deviations from the ground truth. The VPN also produces
detailed samples on the action-conditional Robotic Pushing benchmark and
generalizes to the motion of novel objects.
| Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan, Ivo Danihelka,
Oriol Vinyals, Alex Graves, Koray Kavukcuoglu | null | 1610.00527 | null | null |
Path Integral Guided Policy Search | cs.RO cs.LG | We present a policy search method for learning complex feedback control
policies that map from high-dimensional sensory inputs to motor torques, for
manipulation tasks with discontinuous contact dynamics. We build on a prior
technique called guided policy search (GPS), which iteratively optimizes a set
of local policies for specific instances of a task, and uses these to train a
complex, high-dimensional global policy that generalizes across task instances.
We extend GPS in the following ways: (1) we propose the use of a model-free
local optimizer based on path integral stochastic optimal control (PI2), which
enables us to learn local policies for tasks with highly discontinuous contact
dynamics; and (2) we enable GPS to train on a new set of task instances in
every iteration by using on-policy sampling: this increases the diversity of
the instances that the policy is trained on, and is crucial for achieving good
generalization. We show that these contributions enable us to learn deep neural
network policies that can directly perform torque control from visual input. We
validate the method on a challenging door opening task and a pick-and-place
task, and we demonstrate that our approach substantially outperforms the prior
LQR-based local policy optimizer on these tasks. Furthermore, we show that
on-policy sampling significantly increases the generalization ability of these
policies.
| Yevgen Chebotar, Mrinal Kalakrishnan, Ali Yahya, Adrian Li, Stefan
Schaal, Sergey Levine | null | 1610.00529 | null | null |
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks | cs.CL cs.LG cs.SD | In this paper, a neural network based real-time speech recognition (SR)
system is developed using an FPGA for very low-power operation. The implemented
system employs two recurrent neural networks (RNNs); one is a
speech-to-character RNN for acoustic modeling (AM) and the other is for
character-level language modeling (LM). The system also employs a statistical
word-level LM to improve the recognition accuracy. The results of the AM, the
character-level LM, and the word-level LM are combined using a fairly simple
N-best search algorithm instead of the hidden Markov model (HMM) based network.
The RNNs are implemented using massively parallel processing elements (PEs) for
low latency and high throughput. The weights are quantized to 6 bits to store
all of them in the on-chip memory of an FPGA. The proposed algorithm is
implemented on a Xilinx XC7Z045, and the system can operate much faster than
real-time.
| Minjae Lee, Kyuyeon Hwang, Jinhwan Park, Sungwook Choi, Sungho Shin,
Wonyong Sung | null | 1610.00552 | null | null |
End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural
Networks | cs.LG cs.NI | We investigate sequence machine learning techniques on raw radio signal
time-series data. By applying deep recurrent neural networks we learn to
discriminate between several application layer traffic types on top of a
constant envelope modulation without using an expert demodulation algorithm. We
show that complex protocol sequences can be learned and used for both
classification and generation tasks using this approach.
| Timothy J. O'Shea, Seth Hitefield, Johnathan Corgan | null | 1610.00564 | null | null |
Fast Cosine Similarity Search in Binary Space with Angular Multi-index
Hashing | cs.DB cs.DS cs.IR cs.LG | Given a large dataset of binary codes and a binary query point, we address
how to efficiently find $K$ codes in the dataset that yield the largest cosine
similarities to the query. The straightforward answer to this problem is to
compare the query with all items in the dataset, but this is practical only for
small datasets. One potential solution to enhance the search time and achieve
sublinear cost is to use a hash table populated with binary codes of the
dataset and then look up the nearby buckets to the query to retrieve the
nearest neighbors. However, if codes are compared in terms of cosine similarity
rather than the Hamming distance, then the main issue is that the order of
buckets to probe is not evident. To examine this issue, we first elaborate on
the connection between the Hamming distance and the cosine similarity. Doing
this allows us to systematically find the probing sequence in the hash table.
However, solving the nearest neighbor search with a single table is only
practical for short binary codes. To address this issue, we propose the angular
multi-index hashing search algorithm which relies on building multiple hash
tables on binary code substrings. The proposed search algorithm solves the
exact angular $K$ nearest neighbor problem in a time that is often orders of
magnitude faster than the linear scan baseline and even approximation methods.
| Sepehr Eghbali and Ladan Tahvildari | null | 1610.00574 | null | null |
Quantifying Urban Traffic Anomalies | cs.LG | Detecting and quantifying anomalies in urban traffic is critical for
real-time alerting or re-routing in the short run and urban planning in the
long run. We describe a two-step framework that achieves these two goals in a
robust, fast, online, and unsupervised manner. First, we adapt stable principal
component pursuit to detect anomalies for each road segment. This allows us to
pinpoint traffic anomalies early and precisely in space. Then we group the
road-level anomalies across time and space into meaningful anomaly events using
a simple graph expansion procedure. These events can be easily clustered,
visualized, and analyzed by urban planners. We demonstrate the effectiveness of
our system using 7 weeks of anonymized and aggregated cellular location data in
Dallas-Fort Worth. We suggest potential opportunities for urban planners and
policy makers to use our methodology to make informed changes. These
applications include real-time re-routing of traffic in response to abnormally
high traffic, or identifying candidates for high-impact infrastructure
projects.
| Zhengyi Zhou (AT&T Labs Research), Philipp Meerkamp (Bloomberg LP),
Chris Volinsky (AT&T Labs Research) | null | 1610.00579 | null | null |
Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water
Testing | cs.LG stat.AP | Recovery from the Flint Water Crisis has been hindered by uncertainty in both
the water testing process and the causes of contamination. In this work, we
develop an ensemble of predictive models to assess the risk of lead
contamination in individual homes and neighborhoods. To train these models, we
utilize a wide range of data sources, including voluntary residential water
tests, historical records, and city infrastructure data. Additionally, we use
our models to identify the most prominent factors that contribute to a high
risk of lead contamination. In this analysis, we find that lead service lines
are not the only factor that is predictive of the risk of lead contamination of
water. These results could be used to guide the long-term recovery efforts in
Flint, minimize the immediate damages, and improve resource-allocation
decisions for similar water infrastructure crises.
| Jacob Abernethy (University of Michigan), Cyrus Anderson (University
of Michigan), Chengyu Dai (University of Michigan), Arya Farahi (University
of Michigan), Linh Nguyen (University of Michigan), Adam Rauh (University of
Michigan), Eric Schwartz (University of Michigan), Wenbo Shen (University of
Michigan), Guangsha Shi (University of Michigan), Jonathan Stroud (University
of Michigan), Xinyu Tan (University of Michigan), Jared Webb (University of
Michigan), Sheng Yang (University of Michigan) | null | 1610.0058 | null | null |
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous
Off-Policy Updates | cs.RO cs.AI cs.LG | Reinforcement learning holds the promise of enabling autonomous robots to
learn large repertoires of behavioral skills with minimal human intervention.
However, robotic applications of reinforcement learning often compromise the
autonomy of the learning process in favor of achieving training times that are
practical for real physical systems. This typically involves introducing
hand-engineered policy representations and human-supplied demonstrations. Deep
reinforcement learning alleviates this limitation by training general-purpose
neural network policies, but applications of direct deep reinforcement learning
algorithms have so far been restricted to simulated settings and relatively
simple tasks, due to their apparent high sample complexity. In this paper, we
demonstrate that a recent deep reinforcement learning algorithm based on
off-policy training of deep Q-functions can scale to complex 3D manipulation
tasks and can learn deep neural network policies efficiently enough to train on
real physical robots. We demonstrate that the training times can be further
reduced by parallelizing the algorithm across multiple robots which pool their
policy updates asynchronously. Our experimental evaluation shows that our
method can learn a variety of 3D manipulation skills in simulation and a
complex door opening skill on real robots without any prior demonstrations or
manually designed representations.
| Shixiang Gu and Ethan Holly and Timothy Lillicrap and Sergey Levine | null | 1610.00633 | null | null |
Kernel Selection using Multiple Kernel Learning and Domain Adaptation in
Reproducing Kernel Hilbert Space, for Face Recognition under Surveillance
Scenario | cs.CV cs.LG | Face Recognition (FR) has been the interest to several researchers over the
past few decades due to its passive nature of biometric authentication. Despite
high accuracy achieved by face recognition algorithms under controlled
conditions, achieving the same performance for face images obtained in
surveillance scenarios, is a major hurdle. Some attempts have been made to
super-resolve the low-resolution face images and improve the contrast, without
considerable degree of success. The proposed technique in this paper tries to
cope with the very low resolution and low contrast face images obtained from
surveillance cameras, for FR under surveillance conditions. For Support Vector
Machine classification, the selection of appropriate kernel has been a widely
discussed issue in the research community. In this paper, we propose a novel
kernel selection technique termed as MFKL (Multi-Feature Kernel Learning) to
obtain the best feature-kernel pairing. Our proposed technique employs a
effective kernel selection by Multiple Kernel Learning (MKL) method, to choose
the optimal kernel to be used along with unsupervised domain adaptation method
in the Reproducing Kernel Hilbert Space (RKHS), for a solution to the problem.
Rigorous experimentation has been performed on three real-world surveillance
face datasets : FR\_SURV, SCface and ChokePoint. Results have been shown using
Rank-1 Recognition Accuracy, ROC and CMC measures. Our proposed method
outperforms all other recent state-of-the-art techniques by a considerable
margin.
| Samik Banerjee, Sukhendu Das | null | 1610.0066 | null | null |
Collective Robot Reinforcement Learning with Distributed Asynchronous
Guided Policy Search | cs.LG cs.AI cs.RO | In principle, reinforcement learning and policy search methods can enable
robots to learn highly complex and general skills that may allow them to
function amid the complexity and diversity of the real world. However, training
a policy that generalizes well across a wide range of real-world conditions
requires far greater quantity and diversity of experience than is practical to
collect with a single robot. Fortunately, it is possible for multiple robots to
share their experience with one another, and thereby, learn a policy
collectively. In this work, we explore distributed and asynchronous policy
learning as a means to achieve generalization and improved training times on
challenging, real-world manipulation tasks. We propose a distributed and
asynchronous version of Guided Policy Search and use it to demonstrate
collective policy learning on a vision-based door opening task using four
robots. We show that it achieves better generalization, utilization, and
training times than the single robot alternative.
| Ali Yahya, Adrian Li, Mrinal Kalakrishnan, Yevgen Chebotar, Sergey
Levine | 10.1109/IROS.2017.8202141 | 1610.00673 | null | null |
Team-Optimal Distributed MMSE Estimation in General and Tree Networks | cs.SY cs.LG | We construct team-optimal estimation algorithms over distributed networks for
state estimation in the finite-horizon mean-square error (MSE) sense. Here, we
have a distributed collection of agents with processing and cooperation
capabilities. These agents observe noisy samples of a desired state through a
linear model and seek to learn this state by interacting with each other.
Although this problem has attracted significant attention and been studied
extensively in fields including machine learning and signal processing, all the
well-known strategies do not achieve team-optimal learning performance in the
finite-horizon MSE sense. To this end, we formulate the finite-horizon
distributed minimum MSE (MMSE) when there is no restriction on the size of the
disclosed information, i.e., oracle performance, over an arbitrary network
topology. Subsequently, we show that exchange of local estimates is sufficient
to achieve the oracle performance only over certain network topologies. By
inspecting these network structures, we propose recursive algorithms achieving
the oracle performance through the disclosure of local estimates. For practical
implementations we also provide approaches to reduce the complexity of the
algorithms through the time-windowing of the observations. Finally, in the
numerical examples, we demonstrate the superior performance of the introduced
algorithms in the finite-horizon MSE sense due to optimal estimation.
| Muhammed O. Sayin, Suleyman S. Kozat, and Tamer Ba\c{s}ar | null | 1610.00681 | null | null |
Deep Visual Foresight for Planning Robot Motion | cs.LG cs.AI cs.CV cs.RO | A key challenge in scaling up robot learning to many skills and environments
is removing the need for human supervision, so that robots can collect their
own data and improve their own performance without being limited by the cost of
requesting human feedback. Model-based reinforcement learning holds the promise
of enabling an agent to learn to predict the effects of its actions, which
could provide flexible predictive models for a wide range of tasks and
environments, without detailed human supervision. We develop a method for
combining deep action-conditioned video prediction models with model-predictive
control that uses entirely unlabeled training data. Our approach does not
require a calibrated camera, an instrumented training set-up, nor precise
sensing and actuation. Our results show that our method enables a real robot to
perform nonprehensile manipulation -- pushing objects -- and can handle novel
objects not seen during training.
| Chelsea Finn and Sergey Levine | null | 1610.00696 | null | null |
Sequential Low-Rank Change Detection | stat.ML cs.LG math.ST stat.TH | Detecting emergence of a low-rank signal from high-dimensional data is an
important problem arising from many applications such as camera surveillance
and swarm monitoring using sensors. We consider a procedure based on the
largest eigenvalue of the sample covariance matrix over a sliding window to
detect the change. To achieve dimensionality reduction, we present a
sketching-based approach for rank change detection using the low-dimensional
linear sketches of the original high-dimensional observations. The premise is
that when the sketching matrix is a random Gaussian matrix, and the dimension
of the sketching vector is sufficiently large, the rank of sample covariance
matrix for these sketches equals the rank of the original sample covariance
matrix with high probability. Hence, we may be able to detect the low-rank
change using sample covariance matrices of the sketches without having to
recover the original covariance matrix. We character the performance of the
largest eigenvalue statistic in terms of the false-alarm-rate and the expected
detection delay, and present an efficient online implementation via subspace
tracking.
| Yao Xie and Lee Seversky | null | 1610.00732 | null | null |
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library | cs.LG cs.CR stat.ML | CleverHans is a software library that provides standardized reference
implementations of adversarial example construction techniques and adversarial
training. The library may be used to develop more robust machine learning
models and to provide standardized benchmarks of models' performance in the
adversarial setting. Benchmarks constructed without a standardized
implementation of adversarial example construction are not comparable to each
other, because a good result may indicate a robust model or it may merely
indicate a weak implementation of the adversarial example construction
procedure.
This technical report is structured as follows. Section 1 provides an
overview of adversarial examples in machine learning and of the CleverHans
software. Section 2 presents the core functionalities of the library: namely
the attacks based on adversarial examples and defenses to improve the
robustness of machine learning models to these attacks. Section 3 describes how
to report benchmark results using the library. Section 4 describes the
versioning system.
| Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow,
Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, Aurko
Roy, Alexander Matyasko, Vahid Behzadan, Karen Hambardzumyan, Zhishuai Zhang,
Yi-Lin Juang, Zhi Li, Ryan Sheatsley, Abhibhav Garg, Jonathan Uesato, Willi
Gierke, Yinpeng Dong, David Berthelot, Paul Hendricks, Jonas Rauber, Rujun
Long, and Patrick McDaniel | null | 1610.00768 | null | null |
The Search Problem in Mixture Models | stat.ML cs.LG | We consider the task of learning the parameters of a {\em single} component
of a mixture model, for the case when we are given {\em side information} about
that component, we call this the "search problem" in mixture models. We would
like to solve this with computational and sample complexity lower than solving
the overall original problem, where one learns parameters of all components.
Our main contributions are the development of a simple but general model for
the notion of side information, and a corresponding simple matrix-based
algorithm for solving the search problem in this general setting. We then
specialize this model and algorithm to four common scenarios: Gaussian mixture
models, LDA topic models, subspace clustering, and mixed linear regression. For
each one of these we show that if (and only if) the side information is
informative, we obtain parameter estimates with greater accuracy, and also
improved computation complexity than existing moment based mixture model
algorithms (e.g. tensor methods). We also illustrate several natural ways one
can obtain such side information, for specific problem instances. Our
experiments on real data sets (NY Times, Yelp, BSDS500) further demonstrate the
practicality of our algorithms showing significant improvement in runtime and
accuracy.
| Avik Ray, Joe Neeman, Sujay Sanghavi, Sanjay Shakkottai | null | 1610.00843 | null | null |
Revisiting Role Discovery in Networks: From Node to Edge Roles | stat.ML cs.LG cs.SI | Previous work in network analysis has focused on modeling the
mixed-memberships of node roles in the graph, but not the roles of edges. We
introduce the edge role discovery problem and present a generalizable framework
for learning and extracting edge roles from arbitrary graphs automatically.
Furthermore, while existing node-centric role models have mainly focused on
simple degree and egonet features, this work also explores graphlet features
for role discovery. In addition, we also develop an approach for automatically
learning and extracting important and useful edge features from an arbitrary
graph. The experimental results demonstrate the utility of edge roles for
network analysis tasks on a variety of graphs from various problem domains.
| Nesreen K. Ahmed, Ryan A. Rossi, Theodore L. Willke, Rong Zhou | null | 1610.00844 | null | null |
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep
Learning from Demonstrations | cs.RO cs.LG | Motivated by recent advances in Deep Learning for robot control, this paper
considers two learning algorithms in terms of how they acquire demonstrations.
"Human-Centric" (HC) sampling is the standard supervised learning algorithm,
where a human supervisor demonstrates the task by teleoperating the robot to
provide trajectories consisting of state-control pairs. "Robot-Centric" (RC)
sampling is an increasingly popular alternative used in algorithms such as
DAgger, where a human supervisor observes the robot executing a learned policy
and provides corrective control labels for each state visited. RC sampling can
be challenging for human supervisors and prone to mislabeling. RC sampling can
also induce error in policy performance because it repeatedly visits areas of
the state space that are harder to learn. Although policies learned with RC
sampling can be superior to HC sampling for standard learning models such as
linear SVMs, policies learned with HC sampling may be comparable with
highly-expressive learning models such as deep learning and hyper-parametric
decision trees, which have little model error. We compare HC and RC using a
grid world and a physical robot singulation task, where in the latter the input
is a binary image of a connected set of objects on a planar worksurface and the
policy generates a motion of the gripper to separate one object from the rest.
We observe in simulation that for linear SVMs, policies learned with RC
outperformed those learned with HC but that with deep models this advantage
disappears. We also find that with RC, the corrective control labels provided
by humans can be highly inconsistent. We prove there exists a class of examples
where in the limit, HC is guaranteed to converge to an optimal policy while RC
may fail to converge.
| Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay
Krishnan, Kevin Jamieson, Anca Dragan, Ken Goldberg | null | 1610.0085 | null | null |
Ensemble Maximum Entropy Classification and Linear Regression for Author
Age Prediction | cs.LG cs.CL | The evolution of the internet has created an abundance of unstructured data
on the web, a significant part of which is textual. The task of author
profiling seeks to find the demographics of people solely from their linguistic
and content-based features in text. The ability to describe traits of authors
clearly has applications in fields such as security and forensics, as well as
marketing. Instead of seeing age as just a classification problem, we also
frame age as a regression one, but use an ensemble chain method that
incorporates the power of both classification and regression to learn the
authors exact age.
| Joey Hong, Chris Mattmann, Paul Ramirez | 10.1109/IRI.2017.48 | 1610.00852 | null | null |
Micro-Data Learning: The Other End of the Spectrum | cs.AI cs.LG cs.RO | Many fields are now snowed under with an avalanche of data, which raises
considerable challenges for computer scientists. Meanwhile, robotics (among
other fields) can often only use a few dozen data points because acquiring them
involves a process that is expensive or time-consuming. How can an algorithm
learn with only a few data points?
| Jean-Baptiste Mouret (LORIA, LARSEN) | null | 1610.00946 | null | null |
Embracing data abundance: BookTest Dataset for Reading Comprehension | cs.CL cs.AI cs.LG cs.NE | There is a practically unlimited amount of natural language data available.
Still, recent work in text comprehension has focused on datasets which are
small relative to current computing possibilities. This article is making a
case for the community to move to larger data and as a step in that direction
it is proposing the BookTest, a new dataset similar to the popular Children's
Book Test (CBT), however more than 60 times larger. We show that training on
the new data improves the accuracy of our Attention-Sum Reader model on the
original CBT test data by a much larger margin than many recent attempts to
improve the model architecture. On one version of the dataset our ensemble even
exceeds the human baseline provided by Facebook. We then show in our own human
study that there is still space for further improvement.
| Ondrej Bajgar, Rudolf Kadlec and Jan Kleindienst | null | 1610.00956 | null | null |
Stochastic Optimization with Variance Reduction for Infinite Datasets
with Finite-Sum Structure | stat.ML cs.LG math.OC | Stochastic optimization algorithms with variance reduction have proven
successful for minimizing large finite sums of functions. Unfortunately, these
techniques are unable to deal with stochastic perturbations of input data,
induced for example by data augmentation. In such cases, the objective is no
longer a finite sum, and the main candidate for optimization is the stochastic
gradient descent method (SGD). In this paper, we introduce a variance reduction
approach for these settings when the objective is composite and strongly
convex. The convergence rate outperforms SGD with a typically much smaller
constant factor, which depends on the variance of gradient estimates only due
to perturbations on a single example.
| Alberto Bietti, Julien Mairal | null | 1610.0097 | null | null |
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